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 # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # This file was automatically generated from src/transformers/models/gemma3n/modular_gemma3n.py.
 # Do NOT edit this file manually as any edits will be overwritten by the generation of
 # the file from the modular. If any change should be done, please apply the change to the
 # modular_gemma3n.py file directly. One of our CI enforces this.
 # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # coding=utf-8
 # Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
 #
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 # http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import copy
 import math
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
 from typing import Optional, Union
import torch
 import torch.nn as nn
 import torch.nn.functional as F
from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, HybridCache
 from ...generation import GenerationMixin
 from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import (
 ModelOutput,
 auto_docstring,
 can_return_tuple,
 is_torchdynamo_compiling,
 logging,
 )
 from ...utils.deprecation import deprecate_kwarg
 from ..auto import AutoModel
 from .configuration_gemma3n import Gemma3nAudioConfig, Gemma3nConfig, Gemma3nTextConfig, Gemma3nVisionConfig
logger = logging.get_logger(__name__)
@dataclass
 @auto_docstring(
 custom_intro="""
 Base class for Gemma3n outputs, with hidden states and attentions.
 """
 )
 class Gemma3nModelOutputWithPast(BaseModelOutputWithPast):
 r"""
 past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
 Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
 `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
 `past_key_values` input) to speed up sequential decoding.
 image_hidden_states (`torch.FloatTensor`, *optional*):
 A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
 image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
 audio_hidden_states (`torch.FloatTensor`, *optional*):
 A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
 audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
 """
image_hidden_states: Optional[torch.FloatTensor] = None
audio_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
 @auto_docstring(
 custom_intro="""
 Base class for Gemma3n causal language model (or autoregressive) outputs.
 """
 )
 class Gemma3nCausalLMOutputWithPast(ModelOutput):
 r"""
 loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
 Language modeling loss (for next-token prediction).
 logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`):
 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
 past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
 Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
 `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
 `past_key_values` input) to speed up sequential decoding.
 image_hidden_states (`torch.FloatTensor`, *optional*):
 A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
 image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
 audio_hidden_states (`torch.FloatTensor`, *optional*):
 A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
 audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
 """
loss: Optional[torch.FloatTensor] = None
 logits: Optional[torch.FloatTensor] = None
 past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None
 hidden_states: Optional[tuple[torch.FloatTensor]] = None
 attentions: Optional[tuple[torch.FloatTensor]] = None
 image_hidden_states: Optional[torch.FloatTensor] = None
audio_hidden_states: Optional[torch.FloatTensor] = None
class Gemma3nRMSNorm(nn.Module):
 def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True):
 super().__init__()
 self.eps = eps
 self.with_scale = with_scale
if self.with_scale:
 self.weight = nn.Parameter(torch.ones(dim))
 else:
 self.register_buffer("weight", torch.tensor(1.0), persistent=False)
def _norm(self, x):
 return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x: torch.Tensor) -> torch.Tensor:
 # Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16)
 # See https://github.com/huggingface/transformers/pull/29402
 output = self._norm(x.float()) * self.weight.float()
 return output.type_as(x)
def extra_repr(self):
 return f"{tuple(self.weight.shape)}, eps={self.eps}"
# ==== Audio Encoder ====
class Gemma3nAudioRelativePositionEmbedding(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
self.num_heads = self.config.conf_num_attention_heads
 self.channels = self.config.hidden_size
 self.head_dim = self.channels // self.num_heads
 self.max_backward = max(0, self.config.conf_attention_context_left - 1)
 self.max_forward = self.config.conf_attention_context_right
self.pos_proj = nn.Linear(self.channels, self.num_heads * self.head_dim, bias=False)
min_timescale = 1.0
 max_timescale = 1.0e4
 num_timescales = self.channels // 2
 log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(num_timescales - 1, 1)
 inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
 self.register_buffer(
 "inv_timescales",
 inv_timescales.float().unsqueeze(0).unsqueeze(0),
 persistent=False,
 )
def _get_timing_signal_1d_pos(self, position: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
 position = position.float().unsqueeze(-1)
 scaled_time = position * self.inv_timescales.to(device=position.device, dtype=torch.float32)
 timing_signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=-1)
 return timing_signal.type(dtype)
def _relative_shift(
 self,
 term_bd_before_shift: torch.Tensor,
 batch_size: int,
 num_heads: int,
 num_query_blocks: int,
 query_block_size: int,
 key_context_size: int,
 max_span_plus_1: int,
 ) -> torch.Tensor:
 """Performs the relative shift.
Args:
 term_bd_before_shift: Tensor of shape [B, N, U, W, F_span]. batch_size
 (B), num_heads (N), num_query_blocks (U), query_block_size (W),
 key_context_size (C = W+L+R), max_span_plus_1 (F_span = L+R+1).
Returns:
 Tensor of shape [B, N, U, W, C].
 """
 # term_bd_before_shift shape: [B, N, U, W, F_span]
 # Target shape after shift: [B, N, U, W, C]
# Padding amount for the last dimension (F_span) to become (C + 1)
 # C = key_context_size
 # F_span = max_span_plus_1
 pad_amount_last_dim = (key_context_size + 1) - max_span_plus_1
# PyTorch F.pad expects (pad_left, pad_right, pad_top, pad_bottom ...)
 # We only pad the last dimension on the right.
 padding_tuple = (0, pad_amount_last_dim)
term_bd_padded = nn.functional.pad(term_bd_before_shift, padding_tuple)
 # Shape after pad: [B, N, U, W, C+1]
# Reshape for slicing (emulating JAX's behavior)
 # [B, N, U, W * (C+1)]
 term_bd_reshaped = term_bd_padded.reshape(
 (
 batch_size,
 num_heads,
 num_query_blocks,
 query_block_size * (key_context_size + 1),
 )
 )
# Slice to effective [B, N, U, W * C]
 term_bd_sliced = term_bd_reshaped[:, :, :, : query_block_size * key_context_size]
# Reshape back to [B, N, U, W, C]
 term_bd_shifted = term_bd_sliced.reshape(
 (
 batch_size,
 num_heads,
 num_query_blocks,
 query_block_size,
 key_context_size,
 )
 )
 return term_bd_shifted
def forward(self, queries: torch.Tensor, keys: torch.Tensor) -> torch.Tensor:
 # queries: [B, U, W, N, H] (batch, num_query_blocks, query_block_size, num_heads, head_dim)
 # keys: [B, U, C, N, H] (batch, num_query_blocks, key_context_size, num_heads, head_dim)
 # C = W + L + R (key_context_size)
 # F_span = L + R + 1 (max_span + 1)
batch_size, num_query_blocks, query_block_size, num_heads, head_dim = queries.shape
 _, _, key_context_size, _, _ = keys.shape
# Relative positions for sinusoidal embeddings: [L, L-1, ..., -R]
 # Length is L+R+1 = self.max_span + 1
 pos_indices = torch.arange(self.max_backward, -self.max_forward - 1, -1, device=queries.device).unsqueeze(
 0
 ) # Shape [1, F_span]
max_span_plus_1 = pos_indices.shape[1] # F_span
sin_emb_timing_signal = self._get_timing_signal_1d_pos(
 pos_indices, dtype=queries.dtype
 ) # Shape [1, F_span, self.channels]
# Project sinusoidal embeddings: [1, F_span, self.channels] -> [1, F_span, N*H]
 projected_sin_emb = self.pos_proj(sin_emb_timing_signal)
 # Reshape to [1, F_span, N, H] then squeeze to [F_span, N, H]
 sin_emb = projected_sin_emb.reshape(1, max_span_plus_1, self.num_heads, self.head_dim).squeeze(
 0
 ) # Shape [F, N, H]
# term_ac: Query-Key content interaction
 # queries: [B, U, W, N, H] -> permute to [B, N, U, W, H] for matmul
 # keys: [B, U, C, N, H] -> permute to [B, N, U, H, C] for matmul
 queries_p = queries.permute(0, 3, 1, 2, 4) # [B, N, U, W, H]
 keys_p_t = keys.permute(0, 3, 1, 4, 2) # [B, N, U, H, C]
 term_ac = torch.matmul(queries_p, keys_p_t) # [B, N, U, W, C]
# term_bd: Query-Position interaction
 # Original einsum: term_bd_unshifed = torch.einsum('buwnh,fnh->bnuwf', queries, sin_emb)
 # queries shape: [B, U, W, N, H]
 # sin_emb shape: [F, N, H]
 # Target output shape: [B, N, U, W, F]
# Permute queries to [B, N, U, W, H] for easier broadcasting with sin_emb
 q_permuted = queries.permute(0, 3, 1, 2, 4)
# Permute sin_emb to [N, H, F] to prepare for matmul
 # sin_emb original is [F, N, H]
 s_permuted = sin_emb.permute(1, 2, 0) # Shape: [N, H, F]
# Reshape queries for matmul: [B, N, U*W, H]
 q_reshaped = q_permuted.reshape(batch_size, num_heads, num_query_blocks * query_block_size, head_dim)
# Perform matmul: [B, N, U*W, H] @ [N, H, F]
 # s_permuted ([N, H, F]) will be broadcast to [B, N, H, F]
 # Result: [B, N, U*W, F]
 term_bd_unshifed_matmul = torch.matmul(q_reshaped, s_permuted)
# Reshape to target [B, N, U, W, F]
 term_bd_unshifed = term_bd_unshifed_matmul.reshape(
 batch_size,
 num_heads,
 num_query_blocks,
 query_block_size,
 max_span_plus_1,
 )
# Apply relative shift to term_bd_unshifed
 term_bd_shifted = self._relative_shift(
 term_bd_unshifed,
 batch_size,
 num_heads,
 num_query_blocks,
 query_block_size,
 key_context_size,
 max_span_plus_1,
 ) # Shape [B, N, U, W, C]
return term_ac + term_bd_shifted
class Gemma3nAudioAttention(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
self.num_heads = self.config.conf_num_attention_heads
 self.hidden_size = self.config.hidden_size
 self.head_dim = self.hidden_size // self.num_heads
self.chunk_size = self.config.conf_attention_chunk_size
 self.max_future_horizon = self.config.conf_attention_context_right
 self.max_past_horizon = max(0, self.config.conf_attention_context_left - 1)
 self.attention_logits_soft_cap = self.config.conf_attention_logit_cap
 self.context_size = self.chunk_size + self.max_past_horizon + self.max_future_horizon
self.relative_position_embedding = Gemma3nAudioRelativePositionEmbedding(config)
 self.per_dim_scale = nn.Parameter(torch.zeros((self.head_dim,)))
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
 self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
 self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
q_scale = self.head_dim**-0.5
 r_softplus_0 = 1.0 / torch.nn.functional.softplus(torch.tensor(0.0))
 self.register_buffer("q_scale", (q_scale * r_softplus_0).clone().detach(), persistent=False)
lower_causal_mask = torch.tril(
 torch.ones((self.context_size, self.chunk_size), dtype=torch.bool),
 diagonal=0,
 ).T
 upper_causal_mask = torch.tril(
 torch.ones((self.chunk_size, self.context_size), dtype=torch.bool),
 diagonal=self.max_past_horizon + self.max_future_horizon,
 )
 local_causal_valid_mask = torch.ones((self.chunk_size, self.context_size), dtype=torch.bool)
 local_causal_valid_mask = local_causal_valid_mask * lower_causal_mask * upper_causal_mask
 self.register_buffer("local_causal_valid_mask", local_causal_valid_mask, persistent=False)
self.register_buffer(
 "softcap",
 torch.tensor(self.attention_logits_soft_cap).float(),
 persistent=False,
 )
def _pad_dim1(self, x: torch.Tensor, pad_left: int, pad_right: int) -> torch.Tensor:
 batch, _, *tail_shape = x.shape
 left = x.new_zeros((batch, pad_left, *tail_shape))
 right = x.new_zeros((batch, pad_right, *tail_shape))
 x = torch.cat([left, x, right], dim=1)
 return x
def _convert_to_block(self, hidden_states: torch.Tensor) -> torch.Tensor:
 """Turns a sequence to non overlapping blocks.
Args:
 hidden_states: a tensor of [batch, time, ...].
Returns:
 A tensor of [batch, num_blocks, block_size, ...], with necessary
 paddings,
 where output[:, i, ...] are x[:, i*block_size:(i+1)*block_size, ...].
 """
 shape = hidden_states.shape
 b, t = shape[:2]
 num_blocks = (t + self.chunk_size - 1) // self.chunk_size
if (padding_len := num_blocks * self.chunk_size - t) > 0:
 hidden_states = self._pad_dim1(hidden_states, 0, padding_len)
permute_dims = (b, num_blocks, self.chunk_size) + shape[2:]
 hidden_states = hidden_states.reshape(permute_dims).contiguous()
 return hidden_states
def _extract_block_context(self, hidden_states: torch.Tensor) -> torch.Tensor:
 """Extracts temporal context for every block.
Args:
 hidden_states: a tensor of [batch, time, ...].
Returns:
 A tensor of [batch, num_blocks, context_size, ...], with necessary
 paddings,
 where context_size = block_size + left_context + right_context,
 and output[:, i, ...] are x[:, start-left_context:end+right_context,
 ...],
 start = i * block_size, end = (i + 1) * block_size.
 """
 pad_left = self.max_past_horizon
 # The JAX equivalent padding for signal.frame with pad_mode='valid' is
 # (left_context, right_context + block_size - 1) on the time dimension.
 # PyTorch's _pad_dim1 applies padding symmetrically if only one value is given,
 # or (pad_dim_start, pad_dim_end) if two are given.
 # Our _pad_dim1(x, pad_left, pad_right) pads dim -2 (time for [B,T,N,H])
 # or dim 1 (time for [B,T]).
 # The current pad_right calculation matches the JAX effective padding.
 pad_right = self.max_future_horizon + self.chunk_size - 1
 hidden_states = self._pad_dim1(hidden_states, pad_left, pad_right)
frame_len = self.context_size
 frame_step = self.chunk_size
# Directly use unfold without the subframe_factor logic
 # x.unfold(dimension, size, step)
 # dimension=1 (time dimension, assuming x is [B, T_padded, ...])
 # size=frame_len (context_size)
 # step=frame_step (chunk_size)
 x_unfolded = hidden_states.unfold(dimension=1, size=frame_len, step=frame_step)
# If x was [B, T_padded], x_unfolded is [B, num_blocks, frame_len]
 # If x was [B, T_padded, N, H], x_unfolded is [B, num_blocks, N, H, frame_len]
 # We want to match JAX's typical output for such operations which might be
 # [B, num_blocks, frame_len, N, H] if N, H are present.
 # The relative_position_embedding expects keys as [B, U, C, N, H].
 # If x_unfolded is [B, U, N, H, C(frame_len)], we need to move C.
 if hidden_states.ndim > 2 and x_unfolded.ndim > 3: # Check if inner dimensions (like N, H) exist
 # Current shape after unfold for [B, T_pad, N, H] is [B, U, N, H, C]
 # Target shape for keys in RPE: [B, U, C, N, H]
 x_unfolded = torch.movedim(x_unfolded, source=-1, destination=2)
return x_unfolded.contiguous()
def forward(self, hidden_states: torch.Tensor, mask: torch.BoolTensor) -> torch.Tensor:
 # sl.Dense uses jax.numpy.einsum("...a,abcd->...bcd") and jax.numpy.select()
 qkv_shape = (*hidden_states.shape[:-1], self.num_heads, self.head_dim)
 query_states = self.q_proj(hidden_states).reshape(qkv_shape).contiguous()
 key_states = self.k_proj(hidden_states).reshape(qkv_shape).contiguous()
 value_states = self.v_proj(hidden_states).reshape(qkv_shape).contiguous()
per_dim_scale_sp = torch.nn.functional.softplus(self.per_dim_scale)
broadcast_shape = (1, 1, 1, self.head_dim)
 per_dim_scale_sp_broadcast = per_dim_scale_sp.view(broadcast_shape)
 query_states = query_states * self.q_scale * per_dim_scale_sp_broadcast
batch_size, q_time = query_states.shape[:2]
query_blocks = self._convert_to_block(query_states)
 key_blocks = self._extract_block_context(key_states)
 value_blocks = self._extract_block_context(value_states)
 num_query_blocks = query_blocks.shape[1]
# 1. Create a mask indicating originally valid positions.
 original_valid_mask = ~mask # True for valid, False for padded
# 2. Extract blocks from this validity mask.
 extracted_valid_mask_blocks = self._extract_block_context(original_valid_mask)
# If subframe_factor was used in _extract_block_context for a [B, T] input mask,
 # the shape might be [B, U, C/SF, SF]. Reshape to [B, U, C].
 # batch_size and num_query_blocks are known from query_blocks.
 # self.context_size is C.
 if (
 extracted_valid_mask_blocks.ndim == 4
 and extracted_valid_mask_blocks.shape[2] * extracted_valid_mask_blocks.shape[3] == self.context_size
 ):
 extracted_valid_mask_blocks = extracted_valid_mask_blocks.reshape(
 batch_size, num_query_blocks, self.context_size
 )
 # After potential reshape, ensure it's [B, U, C] if it was from a [B,T] mask.
 # This assertion might be too strict if _extract_block_context handles higher-rank inputs differently,
 # but for the mask case, this should hold.
 if extracted_valid_mask_blocks.shape != (
 batch_size,
 num_query_blocks,
 self.context_size,
 ):
 raise ValueError(
 "Shape of extracted_valid_mask_blocks"
 f" {extracted_valid_mask_blocks.shape} is not ({batch_size},"
 f" {num_query_blocks}, {self.context_size}) after potential reshape."
 )
# 3. Expand dimensions for broadcasting with logits and causal mask.
 # Target shape for broadcasting with logits [B,N,U,W,C]
 # extracted_valid_mask_blocks to [B, 1, U, 1, C]
 condition_from_input_validity = extracted_valid_mask_blocks.unsqueeze(1).unsqueeze(-2)
# self.local_causal_valid_mask is [W, C], True where allowed by local window.
 # Expand to [1, 1, 1, W, C]
 condition_from_causality = self.local_causal_valid_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0)
# 4. Combine the two conditions.
 # final_condition will be True where a key is *both* originally valid *and* causally accessible.
 # Broadcasts to [B, 1, U, W, C]
 final_condition_for_where = torch.logical_and(
 condition_from_input_validity,
 condition_from_causality.to(condition_from_input_validity.device), # Ensure same device
 )
# Embed queries and keys
 logits = self.relative_position_embedding(query_blocks, key_blocks)
# Apply attention logit softcap
 # Ensure softcap is on the same device as logits
 softcap_val = self.softcap.to(logits.device)
 logits = logits / softcap_val
 logits = torch.tanh(logits)
 logits = logits * softcap_val
# Apply the combined mask.
 # final_condition_for_where will broadcast with logits [B,N,U,W,C]
 logits = torch.where(final_condition_for_where, logits, torch.finfo(logits.dtype).min)
 probabilities = torch.nn.functional.softmax(logits, dim=-1, dtype=torch.float32).to(dtype=value_blocks.dtype)
# context_vectors is adapted from jax.numpy.einsum("BNuwc,BucNH->BuwNH", ...)
 b_dim, n_dim, u_dim, w_dim, c_dim = probabilities.shape
 h_dim = value_blocks.shape[-1]
 prob_bun = probabilities.permute(0, 2, 1, 3, 4).reshape(-1, w_dim, c_dim)
 v_bun = value_blocks.permute(0, 1, 3, 2, 4).reshape(-1, c_dim, h_dim)
 result_bmm = torch.bmm(prob_bun, v_bun)
 context_vectors = result_bmm.reshape(b_dim, u_dim, n_dim, w_dim, h_dim).permute(0, 1, 3, 2, 4)
 context_vectors = context_vectors.reshape(
 (
 batch_size,
 num_query_blocks * self.chunk_size,
 self.num_heads,
 self.head_dim,
 )
 )
 context_vectors = context_vectors[:, :q_time]
return context_vectors
class Gemma3nAudioCumulativeGroupNorm(nn.Module):
 """Applies Group Normalization cumulatively over the time dimension.
This layer normalizes the input by calculating the mean and variance
 cumulatively over the time dimension (dim 1). The statistics are computed
 over all feature dimensions (specified by `feature_dims` and `num_channels`)
 for elements marked as valid by the optional `mask`.
If a `mask` is provided (True for valid, False for invalid/padded),
 invalid time steps do not contribute to the statistics calculation, and
 their corresponding output values are zeroed out.
Scale and bias, if enabled, are applied per-channel (last dimension).
 This behavior is similar to JAX's `GroupNormalization` with `num_groups=1`
 and `cumulative=True`.
 """
def __init__(
 self,
 num_channels: int, # Number of channels (size of the last dimension)
 feature_dims: Sequence[int], # Sizes of non-channel feature dimensions, e.g., (H, W) for input [B,T,H,W,C]
 eps: float = 1e-3,
 ):
 super().__init__()
 self.num_channels = num_channels
 self.feature_dims = tuple(feature_dims)
 self.eps = eps
# Scale parameter depends only on the channel dimension
 self.weight = nn.Parameter(torch.ones(num_channels))
# Axes for normalization: all dimensions except Batch (0) and Time (1).
 # For input [B, T, *feature_dims, C], these are dims from 2 onwards.
 self.reduction_axes = tuple(range(2, 2 + len(self.feature_dims) + 1))
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 """Applies cumulative group norm, optionally using a mask.
Args:
 hidden_states: Input tensor, shape [B, T, *feature_dims, C].
Returns:
 Normalized tensor with the same shape as x.
 """
 expected_input_suffix = self.feature_dims + (self.num_channels,)
 if hidden_states.shape[2:] != expected_input_suffix:
 raise ValueError(
 f"Input tensor shape suffix {hidden_states.shape[2:]} does not match expected"
 f" suffix (feature_dims + num_channels) {expected_input_suffix}"
 )
input_dtype = hidden_states.dtype
 # Calculations are performed in float32 for numerical stability.
 calc_dtype = torch.float32
 x_calc = hidden_states.to(calc_dtype)
# Prepare a broadcastable mask (`mask_calc`).
 # If no mask is provided, treat all elements as valid
 # (mask_calc is all ones).
 # Otherwise, expand the [B, T] mask to [B, T, 1, ..., 1] for broadcasting.
 mask_calc = torch.ones_like(x_calc, dtype=calc_dtype)
# Cumulative Statistics Calculation
 # 1. Sum of values over reduction axes at each time step.
 sum_values_at_t = torch.sum(x_calc, dim=self.reduction_axes, keepdim=True)
 # 2. Cumulative sum of values over time.
 cum_sum_values = torch.cumsum(sum_values_at_t, dim=1)
# 3. Count of valid elements in the normalization group at each time step.
 # (A "group" here consists of all features at a given Batch, Time).
 elements_in_group_at_t = torch.sum(mask_calc, dim=self.reduction_axes, keepdim=True)
 # 4. Cumulative count of valid elements over time.
 cum_count_elements = torch.cumsum(elements_in_group_at_t, dim=1)
 # Avoid division by zero if all preceding elements were masked.
 safe_cum_count_elements = torch.clamp(cum_count_elements, min=1.0)
# 5. Cumulative mean.
 cum_mean = cum_sum_values / safe_cum_count_elements
# 6. Sum of squared differences from the cumulative mean.
 # Only sum for valid elements: (x_calc - cum_mean)^2 * mask_calc.
 # Using x_calc here for the difference, as cum_mean already accounts for masking.
 squared_diff_from_mean = (x_calc - cum_mean).pow(2)
 sum_sq_diff_at_t = torch.sum(squared_diff_from_mean, dim=self.reduction_axes, keepdim=True)
# 7. Cumulative sum of squared differences over time.
 cum_sum_sq_diff = torch.cumsum(sum_sq_diff_at_t, dim=1)
# 8. Cumulative variance.
 cum_variance = cum_sum_sq_diff / safe_cum_count_elements
# Normalize the input using the calculated cumulative statistics:
 # (x - E[x]) / sqrt(Var[x] + eps)
 normalized_x = (x_calc - cum_mean) * torch.rsqrt(cum_variance + self.eps)
# Apply affine transformation (scale and bias) if enabled.
 # Scale and bias are applied per-channel (last dimension).
 scale = self.weight.to(calc_dtype)
 # Reshape for broadcasting: [C] -> [1, ..., 1, C]
 scale_view_shape = [1] * (hidden_states.dim() - 1) + [self.num_channels]
 normalized_x = normalized_x * scale.view(scale_view_shape)
# Zero out outputs for time steps that were originally masked (where mask_calc is 0).
 # This ensures padded/invalid positions in the input result in zero output.
 final_output = normalized_x * mask_calc
return final_output.to(input_dtype)
class Gemma3nAudioSSCPConvBlock(nn.Module):
 """A single convolution block for the SubSampleConvProjection.
This block consists of a 2D convolution, followed by CumulativeGroupNorm,
 and a ReLU activation. It handles manual padding for the convolution.
 """
def __init__(
 self,
 config: Gemma3nAudioConfig,
 idx: int,
 input_freq_dim: int, # Changed from input_spatial_dim
 manual_padding: tuple[int, int, int, int] = (0, 0, 0, 0),
 ):
 super().__init__()
 self.config = config
 self.manual_padding = manual_padding
# in_channels is 1 for the first block, or C_out from previous block's conv
 in_channels = 1 if idx == 0 else self.config.sscp_conv_channel_size[idx - 1]
 out_channels = self.config.sscp_conv_channel_size[idx]
 kernel_h, kernel_w = self.config.sscp_conv_kernel_size[idx]
 stride_h, stride_w = self.config.sscp_conv_stride_size[idx]
self.conv = nn.Conv2d(
 in_channels=in_channels,
 out_channels=out_channels,
 kernel_size=(
 kernel_h,
 kernel_w,
 ), # Kernel (kH, kW) operates on (Time, Freq_dim)
 stride=(stride_h, stride_w),
 padding=(0, 0), # Manual padding is used
 bias=False,
 )
# Calculate output frequency dimension (f_out_conv) after this convolution.
 # input_freq_dim is the unpadded width (feature dimension).
 # self.manual_padding is (pad_F_left, pad_F_right, pad_T_top, pad_T_bottom)
 f_in_padded = input_freq_dim + self.manual_padding[0] + self.manual_padding[1]
 f_out_conv = (f_in_padded - kernel_w) // stride_w + 1
self.norm = Gemma3nAudioCumulativeGroupNorm(
 num_channels=out_channels, # Channels of the conv output
 feature_dims=(f_out_conv,), # The frequency dimension size after conv
 eps=self.config.sscp_conv_group_norm_eps,
 )
self.activation = nn.ReLU()
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
 # Input audio_encodings is [B, C_in, T_in, F_in] (e.g., C_in=1)
 # manual_padding is (pad_F_left, pad_F_right, pad_T_top, pad_T_bottom)
 # F.pad applies to last two dims: F_in then T_in
 audio_encodings_padded = F.pad(audio_encodings, self.manual_padding, mode="constant", value=0.0)
 # Expected padded shape for F_in, k_w=3, pad_F=(1,1) -> F_padded = F_in+2
 # Expected padded shape for T_in, k_h=3, pad_T=(0,2) -> T_padded = T_in+2
 audio_encodings_conv = self.conv(audio_encodings_padded)
 # Expected conv output shape: [B, C_out, T_out, F_out]
 # Input to norm is [B, T_out, F_out, C_out]
 x_for_norm = audio_encodings_conv.permute(0, 2, 3, 1).contiguous()
 x_normed = self.norm(x_for_norm)
 # Output of norm is [B, T_out, F_out, C_out], permute back to [B, C_out, T_out, F_out]
 audio_encodings_normed = x_normed.permute(0, 3, 1, 2).contiguous()
 return self.activation(audio_encodings_normed)
class Gemma3nAudioSubSampleConvProjection(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
current_f_for_block_input = config.input_feat_size # Start with original feature dim
 calculated_block_padding = []
 calculated_f_out_dims = [] # Tracking frequency dimension output sizes
for i in range(2): # Assuming 2 conv layers as per sscp_conv_... arrays
 kernel_h, kernel_w = config.sscp_conv_kernel_size[i]
 stride_h, stride_w = config.sscp_conv_stride_size[i]
# Padding for Time (Height for Conv2d) - REVERSE_CAUSAL like
 # JAX 'reverse_causal' padding is (0, kernel_size - 1)
 pad_t_top = 0
 pad_t_bottom = kernel_h - 1
# Frequency Padding (Width for Conv2d)
 # Based on JAX effective padding (1,1) for F_in=10, K_w=3, S_w=2
 # and the successful test configuration.
 # If kernel/stride/input_freq for frequency changes, this might need re-evaluation
 # to match generic JAX 'SAME' behavior if it differs.
 pad_f_left = 1
 pad_f_right = 1
manual_padding_tuple = (
 pad_f_left,
 pad_f_right,
 pad_t_top,
 pad_t_bottom,
 )
 calculated_block_padding.append(manual_padding_tuple)
# Calculate output frequency dimension after this convolution
 # This uses the actual padding applied and kernel/stride.
 f_in_padded = current_f_for_block_input + pad_f_left + pad_f_right
 f_out_after_conv = (f_in_padded - kernel_w) // stride_w + 1 # Assuming dilation_w = 1
 calculated_f_out_dims.append(f_out_after_conv)
 current_f_for_block_input = f_out_after_conv
self.conv_0 = Gemma3nAudioSSCPConvBlock(
 idx=0,
 input_freq_dim=config.input_feat_size, # Pass original feature dim
 config=config,
 manual_padding=calculated_block_padding[0],
 )
 self.conv_1 = Gemma3nAudioSSCPConvBlock(
 idx=1,
 input_freq_dim=calculated_f_out_dims[0], # Output freq dim from conv_0
 config=config,
 manual_padding=calculated_block_padding[1],
 )
 final_c_out = config.sscp_conv_channel_size[-1]
 final_f_out = calculated_f_out_dims[-1] # Final frequency dimension
 self.input_proj_in_features = final_c_out * final_f_out
 self.input_proj_linear = nn.Linear(self.input_proj_in_features, self.config.hidden_size, bias=False)
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
 # audio_encodings is [B, T, F_in]
 # Reshape to [B, 1, T, F_in] (Batch, Channels=1, Height=Time, Width=F_in)
 audio_encodings_reshaped = audio_encodings.unsqueeze(1)
 x = self.conv_0(audio_encodings_reshaped)
 x = self.conv_1(x)
 # x from conv_1 is [B, C_out_1, T_out_1, F_out_1]
 b, c_out, t_out, f_out = x.shape
 # Permute to [B, T_out_1, F_out_1, C_out_1] then flatten F_out_1 and C_out_1
 x_permuted = x.permute(0, 2, 3, 1).contiguous()
 output_flattened = x_permuted.view(b, t_out, f_out * c_out)
 output = self.input_proj_linear(output_flattened)
 return output
class Gemma3nAudioConformerAttention(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
 self.post_in_features = self.config.hidden_size
 self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
 self.pre_attn_norm = Gemma3nRMSNorm(self.config.hidden_size)
 self.attn = Gemma3nAudioAttention(config)
 self.post = nn.Linear(self.post_in_features, self.config.hidden_size, bias=False)
 self.post_norm = Gemma3nRMSNorm(self.config.hidden_size)
def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
 audio_encodings_input_to_attn = audio_encodings
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 audio_encodings_norm = self.pre_attn_norm(audio_encodings)
 # Output of self.attn is [B, T, NumHeads, HeadDim]
 audio_encodings_attn_out = self.attn(audio_encodings_norm, audio_mel_mask)
# Reshape from [B, T, NumHeads, HeadDim] to [B, T, NumHeads * HeadDim]
 # NumHeads * HeadDim = hidden_size
 b, t, num_heads, head_dim = audio_encodings_attn_out.shape
 audio_encodings_reshaped = audio_encodings_attn_out.reshape(b, t, num_heads * head_dim)
audio_encodings = self.post(audio_encodings_reshaped)
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 return audio_encodings_input_to_attn + self.post_norm(audio_encodings)
class Gemma3nAudioConformerFeedForward(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
 self.ffw_layer_1 = nn.Linear(self.config.hidden_size, self.config.hidden_size * 4, bias=False)
 self.ffw_layer_2 = nn.Linear(self.config.hidden_size * 4, self.config.hidden_size, bias=False)
 self.post_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
 self.post_layer_scale = torch.tensor(self.config.conf_residual_weight)
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
 residual = audio_encodings
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 audio_encodings = self.pre_layer_norm(audio_encodings)
 audio_encodings: torch.Tensor = self.ffw_layer_1(audio_encodings)
 audio_encodings = nn.functional.silu(audio_encodings)
 audio_encodings: torch.Tensor = self.ffw_layer_2(audio_encodings)
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 audio_encodings = self.post_layer_norm(audio_encodings)
 return residual + (audio_encodings * self.post_layer_scale)
class Gemma3nAudioConformerLightConv1d(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
 self.linear_start = nn.Linear(self.config.hidden_size, self.config.hidden_size * 2, bias=False)
 self.depthwise_conv1d = nn.Conv1d(
 in_channels=self.config.hidden_size,
 out_channels=self.config.hidden_size,
 kernel_size=self.config.conf_conv_kernel_size,
 stride=1,
 padding=0, # Manual causal padding
 groups=self.config.hidden_size, # Depthwise
 bias=False,
 )
 self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
 self.conv_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
 self.linear_end = nn.Linear(self.config.hidden_size, self.config.hidden_size, bias=False)
self.causal_padding = self.config.conf_conv_kernel_size - 1
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
 audio_encodings_residual = audio_encodings # Save for residual connection
audio_encodings = self.pre_layer_norm(audio_encodings)
 audio_encodings = self.linear_start(audio_encodings)
 audio_encodings = torch.nn.functional.glu(audio_encodings, dim=-1)
 # Permute for Conv1d: [B, T, D] -> [B, D, T]
 audio_encodings_permuted = audio_encodings.permute(0, 2, 1)
 # Apply manual causal padding
 audio_encodings_permuted_padded = F.pad(audio_encodings_permuted, (self.causal_padding, 0))
 audio_encodings = self.depthwise_conv1d(audio_encodings_permuted_padded)
 # Permute back: [B, D, T_out] -> [B, T_out, D]
 audio_encodings = audio_encodings.permute(0, 2, 1)
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 audio_encodings = self.conv_norm(audio_encodings)
 audio_encodings = nn.functional.silu(audio_encodings)
 audio_encodings = self.linear_end(audio_encodings)
 output = audio_encodings + audio_encodings_residual
 return output
class Gemma3nAudioConformerBlock(nn.Module):
 def __init__(self, config: Gemma3nAudioConfig):
 super().__init__()
 self.config = config
self.ffw_layer_start = Gemma3nAudioConformerFeedForward(self.config)
 self.attention = Gemma3nAudioConformerAttention(self.config)
 self.lconv1d = Gemma3nAudioConformerLightConv1d(self.config)
 self.ffw_layer_end = Gemma3nAudioConformerFeedForward(self.config)
 self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
 self.norm = Gemma3nRMSNorm(self.config.hidden_size)
def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
 audio_encodings = self.ffw_layer_start(audio_encodings)
 audio_encodings = self.attention(audio_encodings, audio_mel_mask)
 validity_mask_for_lconv = ~audio_mel_mask # True for valid
 audio_encodings_for_lconv_input = audio_encodings * validity_mask_for_lconv.unsqueeze(-1).to(
 audio_encodings.dtype
 )
 audio_encodings = self.lconv1d(audio_encodings_for_lconv_input)
audio_encodings = self.ffw_layer_end(audio_encodings)
 audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
 output = self.norm(audio_encodings)
 return output
class Gemma3nAudioEncoder(PreTrainedModel):
 """An audio encoder based on the [Universal Speech Model](https://arxiv.org/abs/2303.01037) architecture."""
config_class = Gemma3nAudioConfig
main_input_name = "audio_mel"
def __init__(self, config: Gemma3nAudioConfig):
 super().__init__(config)
 self.config = config
self.subsample_conv_projection = Gemma3nAudioSubSampleConvProjection(config)
 self.conformer = nn.ModuleList(
 [Gemma3nAudioConformerBlock(config) for _ in range(config.conf_num_hidden_layers)]
 )
def forward(
 self, audio_mel: torch.Tensor, audio_mel_mask: torch.BoolTensor
 ) -> tuple[torch.Tensor, torch.BoolTensor]:
 """Encodes a batch of MELs.
Args:
 audio_mel: a torch.Tensor of shape [batch, num_frames, num_channels,
 mel_bins].
Returns:
 audio_encodings: a torch.Tensor of shape
 `[batch_size, self.config.audio_soft_tokens_per_image,
 self.config.audio_config.hidden_size]`
 audio_mel_mask: a torch.BoolTensor of shape [batch, num_frames].
 """
 audio_encodings = self.subsample_conv_projection(audio_mel) # audio_encodings: [B, T_sub, D]
# Subsample the input audio_mel_mask to match the time dimension of audio_encodings (T_sub)
 t_sub = audio_encodings.shape[1]
time_stride_product = 1
 for stride_pair_idx in range(len(self.config.sscp_conv_stride_size)):
 time_stride_product *= self.config.sscp_conv_stride_size[stride_pair_idx][0]
# Create indices for gathering from the original mask.
 # These indices map to original time steps corresponding to the start of each
 # receptive field in the subsampled output.
 indices = torch.arange(t_sub, device=audio_mel_mask.device) * time_stride_product
 indices = torch.clamp(indices, max=audio_mel_mask.shape[1] - 1) # Ensure indices are valid
# Expand indices for batch compatibility if B > 1 and indices is 1D.
 if audio_mel_mask.ndim > 1 and indices.ndim == 1:
 indices = indices.unsqueeze(0).expand(audio_mel_mask.shape[0], -1) # [B, T_sub]
 elif (
 audio_mel_mask.ndim == indices.ndim
 and audio_mel_mask.shape[0] == 1
 and indices.shape[0] != 1
 and t_sub == indices.shape[0]
 ):
 # Handle case where B=1 but indices became [T_sub] instead of [1, T_sub]
 indices = indices.unsqueeze(0)
current_mask = torch.gather(audio_mel_mask, 1, indices) # [B, T_sub]
for block in self.conformer:
 audio_encodings = block(audio_encodings, current_mask) # Pass the processed mask
if self.config.conf_reduction_factor > 1:
 audio_encodings = audio_encodings[:, :: self.config.conf_reduction_factor]
 # Reduce the mask as well
 current_mask = current_mask[:, :: self.config.conf_reduction_factor]
audio_encodings = audio_encodings.masked_fill(current_mask.unsqueeze(-1), 0.0)
 return audio_encodings, current_mask
class Gemma3nTextScaledWordEmbedding(nn.Embedding):
 """
 This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
 """
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
 super().__init__(num_embeddings, embedding_dim, padding_idx)
 self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
def forward(self, input_ids: torch.Tensor):
 return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
class Gemma3nTextLaurelBlock(nn.Module):
 """Learned Augmented Residual Layer"""
def __init__(self, config: Gemma3nTextConfig):
 super().__init__()
 self.config = config
self.linear_left = nn.Linear(self.config.hidden_size, self.config.laurel_rank, bias=False)
 self.linear_right = nn.Linear(self.config.laurel_rank, self.config.hidden_size, bias=False)
 self.post_laurel_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 laurel_hidden_states: torch.Tensor = self.linear_left(hidden_states)
 laurel_hidden_states: torch.Tensor = self.linear_right(laurel_hidden_states)
 normed_laurel_hidden_states = self.post_laurel_norm(laurel_hidden_states)
 return hidden_states + normed_laurel_hidden_states
class Gemma3nTextMLP(nn.Module):
 def __init__(self, config: Gemma3nTextConfig, layer_idx: int = 0):
 super().__init__()
 self.config = config
 self.hidden_size = config.hidden_size
 self.intermediate_size = config.intermediate_size[layer_idx]
 self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
 self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
 self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
 self.act_fn = ACT2FN[config.hidden_activation]
 self.activation_sparsity = config.activation_sparsity_pattern[layer_idx]
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 gate_proj = self.gate_proj(hidden_states)
 if self.activation_sparsity > 0.0:
 gate_proj = self._gaussian_topk(gate_proj)
 activations = self.act_fn(gate_proj)
 up_proj = self.up_proj(hidden_states)
 down_proj = self.down_proj(activations * up_proj)
 return down_proj
def _gaussian_topk(self, inputs: torch.Tensor) -> torch.Tensor:
 target_sparsity_tensor = torch.tensor(self.activation_sparsity, dtype=torch.float32, device=inputs.device)
 # normal_dist and std_multiplier are adapted from jax.scipy.stats.norm.ppf().
 #
 # References:
 # * https://docs.jax.dev/en/latest/_autosummary/jax.scipy.stats.norm.ppf.html
 # * https://pytorch.org/docs/stable/distributions.html#torch.distributions.normal.Normal
 # * https://pytorch.org/docs/stable/distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.icdf
 normal_dist = torch.distributions.normal.Normal(0, 1)
 std_multiplier: torch.Tensor = normal_dist.icdf(target_sparsity_tensor)
 std_multiplier = std_multiplier.type(inputs.dtype)
 inputs_mean = torch.mean(inputs, dim=-1, keepdim=True)
 inputs_std = torch.std(inputs, dim=-1, keepdim=True, unbiased=False)
 cutoff_x = inputs_mean + inputs_std * std_multiplier
 return nn.functional.relu(inputs - cutoff_x)
class Gemma3nTextAltUp(nn.Module):
 """Alternating Updates (AltUp)
The AltUp module wraps transformer layers. The `predict` step modifies the
 input to the transformer layer, and the `correct` step propagates the output
 of the transformer layer to the sparsely updated dimensions.
See more in the research paper:
https://proceedings.neurips.cc/paper_files/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf
 """
def __init__(self, config: Gemma3nTextConfig):
 super().__init__()
 self.config = config
 self.correct_output_scale = nn.Parameter(torch.zeros(self.config.hidden_size))
 self.correction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs, bias=False)
 self.prediction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs**2, bias=False)
 self.modality_router = nn.Linear(self.config.hidden_size, self.config.altup_num_inputs, bias=False)
 self.router_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
 self.register_buffer("router_input_scale", torch.tensor(self.config.hidden_size**-1.0), persistent=False)
def compute_router_modalities(self, x: torch.Tensor) -> torch.Tensor:
 router_inputs = self.router_norm(x) * self.router_input_scale
 routed = self.modality_router(router_inputs)
 return torch.tanh(routed.float()).type_as(x)
def predict(self, hidden_states: torch.Tensor) -> torch.Tensor:
 """Predicts the output of a layer using a trainable map.
Args:
 hidden_states: A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` derived by
 stacking the input embeddings and preprocessing the last `num_altup_inputs - 1` matrices.
Returns:
 A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` containing the predictions.
 """
 modalities = self.compute_router_modalities(hidden_states[self.config.altup_active_idx])
if self.training and self.config.altup_coef_clip is not None:
 self.prediction_coefs.weight.data.clamp_(-self.config.altup_coef_clip, self.config.altup_coef_clip)
# Project and then transpose all 2D matrices contained so that mulmat gives the correct result
 all_coefs: torch.Tensor = (
 self.prediction_coefs(modalities)
 .reshape(*modalities.shape[:-1], self.config.altup_num_inputs, self.config.altup_num_inputs)
 .permute(0, 1, 3, 2)
 )
# permute hidden_states to [batch_size, num_tokens, hidden_size, altup_num_inputs]
 predictions = torch.matmul(hidden_states.permute(1, 2, 3, 0), all_coefs)
 predictions = predictions.permute(3, 0, 1, 2) # undo the permute
 predictions += hidden_states # add the original input
 return predictions.contiguous().type_as(hidden_states)
def correct(self, predictions: torch.Tensor, activated: torch.Tensor) -> torch.Tensor:
 """Corrects the predictions relative to the
Args:
 predictions: A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` derived by
 stacking the input embeddings and preprocessing the last `num_altup_inputs - 1` matrices.
 activated: A 3D tensor of shape `[batch_size, num_tokens, hidden_size]` containing the activated inputs.
Returns:
 A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` correcting the original
 predictions relative to the activated input embeddings.
 """
 modalities = self.compute_router_modalities(activated)
 innovation = activated - predictions[self.config.altup_active_idx] # (batch, num_tokens, hidden_size)
 innovation = innovation.repeat(self.config.altup_num_inputs, 1, 1, 1) # Repeat on dim0 to match predictions
if self.config.altup_coef_clip is not None:
 self.correction_coefs.weight.data.clamp_(-self.config.altup_coef_clip, self.config.altup_coef_clip)
# all_coefs adapted from jax.numpy.einsum("...p,pi->...i", ...)
 # Permute to (altup_num_inputs, batch_size, num_tokens) as the last dim is a scalar applied to each altup input
 # and expand on dim1 for broadcastability
 all_coefs: torch.Tensor = self.correction_coefs(modalities) + 1.0
 all_coefs = all_coefs.permute(2, 0, 1).unsqueeze(-1)
corrected = torch.mul(innovation, all_coefs)
 corrected += predictions # add the original input
 return corrected.contiguous().type_as(activated)
def forward(self, corrected: torch.Tensor) -> torch.Tensor:
 """
 This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
 (which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
 `scale_corrected_output`
 """
 return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
 """Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
 return self.forward(corrected)
class Gemma3nTextRotaryEmbedding(nn.Module):
 def __init__(self, config: Gemma3nTextConfig, device=None):
 super().__init__()
 # BC: "rope_type" was originally "type"
 if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
 self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
 else:
 self.rope_type = "default"
 self.max_seq_len_cached = config.max_position_embeddings
 self.original_max_seq_len = config.max_position_embeddings
self.config = config
 self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
 self.register_buffer("inv_freq", inv_freq, persistent=False)
 self.original_inv_freq = self.inv_freq
@torch.no_grad()
 @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
 def forward(self, x, position_ids):
 inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
 position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
 with torch.autocast(device_type=device_type, enabled=False): # Force float32
 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
 emb = torch.cat((freqs, freqs), dim=-1)
 cos = emb.cos() * self.attention_scaling
 sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x):
 """Rotates half the hidden dims of the input."""
 x1 = x[..., : x.shape[-1] // 2]
 x2 = x[..., x.shape[-1] // 2 :]
 return torch.cat((-x2, x1), dim=-1)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 """
 This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
 num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
 """
 batch, num_key_value_heads, slen, head_dim = hidden_states.shape
 if n_rep == 1:
 return hidden_states
 hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
 return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
 module: nn.Module,
 query: torch.Tensor,
 key: torch.Tensor,
 value: torch.Tensor,
 attention_mask: Optional[torch.Tensor],
 dropout: float = 0.0,
 scaling: Optional[float] = None,
 softcap: Optional[float] = None,
 **kwargs,
 ) -> tuple[torch.Tensor, torch.Tensor]:
 if scaling is None:
 scaling = module.head_dim**-0.5
key_states = repeat_kv(key, module.num_key_value_groups)
 value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if softcap is not None:
 attn_weights = attn_weights / softcap
 attn_weights = torch.tanh(attn_weights)
 attn_weights = attn_weights * softcap
 if attention_mask is not None: # no matter the length, we just slice it
 causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
 attn_weights = attn_weights + causal_mask
# upcast attention to fp32
 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
 attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
 attn_output = torch.matmul(attn_weights, value_states)
 attn_output = attn_output.transpose(1, 2).contiguous()
 return attn_output, attn_weights
def apply_rotary_pos_emb(
 x: torch.Tensor,
 cos: torch.Tensor,
 sin: torch.Tensor,
 position_ids: Optional[torch.Tensor] = None,
 unsqueeze_dim: int = 1,
 ):
 """Applies Rotary Position Embedding to the query and key tensors.
Args:
 x (`torch.Tensor`): The tensor to embed.
 cos (`torch.Tensor`): The cosine part of the rotary embedding.
 sin (`torch.Tensor`): The sine part of the rotary embedding.
 position_ids (`torch.Tensor`, *optional*):
 Deprecated and unused.
 unsqueeze_dim (`int`, *optional*, defaults to 1):
 The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
 sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
 that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
 k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
 cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
 the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
 Returns:
 `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
 """
 cos = cos.unsqueeze(unsqueeze_dim)
 sin = sin.unsqueeze(unsqueeze_dim)
 return (x * cos) + (rotate_half(x) * sin)
class Gemma3nTextAttention(nn.Module):
 """Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
 super().__init__()
 self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
 self.config = config
 self.layer_idx = layer_idx
 self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
 self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
 self.attention_dropout = self.config.attention_dropout
 self.is_causal = True
self.q_proj = nn.Linear(
 config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
 )
 self.k_proj = nn.Linear(
 config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
 )
 self.v_proj = nn.Linear(
 config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
 )
 self.o_proj = nn.Linear(
 config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
 )
 self.sliding_window = config.sliding_window if self.is_sliding else None
self.q_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
 self.k_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
 self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
 self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
 # Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
 layer_type = config.layer_types[layer_idx]
 self.kv_shared_layer_index = (
 first_kv_shared_layer_idx - 1 - config.layer_types[first_kv_shared_layer_idx - 1 :: -1].index(layer_type)
 if self.is_kv_shared_layer
 else None
 )
def forward(
 self,
 hidden_states: torch.Tensor,
 position_embeddings: torch.Tensor,
 attention_mask: Optional[torch.Tensor],
 past_key_value: Optional[Cache] = None,
 cache_position: Optional[torch.LongTensor] = None,
 **kwargs: Unpack[FlashAttentionKwargs],
 ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
 input_shape = hidden_states.shape[:-1]
 hidden_shape = (*input_shape, -1, self.config.head_dim)
cos, sin = position_embeddings
query_states = self.q_proj(hidden_states).view(hidden_shape)
 query_states = self.q_norm(query_states)
 query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)
 query_states = query_states.transpose(1, 2)
if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
 # Device of past layer may be different from current one
 indices = cache_position.to(past_key_value.key_cache[self.kv_shared_layer_index].device)
 # In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
 if isinstance(past_key_value, HybridCache) and self.is_sliding:
 max_length = past_key_value.sliding_window
 indices = (
 slice(0, max_length)
 if cache_position.shape[0] > max_length
 else cache_position.clamp(min=0, max=max_length - 1)
 )
# Device of past layer may be different from current one
 key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices].to(query_states.device)
 value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices].to(
 query_states.device
 )
 else:
 key_states = self.k_proj(hidden_states).view(hidden_shape)
 key_states = self.k_norm(key_states)
 key_states = apply_rotary_pos_emb(key_states, cos, sin, unsqueeze_dim=2)
 key_states = key_states.transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape)
 value_states = self.v_norm(value_states)
 value_states = value_states.transpose(1, 2)
if past_key_value is not None:
 # sin and cos are specific to RoPE models; cache_position needed for the static cache
 cache_kwargs = {
 "sin": sin,
 "cos": cos,
 "cache_position": cache_position,
 "sliding_window": self.sliding_window,
 }
 key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
 if self.config._attn_implementation != "eager":
 attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
 self,
 query_states,
 key_states,
 value_states,
 attention_mask,
 dropout=self.attention_dropout if self.training else 0.0,
 scaling=1.0,
 sliding_window=self.sliding_window,
 **kwargs,
 )
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
 attn_output = self.o_proj(attn_output)
 return attn_output, attn_weights
class Gemma3nTextDecoderLayer(GradientCheckpointingLayer):
 def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
 super().__init__()
 self.config = config
 self.hidden_size = config.hidden_size
 self.layer_idx = layer_idx
 self.attention_type = config.layer_types[layer_idx]
 self.self_attn = Gemma3nTextAttention(config, layer_idx)
 self.mlp = Gemma3nTextMLP(config, layer_idx=layer_idx)
 self.input_layernorm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
 self.post_attention_layernorm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
 self.pre_feedforward_layernorm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
 self.post_feedforward_layernorm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
 self.act_fn = ACT2FN[config.hidden_activation]
self.altup = Gemma3nTextAltUp(config)
 self.laurel = Gemma3nTextLaurelBlock(config)
 self.per_layer_input_gate = nn.Linear(self.hidden_size, self.hidden_size_per_layer_input, bias=False)
 self.per_layer_projection = nn.Linear(self.hidden_size_per_layer_input, self.hidden_size, bias=False)
 self.post_per_layer_input_norm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
@deprecate_kwarg("last_cache_position", version="4.53.0")
 def forward(
 self,
 hidden_states: torch.Tensor,
 position_embeddings_global: torch.Tensor,
 position_embeddings_local: torch.Tensor,
 per_layer_input: torch.Tensor,
 attention_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.LongTensor] = None,
 past_key_value: Optional[Cache] = None,
 output_attentions: Optional[bool] = False,
 use_cache: Optional[bool] = False,
 cache_position: Optional[torch.LongTensor] = None,
 **kwargs,
 ) -> tuple[torch.Tensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
 predictions = self.altup.predict(hidden_states)
 active_prediction = predictions[self.config.altup_active_idx]
active_prediction_normed = self.input_layernorm(active_prediction)
 laurel_output = self.laurel(active_prediction_normed)
# apply global RoPE to non-sliding layer only
 if self.self_attn.is_sliding:
 position_embeddings = position_embeddings_local
 else:
 position_embeddings = position_embeddings_global
attn, self_attn_weights = self.self_attn(
 hidden_states=active_prediction_normed,
 position_embeddings=position_embeddings,
 attention_mask=attention_mask,
 position_ids=position_ids,
 past_key_value=past_key_value,
 output_attentions=output_attentions,
 use_cache=use_cache,
 cache_position=cache_position,
 **kwargs,
 )
 attn = self.post_attention_layernorm(attn)
attn_gated = active_prediction + attn
 attn_laurel = (attn_gated + laurel_output) / math.sqrt(2)
attn_norm = self.pre_feedforward_layernorm(attn_laurel)
 attn_ffw = self.mlp(attn_norm)
 attn_ffw_norm = self.post_feedforward_layernorm(attn_ffw)
 attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
 corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
 if self.config.altup_correct_scale:
 first_prediction = self.altup.scale_corrected_output(first_prediction)
# per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
 first_prediction = self.per_layer_input_gate(first_prediction)
 first_prediction = self.act_fn(first_prediction)
 first_prediction = torch.multiply(first_prediction, per_layer_input)
# per_layer_projection adapted from jax.numpy.einsum("btp,pd->btd", ...)
 first_prediction = self.per_layer_projection(first_prediction)
 first_prediction = self.post_per_layer_input_norm(first_prediction)
 corrected_predictions[1:] += first_prediction
outputs = (corrected_predictions,)
if output_attentions:
 outputs += (self_attn_weights,)
return outputs
@auto_docstring
 class Gemma3nPreTrainedModel(PreTrainedModel):
 config_class = Gemma3nConfig
 base_model_prefix = ""
 supports_gradient_checkpointing = True
 _no_split_modules = ["Gemma3nTextDecoderLayer"]
 _skip_keys_device_placement = ["past_key_values"]
 _supports_flash_attn_3 = True
 _supports_flash_attn_2 = True
 _supports_sdpa = True
 _supports_flex_attn = True
 _supports_cache_class = True
 _supports_quantized_cache = True
 _supports_static_cache = True
 _supports_attention_backend = True
def _init_weights(self, module):
 # important: this ported version of Gemma2 isn't meant for training from scratch - only
 # inference and fine-tuning - so the proper init weights code has been removed
 std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)):
 module.weight.data.normal_(mean=0.0, std=std)
 if module.bias is not None:
 module.bias.data.zero_()
 elif isinstance(module, nn.Embedding):
 module.weight.data.normal_(mean=0.0, std=std)
 if module.padding_idx is not None:
 module.weight.data[module.padding_idx].zero_()
 elif isinstance(module, Gemma3nRMSNorm):
 if module.with_scale:
 module.weight.data.fill_(1.0)
 elif isinstance(module, Gemma3nAudioCumulativeGroupNorm):
 module.weight.data.fill_(1.0)
 elif isinstance(module, Gemma3nAudioAttention):
 module.per_dim_scale.data.zero_()
 elif isinstance(module, Gemma3nTextAltUp):
 module.correct_output_scale.data.zero_()
@auto_docstring(custom_intro="The base Gemma 3n language model without a language modeling head.")
 class Gemma3nTextModel(Gemma3nPreTrainedModel):
 config_class = Gemma3nTextConfig
def __init__(self, config: Gemma3nTextConfig):
 super().__init__(config)
 self.padding_idx = config.pad_token_id
 self.vocab_size = config.vocab_size
# Gemma3n downcasts the below to bfloat16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
 self.embed_tokens = Gemma3nTextScaledWordEmbedding(
 config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
 )
 self.layers = nn.ModuleList(
 [Gemma3nTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
 )
self.norm = Gemma3nRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 self.rotary_emb = Gemma3nTextRotaryEmbedding(config=config)
 self.gradient_checkpointing = False
# TODO (raushan): Fix this after RoPE refactor. For now we hack it by
 # reassigning thetas when we want to create a local RoPE layer. Config
 # defaults should hold values for global RoPE.
 config = copy.deepcopy(config)
 config.rope_theta = config.rope_local_base_freq
 config.rope_scaling = {"rope_type": "default"}
 self.rotary_emb_local = Gemma3nTextRotaryEmbedding(config=config)
self.hidden_size = config.hidden_size
 self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
self.embed_tokens_per_layer = Gemma3nTextScaledWordEmbedding(
 config.vocab_size_per_layer_input,
 config.num_hidden_layers * config.hidden_size_per_layer_input,
 self.padding_idx,
 embed_scale=config.hidden_size_per_layer_input**0.5,
 )
self.per_layer_model_projection = nn.Linear(
 self.hidden_size,
 config.num_hidden_layers * config.hidden_size_per_layer_input,
 bias=False,
 )
self.per_layer_projection_norm = Gemma3nRMSNorm(config.hidden_size_per_layer_input, eps=config.rms_norm_eps)
self.altup_projections = nn.ModuleList(
 [nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
 )
self.altup_unembed_projections = nn.ModuleList(
 [nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
 )
self.register_buffer("per_layer_projection_scale", torch.tensor(self.hidden_size**-0.5), persistent=False)
 self.register_buffer("per_layer_input_scale", torch.rsqrt(torch.tensor(2.0)), persistent=False)
# Initialize weights and apply final processing
 self.post_init()
def get_input_embeddings(self):
 return self.embed_tokens
def set_input_embeddings(self, value):
 self.embed_tokens = value
@can_return_tuple
 @auto_docstring
 def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None,
 per_layer_inputs: Optional[torch.Tensor] = None,
 attention_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.LongTensor] = None,
 past_key_values: Optional[Cache] = None,
 inputs_embeds: Optional[torch.FloatTensor] = None,
 use_cache: Optional[bool] = None,
 output_attentions: Optional[bool] = None,
 output_hidden_states: Optional[bool] = None,
 cache_position: Optional[torch.LongTensor] = None,
 **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
 ) -> BaseModelOutputWithPast:
 r"""
 per_layer_inputs (torch.Tensor, *optional*, defaults to None):
 Pre-computed per-layer embeddings. If None, they are derived from input_ids if provided.
 """
 output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
 output_hidden_states = (
 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
 )
 use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
 raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
 logger.warning_once(
 "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
 )
 use_cache = False
if input_ids is not None:
 inputs_embeds = self.embed_tokens(input_ids)
 per_layer_inputs = self.get_per_layer_inputs(input_ids)
per_layer_inputs = self.project_per_layer_inputs(inputs_embeds, per_layer_inputs)
if use_cache and past_key_values is None and not self.training:
 past_key_values = DynamicCache()
if cache_position is None:
 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
 cache_position = torch.arange(
 past_seen_tokens,
 past_seen_tokens + inputs_embeds.shape[1],
 device=inputs_embeds.device,
 )
if position_ids is None:
 position_ids = cache_position.unsqueeze(0)
# It may already have been prepared by e.g. `generate`
 if not isinstance(causal_mask_mapping := attention_mask, dict):
 # Prepare mask arguments
 mask_kwargs = {
 "config": self.config,
 "input_embeds": inputs_embeds,
 "attention_mask": attention_mask,
 "cache_position": cache_position,
 "past_key_values": past_key_values,
 }
 # Create the masks
 causal_mask_mapping = {
 "full_attention": create_causal_mask(**mask_kwargs),
 "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
 }
# embed positions
 hidden_states_0 = inputs_embeds
# Initialize RoPE embeddings
 position_embeddings_global = self.rotary_emb(hidden_states_0, position_ids)
 position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
# Expand hidden_states to support per-layer inputs
 target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
 epsilon_tensor = torch.tensor(1e-5)
temp_hidden_states = [hidden_states_0]
 for i in range(1, self.config.altup_num_inputs):
 # altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
 altup_proj = self.altup_projections[i - 1](hidden_states_0)
 current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
 new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
 new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
 current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
 temp_hidden_states.append(current_hidden_state)
hidden_states = torch.stack(temp_hidden_states, dim=0) # [num_altup_inputs, batch, seq_len, hidden_size]
# decoder layers
 all_hidden_states = () if output_hidden_states else None
 all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
 if output_hidden_states:
 all_hidden_states += (hidden_states,)
causal_mask = causal_mask_mapping[decoder_layer.attention_type]
 per_layer_input = per_layer_inputs[:, :, decoder_layer.layer_idx, :]
layer_outputs = decoder_layer(
 hidden_states,
 position_embeddings_global,
 position_embeddings_local,
 per_layer_input,
 attention_mask=causal_mask,
 position_ids=position_ids,
 past_key_value=past_key_values,
 output_attentions=output_attentions,
 use_cache=use_cache,
 cache_position=cache_position,
 **flash_attn_kwargs,
 )
hidden_states = layer_outputs[0]
if output_attentions:
 all_self_attns += (layer_outputs[1],)
# add hidden states from the last decoder layer (but before reprojecting to stay consistent with layer output)
 if output_hidden_states:
 all_hidden_states += (hidden_states,)
# Per-layer inputs to single output
 target_magnitude = torch.mean(hidden_states[0] ** 2, dim=-1, keepdim=True) ** 0.5
 temp_hidden_states = [hidden_states[0]]
 for i in range(1, self.config.altup_num_inputs):
 # altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
 altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
 current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
 new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
 new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
 current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
 temp_hidden_states.append(current_hidden_state)
hidden_states = torch.stack(temp_hidden_states)
 hidden_states = torch.mean(hidden_states, dim=0)
 hidden_states = self.norm(hidden_states)
return BaseModelOutputWithPast(
 last_hidden_state=hidden_states,
 past_key_values=past_key_values,
 hidden_states=all_hidden_states,
 attentions=all_self_attns,
 )
def get_per_layer_inputs(self, input_ids: torch.LongTensor) -> torch.Tensor:
 return self.embed_tokens_per_layer(input_ids).reshape(
 *input_ids.shape,
 self.config.num_hidden_layers,
 self.hidden_size_per_layer_input,
 )
def project_per_layer_inputs(
 self,
 inputs_embeds: torch.Tensor,
 per_layer_inputs: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
 per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
 per_layer_projection *= self.per_layer_projection_scale.to(
 dtype=inputs_embeds.dtype, device=per_layer_projection.device
 )
 per_layer_projection = per_layer_projection.reshape(
 *inputs_embeds.shape[:-1],
 self.config.num_hidden_layers,
 self.hidden_size_per_layer_input,
 )
 per_layer_projection = self.per_layer_projection_norm(per_layer_projection)
if per_layer_inputs is None:
 return per_layer_projection
if per_layer_projection.shape != per_layer_inputs.shape:
 # per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
 per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
 dtype=inputs_embeds.dtype, device=per_layer_projection.device
 )
@auto_docstring(custom_intro="The base Gemma 3n language model with a language modeling head.")
 class Gemma3nForCausalLM(Gemma3nPreTrainedModel, GenerationMixin):
 _tied_weights_keys = ["lm_head.weight"]
 _tp_plan = {"lm_head": "colwise_rep"}
 _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
 config_class = Gemma3nTextConfig
 base_model_prefix = "model"
 _checkpoint_conversion_mapping = {"model.language_model": "model"}
def __init__(self, config: Gemma3nTextConfig):
 super().__init__(config)
 self.model = Gemma3nTextModel(config)
 self.vocab_size = config.vocab_size
 self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
 self.post_init()
def get_input_embeddings(self):
 return self.model.embed_tokens
def set_input_embeddings(self, value):
 self.model.embed_tokens = value
def get_output_embeddings(self):
 return self.lm_head
def set_output_embeddings(self, new_embeddings):
 self.lm_head = new_embeddings
def set_decoder(self, decoder):
 self.model = decoder
def get_decoder(self):
 return self.model
@can_return_tuple
 @auto_docstring
 def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None,
 attention_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.LongTensor] = None,
 past_key_values: Optional[Cache] = None,
 inputs_embeds: Optional[torch.FloatTensor] = None,
 labels: Optional[torch.LongTensor] = None,
 use_cache: Optional[bool] = None,
 output_attentions: Optional[bool] = None,
 output_hidden_states: Optional[bool] = None,
 cache_position: Optional[torch.LongTensor] = None,
 logits_to_keep: Union[int, torch.Tensor] = 0,
 **loss_kwargs,
 ) -> CausalLMOutputWithPast:
 r"""
 labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
 config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
 >>> from transformers import AutoTokenizer, Gemma3nForCausalLM
>>> model = Gemma3nForCausalLM.from_pretrained("google/gemma-2-9b")
 >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
>>> prompt = "What is your favorite condiment?"
 >>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
 >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
 >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 "What is your favorite condiment?"
 ```"""
if self.training and self.config._attn_implementation != "eager":
 logger.warning_once(
 "It is strongly recommended to train Gemma3n models with the `eager` attention implementation "
 f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
 )
 output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
 output_hidden_states = (
 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
 )
 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
 outputs: BaseModelOutputWithPast = self.model(
 input_ids=input_ids,
 attention_mask=attention_mask,
 position_ids=position_ids,
 past_key_values=past_key_values,
 inputs_embeds=inputs_embeds,
 use_cache=use_cache,
 output_attentions=output_attentions,
 output_hidden_states=output_hidden_states,
 cache_position=cache_position,
 **loss_kwargs,
 )
hidden_states = outputs.last_hidden_state
 # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
 slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
 logits = self.lm_head(hidden_states[:, slice_indices, :])
 if self.config.final_logit_softcapping is not None:
 logits = logits / self.config.final_logit_softcapping
 logits = torch.tanh(logits)
 logits = logits * self.config.final_logit_softcapping
loss = None
 if labels is not None:
 loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
return CausalLMOutputWithPast(
 loss=loss,
 logits=logits,
 past_key_values=outputs.past_key_values,
 hidden_states=outputs.hidden_states,
 attentions=outputs.attentions,
 )
class Gemma3nMultimodalEmbedder(nn.Module):
 """Embeds token ids or soft tokens for multimodal content into language model space."""
def __init__(
 self,
 multimodal_config: Union[Gemma3nAudioConfig, Gemma3nVisionConfig],
 text_config: Gemma3nTextConfig,
 ):
 super().__init__()
self.multimodal_hidden_size = multimodal_config.hidden_size
 self.eps = multimodal_config.rms_norm_eps
 self.vocab_offset = multimodal_config.vocab_offset
 self.vocab_size = multimodal_config.vocab_size
 self.text_hidden_size = text_config.hidden_size
self.embedding = nn.Embedding(self.vocab_size, self.multimodal_hidden_size)
 self.hard_embedding_norm = Gemma3nRMSNorm(self.multimodal_hidden_size, eps=self.eps)
 self.soft_embedding_norm = Gemma3nRMSNorm(self.multimodal_hidden_size, eps=self.eps)
 self.embedding_projection = nn.Linear(self.multimodal_hidden_size, self.text_hidden_size, bias=False)
 self.embedding_post_projection_norm = Gemma3nRMSNorm(self.text_hidden_size, eps=self.eps, with_scale=False)
def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None,
 inputs_embeds: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
 """Embeds token ids or soft tokens for multimodal content into language model space.
Args:
 input_ids: A torch.LongTensor containing the token ids to embed. Values should be in the range
 `[vocab_offset, vocab_offset + vocab_size)`.
 inputs_embeds: A torch.Tensor containing the soft tokens to embed.
Returns:
 A torch.Tensor of embeddings with shape `[batch_size, seq_len, self.config.text_config.hidden_size]`.
 """
 if (input_ids is None) ^ (inputs_embeds is not None):
 raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is not None:
 emb_norm = self.soft_embedding_norm(inputs_embeds)
 else:
 hard_emb = self.embedding(input_ids - self.vocab_offset)
 emb_norm = self.hard_embedding_norm(hard_emb)
emb_norm_proj = self.embedding_projection(emb_norm)
 return self.embedding_post_projection_norm(emb_norm_proj)
@auto_docstring(
 custom_intro="""
 The base Gemma 3n model comprising a vision backbone, an audio backbone, and a language model without a
 language modeling head.
 """
 )
 class Gemma3nModel(Gemma3nPreTrainedModel):
 _checkpoint_conversion_mapping = {}
 # we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
 accepts_loss_kwargs = False
def __init__(self, config: Gemma3nConfig):
 super().__init__(config)
 self.vision_tower = AutoModel.from_config(config=config.vision_config)
 self.vocab_size = config.text_config.vocab_size
language_model = AutoModel.from_config(config=config.text_config)
 self.language_model = language_model
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
 self.vocab_size_per_layer_input = config.text_config.vocab_size_per_layer_input
 self.audio_tower = AutoModel.from_config(config.audio_config)
 self.embed_vision = Gemma3nMultimodalEmbedder(config.vision_config, config.text_config)
 self.embed_audio = Gemma3nMultimodalEmbedder(config.audio_config, config.text_config)
 self.post_init()
def get_input_embeddings(self):
 return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
 self.language_model.set_input_embeddings(value)
def set_decoder(self, decoder):
 self.language_model = decoder
def get_decoder(self):
 return self.language_model
def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
 """
 Projects the last hidden state from the vision model into language model space.
Args:
 pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
 The tensors corresponding to the input images.
Returns:
 image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
 """
 vision_outputs = self.vision_tower(
 pixel_values=pixel_values, do_pooling=False, return_dict=True
 ).last_hidden_state
 # Convert from (batch, channels, height, width) to (batch, height * width, channels) where:
 # height == width and height * width == Gemma3nConfig.vision_soft_tokens_per_image.
 vision_outputs = vision_outputs.reshape(
 vision_outputs.shape[0],
 self.config.vision_config.hidden_size,
 self.config.vision_soft_tokens_per_image,
 ).permute(0, 2, 1)
 # Normalize and embed the soft tokens into language model space.
 vision_outputs *= self.config.vision_config.hidden_size**0.5
 return self.embed_vision(inputs_embeds=vision_outputs)
@can_return_tuple
 def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None, # text inputs
 pixel_values: Optional[torch.FloatTensor] = None, # vision inputs
 input_features: Optional[torch.FloatTensor] = None, # audio inputs
 attention_mask: Optional[torch.Tensor] = None,
 input_features_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.LongTensor] = None,
 past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
 token_type_ids: Optional[torch.LongTensor] = None,
 cache_position: Optional[torch.LongTensor] = None,
 inputs_embeds: Optional[torch.FloatTensor] = None,
 labels: Optional[torch.LongTensor] = None,
 use_cache: Optional[bool] = None,
 output_attentions: Optional[bool] = None,
 output_hidden_states: Optional[bool] = None,
 **lm_kwargs,
 ) -> Gemma3nCausalLMOutputWithPast:
 r"""
 labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
 config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
Example:
```python
 >>> from PIL import Image
 >>> import requests
 >>> from transformers import AutoProcessor, Gemma3nForConditionalGeneration
>>> model = Gemma3nForConditionalGeneration.from_pretrained("google/gemma3n2-3b-mix-224")
 >>> processor = AutoProcessor.from_pretrained("google/gemma3n2-3b-mix-224")
>>> prompt = "Where is the cat standing?"
 >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
 >>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
>>> # Generate
 >>> generate_ids = model.generate(**inputs,)
 >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 "Where is the cat standing?\nsnow"
 ```
 """
 if (input_ids is None) ^ (inputs_embeds is not None):
 raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
 output_hidden_states = (
 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
 )
if input_ids is not None:
 inputs_embeds = self.get_input_embeddings()(input_ids)
# Prepare per-layer inputs from inputs_ids
 per_layer_inputs_mask = torch.logical_and(input_ids >= 0, input_ids < self.vocab_size_per_layer_input)
 per_layer_inputs_tokens = torch.where(per_layer_inputs_mask, input_ids, torch.zeros_like(input_ids))
 per_layer_inputs = self.language_model.get_per_layer_inputs(per_layer_inputs_tokens)
# Handle vision tokens (>= embed_vision.vocab_offset and < embed_audio.vocab_offset)
 vision_mask = torch.logical_and(
 input_ids >= self.embed_vision.vocab_offset, input_ids < self.embed_audio.vocab_offset
 )
 dummy_vision_token_id = self.embed_vision.vocab_offset + self.embed_vision.vocab_size - 1
 vision_input_ids = torch.where(vision_mask, input_ids, dummy_vision_token_id).to(inputs_embeds.device)
 vision_embeds = self.embed_vision(input_ids=vision_input_ids)
 expanded_vision_mask = vision_mask.unsqueeze(-1).expand_as(inputs_embeds)
 inputs_embeds = torch.where(expanded_vision_mask, vision_embeds, inputs_embeds)
# Handle audio tokens (>= embed_audio.vocab_offset)
 audio_mask = input_ids >= self.embed_audio.vocab_offset
 dummy_audio_token_id = self.embed_audio.vocab_offset + self.embed_audio.vocab_size - 1
 audio_input_ids = torch.where(audio_mask, input_ids, dummy_audio_token_id).to(inputs_embeds.device)
 audio_embeds = self.embed_audio(input_ids=audio_input_ids)
 expanded_audio_mask = audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
 inputs_embeds = torch.where(expanded_audio_mask, audio_embeds, inputs_embeds)
 else:
 per_layer_inputs = None
# Merge text and images
 if pixel_values is not None:
 image_features = self.get_image_features(pixel_values)
if input_ids is None:
 special_image_mask = inputs_embeds == self.get_input_embeddings()(
 torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
 )
 else:
 special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
 special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
 image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
 raise ValueError(
 f"Number of images does not match number of special image tokens in the input text. "
 f"Got {image_tokens_in_text} image tokens in the text and "
 f"{image_features.shape[0] * image_features.shape[1]} tokens from image embeddings."
 )
 image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
 inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
# Merge text and audio
 if input_features is not None and input_features_mask is not None:
 audio_features, audio_mask = self.get_audio_features(input_features, ~input_features_mask)
# The Gemma3nProcessor expects all audio will be 30s in length and inserts 188 audio soft tokens into the
 # text to account for this. However, the audio preprocessing and encoder do not gurarantee they will
 # produce 188 soft tokens; they will produce at most that many tokens, but they may produce fewer tokens
 # depending on the length of the longest audio input in the batch. When we encounter this situation, we pad
 # the audio feature out to 188 soft tokens with the emebedding of the last token in the embed_audio vocab.
 audio_padding_toks = torch.tensor([[self.vocab_size - 1]], dtype=torch.long, device=audio_features.device)
 audio_padding_embs = self.embed_audio(input_ids=audio_padding_toks)
 audio_features = torch.where(audio_mask.unsqueeze(-1), audio_padding_embs, audio_features)
audio_batch_size, audio_seq_len, audio_embed_dim = audio_features.shape
 extra_padding_tokens = self.config.audio_soft_tokens_per_image - audio_seq_len
 extra_padding_features = audio_padding_embs.expand(audio_batch_size, extra_padding_tokens, audio_embed_dim)
audio_features = torch.cat((audio_features, extra_padding_features), dim=1)
if input_ids is None:
 special_audio_mask = inputs_embeds == self.embed_audio(
 input_ids=torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
 )
 else:
 special_audio_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
 special_audio_mask = special_audio_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
if not is_torchdynamo_compiling() and inputs_embeds[special_audio_mask].numel() != audio_features.numel():
 audio_tokens_in_text = (special_audio_mask).sum(dim=1).sum(dim=0)[0]
 raise ValueError(
 f"Number of audio input features does not match number of special audio tokens in the input text. "
 f"Got {audio_tokens_in_text} audio tokens in the text and "
 f"{audio_features.shape[0] * audio_features.shape[1]} tokens from audio embeddings."
 )
 audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
 inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_features)
outputs = self.language_model(
 input_ids=None,
 per_layer_inputs=per_layer_inputs,
 attention_mask=attention_mask,
 position_ids=position_ids,
 past_key_values=past_key_values,
 inputs_embeds=inputs_embeds,
 use_cache=use_cache,
 output_attentions=output_attentions,
 output_hidden_states=output_hidden_states,
 return_dict=True,
 cache_position=cache_position,
 **lm_kwargs,
 )
return Gemma3nModelOutputWithPast(
 last_hidden_state=outputs.last_hidden_state,
 past_key_values=outputs.past_key_values if use_cache else None,
 hidden_states=outputs.hidden_states,
 attentions=outputs.attentions,
 image_hidden_states=image_features if pixel_values is not None else None,
 audio_hidden_states=audio_features if input_features is not None else None,
 )
def get_audio_features(
 self, input_features: torch.Tensor, input_features_mask: torch.Tensor
 ) -> tuple[torch.Tensor, torch.Tensor]:
 """
 Projects the last hidden state from the audio encoder into language model space.
Args:
 input_features (`torch.FloatTensor]` of shape `(num_images, seq_length, num_features)`):
 The tensors corresponding to the input audio.
 input_features (`torch.FloatTensor]` of shape `(num_images, seq_length)`):
 The attention mask for the input audio.
Returns:
 audio_features (`torch.Tensor`): Audio feature tensor of shape `(num_images, audio_length, embed_dim)`).
 """
 audio_outputs, audio_mask = self.audio_tower(input_features, input_features_mask)
 return self.embed_audio(inputs_embeds=audio_outputs), audio_mask
@auto_docstring(
 custom_intro="""
 The base Gemma 3n model comprising a vision backbone, an audio backbone, a language model, and a language modeling
 head.
 """
 )
 class Gemma3nForConditionalGeneration(Gemma3nPreTrainedModel, GenerationMixin):
 _checkpoint_conversion_mapping = {}
 _tied_weights_keys = ["lm_head.weight"]
 base_model_prefix = "model"
def __init__(self, config: Gemma3nConfig):
 super().__init__(config)
 self.model = Gemma3nModel(config)
 self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
 self.post_init()
def get_input_embeddings(self):
 return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
 self.model.set_input_embeddings(value)
def get_output_embeddings(self):
 return self.lm_head
def set_output_embeddings(self, new_embeddings):
 self.lm_head = new_embeddings
def set_decoder(self, decoder):
 self.model.set_decoder(decoder)
def get_decoder(self):
 return self.model.get_decoder()
def get_image_features(self, pixel_values):
 return self.model.get_image_features(pixel_values)
# Make modules available throught conditional class for BC
 @property
 def language_model(self):
 return self.model.language_model
@property
 def vision_tower(self):
 return self.model.vision_tower
@property
 def multi_modal_projector(self):
 raise AttributeError("Use embed_vision instead of multi_modal_projector.")
@can_return_tuple
 @auto_docstring
 def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None, # text inputs
 pixel_values: Optional[torch.FloatTensor] = None, # vision inputs
 input_features: Optional[torch.FloatTensor] = None, # audio inputs
 attention_mask: Optional[torch.Tensor] = None,
 input_features_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.LongTensor] = None,
 past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
 token_type_ids: Optional[torch.LongTensor] = None,
 cache_position: Optional[torch.LongTensor] = None,
 inputs_embeds: Optional[torch.FloatTensor] = None,
 labels: Optional[torch.LongTensor] = None,
 use_cache: Optional[bool] = None,
 output_attentions: Optional[bool] = None,
 output_hidden_states: Optional[bool] = None,
 logits_to_keep: Union[int, torch.Tensor] = 0,
 **lm_kwargs,
 ) -> Gemma3nCausalLMOutputWithPast:
 r"""
 input_features (torch.Tensor, *optional*, defaults to None):
 The audio inputs to be encoded.
 input_features_mask (torch.Tensor, *optional*, defaults to None):
 The attention mask for the input audio.
 labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
 config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are
 ignored (masked), the loss is only computed for the tokens with labels in
 `[0, ..., config.text_config.vocab_size]`.
Example:
```python
 >>> from PIL import Image
 >>> import requests
 >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
>>> model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma-3-4b-it")
 >>> processor = AutoProcessor.from_pretrained("google/gemma-3-4b-it")
>>> messages = [
 ... {
 ... "role": "system",
 ... "content": [
 ... {"type": "text", "text": "You are a helpful assistant."}
 ... ]
 ... },
 ... {
 ... "role": "user", "content": [
 ... {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
 ... {"type": "text", "text": "Where is the cat standing?"},
 ... ]
 ... },
 ... ]
>>> inputs = processor.apply_chat_template(
 ... messages,
 ... tokenizer=True,
 ... return_dict=True,
 ... return_tensors="pt",
 ... add_generation_prompt=True
 ... )
 >>> # Generate
 >>> generate_ids = model.generate(**inputs)
 >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 "user\nYou are a helpful assistant.\n\n\n\n\n\nWhere is the cat standing?\nmodel\nBased on the image, the cat is standing in a snowy area, likely outdoors. It appears to"
 ```
 """
 output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
 output_hidden_states = (
 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
 )
outputs = self.model(
 input_ids=input_ids,
 pixel_values=pixel_values,
 input_features=input_features,
 attention_mask=attention_mask,
 input_features_mask=input_features_mask,
 position_ids=position_ids,
 past_key_values=past_key_values,
 token_type_ids=token_type_ids,
 cache_position=cache_position,
 inputs_embeds=inputs_embeds,
 labels=labels,
 use_cache=use_cache,
 output_attentions=output_attentions,
 output_hidden_states=output_hidden_states,
 return_dict=True,
 **lm_kwargs,
 )
hidden_states = outputs.last_hidden_state
 # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
 slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
 logits = self.lm_head(hidden_states[:, slice_indices, :])
 if (final_logit_softcapping := self.config.get_text_config().final_logit_softcapping) is not None:
 logits = logits / final_logit_softcapping
 logits = torch.tanh(logits)
 logits = logits * final_logit_softcapping
loss = None
 if labels is not None:
 # Upcast to float if we need to compute the loss to avoid potential precision issues
 logits = logits.float()
 shift_logits = logits[..., :-1, :]
 shift_labels = labels[..., 1:]
 if attention_mask is not None:
 # we use the input attention mask to shift the logits and labels, because it is 2D.
 # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
 shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
 shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
 shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
 else:
 shift_logits = shift_logits.contiguous()
 shift_labels = shift_labels.contiguous()
 # Flatten the tokens
 loss_fct = nn.CrossEntropyLoss()
flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
 flat_labels = shift_labels.view(-1).to(shift_logits.device)
 loss = loss_fct(flat_logits, flat_labels)
return Gemma3nCausalLMOutputWithPast(
 loss=loss,
 logits=logits,
 past_key_values=outputs.past_key_values,
 hidden_states=outputs.hidden_states,
 attentions=outputs.attentions,
 image_hidden_states=outputs.image_hidden_states,
 audio_hidden_states=outputs.audio_hidden_states,
 )
def prepare_inputs_for_generation(
 self,
 input_ids,
 past_key_values=None,
 inputs_embeds=None,
 cache_position=None,
 position_ids=None,
 pixel_values=None,
 input_features=None,
 attention_mask=None,
 input_features_mask=None,
 token_type_ids=None,
 use_cache=True,
 logits_to_keep=None,
 labels=None,
 **kwargs,
 ):
 # Overwritten -- custom `position_ids` and `pixel_values` handling
 model_inputs = super().prepare_inputs_for_generation(
 input_ids,
 past_key_values=past_key_values,
 inputs_embeds=inputs_embeds,
 attention_mask=attention_mask,
 position_ids=position_ids,
 cache_position=cache_position,
 use_cache=use_cache,
 logits_to_keep=logits_to_keep,
 token_type_ids=token_type_ids,
 **kwargs,
 )
# If we're in cached decoding stage, multimodal inputs should be None because input ids do not contain special
 # tokens anymore. Otherwise multimodal inputs should be passed to model.
 # NOTE: use_cache=False always needs pixel_values, input_features, and input_features_mask
 if cache_position[0] == 0:
 model_inputs["pixel_values"] = pixel_values
 model_inputs["input_features"] = input_features
 model_inputs["input_features_mask"] = input_features_mask
return model_inputs
@property
 def audio_tower(self):
 return self.model.audio_tower
__all__ = [
 "Gemma3nAudioEncoder",
 "Gemma3nForCausalLM",
 "Gemma3nForConditionalGeneration",
 "Gemma3nModel",
 "Gemma3nPreTrainedModel",
 "Gemma3nTextModel",
 ]
</script>
<script id="dependencies" type="text/plain">
 # coding=utf-8
 # Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 # http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import copy
 import inspect
 import os
 import warnings
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Callable, Optional, Union
import numpy as np
 import torch
 import torch.distributed as dist
 from huggingface_hub import file_exists
 from packaging import version
 from torch import nn
 from torch.nn import functional as F
from ..cache_utils import (
 Cache,
 DynamicCache,
 EncoderDecoderCache,
 HybridChunkedCache,
 OffloadedCache,
 OffloadedHybridCache,
 QuantizedCacheConfig,
 )
 from ..configuration_utils import PretrainedConfig
 from ..dynamic_module_utils import (
 check_python_requirements,
 get_cached_module_file,
 get_class_in_module,
 resolve_trust_remote_code,
 )
 from ..integrations.deepspeed import is_deepspeed_zero3_enabled
 from ..integrations.fsdp import is_fsdp_managed_module
 from ..masking_utils import create_masks_for_generate
 from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
 from ..pytorch_utils import isin_mps_friendly
 from ..tokenization_utils import ExtensionsTrie
 from ..utils import (
 ModelOutput,
 is_accelerate_available,
 is_hqq_available,
 is_optimum_quanto_available,
 is_torchdynamo_exporting,
 logging,
 )
 from .beam_constraints import DisjunctiveConstraint, PhrasalConstraint
 from .beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
 from .candidate_generator import (
 AssistantVocabTranslatorCache,
 AssistedCandidateGenerator,
 AssistedCandidateGeneratorDifferentTokenizers,
 CandidateGenerator,
 EarlyExitCandidateGenerator,
 PromptLookupCandidateGenerator,
 UniversalSpeculativeDecodingGenerator,
 _crop_past_key_values,
 _prepare_attention_mask,
 _prepare_token_type_ids,
 )
 from .configuration_utils import (
 NEED_SETUP_CACHE_CLASSES_MAPPING,
 QUANT_BACKEND_CLASSES_MAPPING,
 CompileConfig,
 GenerationConfig,
 GenerationMode,
 )
 from .continuous_batching import ContinuousMixin
 from .logits_process import (
 EncoderNoRepeatNGramLogitsProcessor,
 EncoderRepetitionPenaltyLogitsProcessor,
 EpsilonLogitsWarper,
 EtaLogitsWarper,
 ExponentialDecayLengthPenalty,
 ForcedBOSTokenLogitsProcessor,
 ForcedEOSTokenLogitsProcessor,
 HammingDiversityLogitsProcessor,
 InfNanRemoveLogitsProcessor,
 LogitNormalization,
 LogitsProcessorList,
 MinLengthLogitsProcessor,
 MinNewTokensLengthLogitsProcessor,
 MinPLogitsWarper,
 NoBadWordsLogitsProcessor,
 NoRepeatNGramLogitsProcessor,
 PrefixConstrainedLogitsProcessor,
 RepetitionPenaltyLogitsProcessor,
 SequenceBiasLogitsProcessor,
 SuppressTokensAtBeginLogitsProcessor,
 SuppressTokensLogitsProcessor,
 TemperatureLogitsWarper,
 TopKLogitsWarper,
 TopPLogitsWarper,
 TypicalLogitsWarper,
 UnbatchedClassifierFreeGuidanceLogitsProcessor,
 )
 from .stopping_criteria import (
 ConfidenceCriteria,
 EosTokenCriteria,
 MaxLengthCriteria,
 MaxTimeCriteria,
 StoppingCriteria,
 StoppingCriteriaList,
 StopStringCriteria,
 )
if TYPE_CHECKING:
 from ..modeling_utils import PreTrainedModel
 from ..tokenization_utils_base import PreTrainedTokenizerBase
 from .streamers import BaseStreamer
logger = logging.get_logger(__name__)
if is_accelerate_available():
 from accelerate.hooks import AlignDevicesHook, add_hook_to_module
# Variable names used to hold the cache at generation time
 ALL_CACHE_NAMES = [
 "past_key_values", # default
 "cache_params", # mamba-based models
 "state", # rwkv
 "mems", # xlnet
 "past_buckets_states", # reformer
 ]
@dataclass
 class GenerateDecoderOnlyOutput(ModelOutput):
 """
 Outputs of decoder-only generation models, when using non-beam methods.
Args:
 sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
 The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
 if all batches finished early due to the `eos_token_id`.
 scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
 Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
 logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
 Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
 attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
 hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
 past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`):
 Returns the model cache, used to speed up decoding. Different models have a different cache format, check
 the model's documentation. Usually, a [`~cache_utils.Cache`] instance.
 """
sequences: torch.LongTensor
 scores: Optional[tuple[torch.FloatTensor]] = None
 logits: Optional[tuple[torch.FloatTensor]] = None
 attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None
 past_key_values: Optional[tuple[tuple[tuple[torch.FloatTensor]]]] = None
@dataclass
 class GenerateEncoderDecoderOutput(ModelOutput):
 """
 Outputs of encoder-decoder generation models, when using non-beam methods.
Args:
 sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
 The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
 if all batches finished early due to the `eos_token_id`.
 scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
 Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
 logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
 Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
 encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`):
 Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
 sequence_length, sequence_length)`.
 encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`):
 Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
 shape `(batch_size, sequence_length, hidden_size)`.
 decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
 cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
 decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
 past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
 Returns the model cache, used to speed up decoding. Different models have a different cache format, check
 the model's documentation. Usually, a [`~cache_utils.Cache`] instance.
 """
sequences: torch.LongTensor
 scores: Optional[tuple[torch.FloatTensor]] = None
 logits: Optional[tuple[torch.FloatTensor]] = None
 encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
 encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
 decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 cross_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 decoder_hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None
 past_key_values: Optional[tuple[tuple[tuple[torch.FloatTensor]]]] = None
@dataclass
 class GenerateBeamDecoderOnlyOutput(ModelOutput):
 """
 Outputs of decoder-only generation models, when using beam methods.
Args:
 sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
 The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
 if all batches finished early due to the `eos_token_id`.
 sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True`):
 Final beam scores of the generated `sequences`.
 scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
 Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
 of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
 Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
 with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
 logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
 Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
 beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True`):
 Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
 `(batch_size*num_return_sequences, sequence_length)`.
 attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
 hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
 past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`):
 Returns the model cache, used to speed up decoding. Different models have a different cache format, check
 the model's documentation. Usually, a [`~cache_utils.Cache`] instance.
 """
sequences: torch.LongTensor
 sequences_scores: Optional[torch.FloatTensor] = None
 scores: Optional[tuple[torch.FloatTensor]] = None
 logits: Optional[tuple[torch.FloatTensor]] = None
 beam_indices: Optional[torch.LongTensor] = None
 attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None
 past_key_values: Optional[tuple[tuple[tuple[torch.FloatTensor]]]] = None
@dataclass
 class GenerateBeamEncoderDecoderOutput(ModelOutput):
 """
 Outputs of encoder-decoder generation models, when using beam methods.
Args:
 sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
 The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
 if all batches finished early due to the `eos_token_id`.
 sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True`):
 Final beam scores of the generated `sequences`.
 scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
 Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
 of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
 Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
 with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
 logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
 Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
 at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
 each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
 beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True`):
 Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
 `(batch_size*num_return_sequences, sequence_length)`.
 encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`):
 Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
 sequence_length, sequence_length)`.
 encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`):
 Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
 shape `(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
 decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
 sequence_length)`.
 cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
 decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
 Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
 `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
 past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`):
 Returns the model cache, used to speed up decoding. Different models have a different cache format, check
 the model's documentation. Usually, a [`~cache_utils.Cache`] instance.
 """
sequences: torch.LongTensor
 sequences_scores: Optional[torch.FloatTensor] = None
 scores: Optional[tuple[torch.FloatTensor]] = None
 logits: Optional[tuple[torch.FloatTensor]] = None
 beam_indices: Optional[torch.LongTensor] = None
 encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
 encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
 decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 cross_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
 decoder_hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None
 past_key_values: Optional[tuple[tuple[tuple[torch.FloatTensor]]]] = None
# TODO (joao): remove the equivalent classes and typing shortcuts below in v5
 # Equivalent classes (kept for retrocompatibility purposes)
 GreedySearchDecoderOnlyOutput = GenerateDecoderOnlyOutput
 ContrastiveSearchDecoderOnlyOutput = GenerateDecoderOnlyOutput
 SampleDecoderOnlyOutput = GenerateDecoderOnlyOutput
ContrastiveSearchEncoderDecoderOutput = GenerateEncoderDecoderOutput
 GreedySearchEncoderDecoderOutput = GenerateEncoderDecoderOutput
 SampleEncoderDecoderOutput = GenerateEncoderDecoderOutput
BeamSearchDecoderOnlyOutput = GenerateBeamDecoderOnlyOutput
 BeamSampleDecoderOnlyOutput = GenerateBeamDecoderOnlyOutput
BeamSearchEncoderDecoderOutput = GenerateBeamEncoderDecoderOutput
 BeamSampleEncoderDecoderOutput = GenerateBeamEncoderDecoderOutput
GreedySearchOutput = Union[GreedySearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput]
 SampleOutput = Union[SampleEncoderDecoderOutput, SampleDecoderOnlyOutput]
 BeamSearchOutput = Union[BeamSearchEncoderDecoderOutput, BeamSearchDecoderOnlyOutput]
 BeamSampleOutput = Union[BeamSampleEncoderDecoderOutput, BeamSampleDecoderOnlyOutput]
 ContrastiveSearchOutput = Union[ContrastiveSearchEncoderDecoderOutput, ContrastiveSearchDecoderOnlyOutput]
# Typing shortcuts
 GenerateNonBeamOutput = Union[GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput]
 GenerateBeamOutput = Union[GenerateBeamDecoderOnlyOutput, GenerateBeamEncoderDecoderOutput]
 GenerateOutput = Union[GenerateNonBeamOutput, GenerateBeamOutput]
class GenerationMixin(ContinuousMixin):
 """
 A class containing all functions for auto-regressive text generation, to be used as a mixin in model classes.
 Inheriting from this class causes the model to have special generation-related behavior, such as loading a
 `GenerationConfig` at initialization time or ensuring `generate`-related tests are run in `transformers` CI.
A model class should inherit from `GenerationMixin` to enable calling methods like `generate`, or when it
 has defined a custom `generate` method that relies on `GenerationMixin`, directly or indirectly, which
 approximately shares the same interface to public methods like `generate`. Three examples:
 - `LlamaForCausalLM` should inherit from `GenerationMixin` to enable calling `generate` and other public
 methods in the mixin;
 - `BlipForQuestionAnswering` has a custom `generate` method that approximately shares the same interface as
 `GenerationMixin.generate` (it has a few extra arguments, and the same output). That function also calls
 `GenerationMixin.generate` indirectly, through an inner model. As such, `BlipForQuestionAnswering` should
 inherit from `GenerationMixin` to benefit from all generation-related automation in our codebase;
 - `BarkModel` has a custom `generate` method and one of its inner models calls `GenerationMixin.generate`.
 However, its `generate` does not share the same interface as `GenerationMixin.generate`. In this case,
 `BarkModel` should NOT inherit from `GenerationMixin`, as it breaks the `generate` interface.
The class exposes [`~generation.GenerationMixin.generate`], which can be used for:
 - *greedy decoding* if `num_beams=1` and `do_sample=False`
 - *contrastive search* if `penalty_alpha>0` and `top_k>1`
 - *multinomial sampling* if `num_beams=1` and `do_sample=True`
 - *beam-search decoding* if `num_beams>1` and `do_sample=False`
 - *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True`
 - *diverse beam-search decoding* if `num_beams>1` and `num_beam_groups>1`
 - *constrained beam-search decoding* if `constraints!=None` or `force_words_ids!=None`
 - *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`
To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
 """
def load_custom_generate(
 self,
 pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
 trust_remote_code: Optional[bool] = None,
 **kwargs,
 ) -> Callable:
 """
 Loads and returns a custom generate function, given a model repo.
Args:
 pretrained_model_name_or_path (`str` or `os.PathLike`):
 Can be either:
 - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
 - A path to a *directory* containing model weights saved using
 [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
 trust_remote_code (`bool`, *optional*):
 Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
 should only be set to `True` for repositories you trust and in which you have read the code, as it will
 execute code present on the Hub on your local machine.
 **kwargs:
 Additional keyword arguments for remote code loading.
Raises:
 OSError: If `pretrained_model_name_or_path` does not contain a `custom_generate` subdirectory.
Returns:
 A callable that can be used to generate text.
 """
 # Does `pretrained_model_name_or_path` have a `custom_generate` subdirectory? If not -> OSError
 is_local_code = os.path.exists(pretrained_model_name_or_path)
 has_custom_generate_folder = True
 if is_local_code:
 if not os.path.exists(os.path.join(pretrained_model_name_or_path, "custom_generate/generate.py")):
 has_custom_generate_folder = False
 else:
 if not file_exists(pretrained_model_name_or_path, "custom_generate/generate.py"):
 has_custom_generate_folder = False
if not has_custom_generate_folder:
 raise OSError(
 f"`{pretrained_model_name_or_path}` does not contain a `custom_generate` subdirectory with a "
 "`generate.py` file, can't load the custom generate function."
 )
# Handle opt-in `trust_remote_code` and related exceptions
 error_message = (
 f"The repository `{pretrained_model_name_or_path}` contains custom generation code that will override "
 "the default `generate` method."
 )
 resolve_trust_remote_code(
 trust_remote_code,
 pretrained_model_name_or_path,
 has_local_code=is_local_code,
 has_remote_code=not is_local_code,
 error_message=error_message,
 )
# Load the custom generate function
 check_python_requirements(
 pretrained_model_name_or_path, requirements_file="custom_generate/requirements.txt", **kwargs
 )
 module = get_cached_module_file(
 pretrained_model_name_or_path, module_file="custom_generate/generate.py", **kwargs
 )
 custom_generate_function = get_class_in_module("generate", module)
 return custom_generate_function
def _cache_dependant_input_preparation(
 self,
 input_ids: torch.LongTensor,
 inputs_embeds: Optional[torch.FloatTensor],
 cache_position: Optional[torch.LongTensor],
 ) -> tuple[torch.FloatTensor, torch.LongTensor]:
 """
 Generic cache-dependent input preparation
 The code is put in a separate function to allow granular unit testing
 as it needs a different implementation to be exportable.
If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
 - Exception 1: when passing input_embeds, input_ids may be missing entries
 - Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
 - Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
 - Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
 generate the first token for each sequence. Later use the generated Input ids for continuation.
The current implementation does not rely on ``self`` and could be
 a class method. It is left as a standard method to be easily rewritten.
 """
 if is_torchdynamo_exporting():
 return self._cache_dependant_input_preparation_exporting(input_ids, inputs_embeds, cache_position)
 if inputs_embeds is not None and input_ids.shape[1] == 0: # Exception 4
 inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
 elif (
 inputs_embeds is not None # Exception 1
 or (cache_position[-1] >= input_ids.shape[1]) # Exception 3
 ):
 input_ids = input_ids[:, -cache_position.shape[0] :]
 elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
 input_ids = input_ids[:, cache_position]
 return inputs_embeds, input_ids
def _cache_dependant_input_preparation_exporting(
 self,
 input_ids: torch.LongTensor,
 inputs_embeds: Optional[torch.FloatTensor],
 cache_position: Optional[torch.LongTensor],
 ) -> tuple[torch.FloatTensor, torch.LongTensor]:
 """
 This method implements method ``_cache_dependant_input_preparation``
 with :func:`torch.cond` to make it exportable with :func:`torch.export.export`.
 The code is put in a separate function to allow granular unit testing.
 """
 if inputs_embeds is None:
 input_ids = input_ids[:, cache_position]
 else:
 # This is the code we need to implemented with torch.cond.
 # if input_ids.shape[1] == 0:
 # inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
 # else:
 # if cache_position[-1] >= input_ids.shape[1]:
 # input_ids = input_ids[:, -cache_position.shape[0] :]
 # else:
 # if input_ids.shape[1] != cache_position.shape[0]:
 # input_ids = input_ids[:, cache_position]
 def branch_1(inputs_embeds, cache_position):
 return inputs_embeds[:, -cache_position.shape[0] :]
def branch_2(input_ids, cache_position):
 return input_ids[:, -cache_position.shape[0] :]
def branch_3(input_ids, cache_position):
 return input_ids[:, cache_position]
inputs_embeds, input_ids = torch.cond(
 input_ids.shape[1] == 0,
 (
 lambda input_ids, inputs_embeds, cache_position: (
 branch_1(inputs_embeds, cache_position),
 input_ids,
 )
 ),
 (
 lambda input_ids, inputs_embeds, cache_position: (
 inputs_embeds,
 torch.cond(
 cache_position[-1] >= input_ids.shape[1],
 branch_2,
 lambda input_ids, cache_position: (
 torch.cond(
 input_ids.shape[1] != cache_position.shape[0],
 branch_3,
 (lambda input_ids, cache_position: input_ids),
 [input_ids, cache_position],
 )
 ),
 [input_ids, cache_position],
 ),
 )
 ),
 [input_ids, inputs_embeds, cache_position],
 )
 return inputs_embeds, input_ids
def prepare_inputs_for_generation(
 self,
 input_ids: torch.LongTensor,
 past_key_values: Optional[Cache] = None,
 attention_mask: Optional[torch.LongTensor] = None,
 inputs_embeds: Optional[torch.FloatTensor] = None,
 cache_position: Optional[torch.LongTensor] = None,
 **kwargs,
 ):
 """
 Prepare the model inputs for generation. It includes operations like computing the 4D attention mask or
 slicing inputs given the existing cache.
See the forward pass in the model documentation for expected arguments (different models might have different
 requirements for e.g. `past_key_values`). This function should work as is for most LLMs.
 """
# 1. Handle BC:
 model_inputs = {}
 # - some models don't have `Cache` support (which implies they don't expect `cache_position` in `forward`)
 if self._supports_cache_class:
 model_inputs["cache_position"] = cache_position
 # - `cache_position` was not a mandatory input in `prepare_inputs_for_generation` for those models, and this
 # function may be called outside of `generate`. Handle most use cases by creating `cache_position` on the fly
 # (this alternative is not as robust as calling `generate` and letting it create `cache_position`)
 elif cache_position is None:
 past_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
 cache_position = torch.arange(past_length, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
# 2. Generic cache-dependent input preparation
 if past_key_values is not None:
 model_inputs["past_key_values"] = past_key_values
 inputs_embeds, input_ids = self._cache_dependant_input_preparation(
 input_ids, inputs_embeds, cache_position
 )
# 3. Prepare base model inputs
 input_ids_key = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
 # if `inputs_embeds` are passed, we only want to use them in the 1st generation step for every prompt.
 if not self.config.is_encoder_decoder:
 if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
 model_inputs[input_ids_key] = None
 model_inputs["inputs_embeds"] = inputs_embeds
 else:
 # `clone` calls in this function ensure a consistent stride. See #32227
 model_inputs[input_ids_key] = input_ids.clone(memory_format=torch.contiguous_format)
 model_inputs["inputs_embeds"] = None
 else:
 model_inputs[input_ids_key] = input_ids.clone(memory_format=torch.contiguous_format)
# 4. Create missing `position_ids` on the fly
 encoder_attention_mask = attention_mask if self.config.is_encoder_decoder else None
 attention_mask = (
 kwargs.pop("decoder_attention_mask", None) if self.config.is_encoder_decoder else attention_mask
 )
 attention_mask_key = "decoder_attention_mask" if self.config.is_encoder_decoder else "attention_mask"
 position_ids_key = "decoder_position_ids" if self.config.is_encoder_decoder else "position_ids"
 if (
 attention_mask is not None
 and kwargs.get(position_ids_key) is None
 and position_ids_key in set(inspect.signature(self.forward).parameters.keys())
 ):
 position_ids = attention_mask.long().cumsum(-1) - 1
 position_ids.masked_fill_(attention_mask == 0, 1)
 kwargs[position_ids_key] = position_ids # placed in kwargs for further processing (see below)
# 5. Slice model inputs if it's an input that should have the same length as `input_ids`
 for model_input_name in ["position_ids", "token_type_ids", "decoder_position_ids"]:
 model_input = kwargs.get(model_input_name)
 if model_input is not None:
 if past_key_values is not None:
 current_input_length = (
 model_inputs["inputs_embeds"].shape[1]
 if model_inputs.get("inputs_embeds") is not None
 else model_inputs[input_ids_key].shape[1]
 )
 model_input = model_input[:, -current_input_length:]
 model_input = model_input.clone(memory_format=torch.contiguous_format)
 model_inputs[model_input_name] = model_input
# 6. Create 4D attention mask is we are using a compilable cache (important for performant compiled forward
 # pass)
 if (
 isinstance(past_key_values, Cache)
 and past_key_values.is_compileable
 and attention_mask is not None
 and attention_mask.ndim == 2
 ):
 if not self.config.is_encoder_decoder and model_inputs["inputs_embeds"] is not None:
 batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
 else:
 batch_size, sequence_length = model_inputs[input_ids_key].shape[:2]
# Create the causal mask with fixed shape in advance, to reduce recompilations. If the function to create
 # the 4D causal mask exists, it should be present in the base model (XXXModel class) or in its decoder.
 base_model = getattr(self, self.base_model_prefix, self)
 decoder = base_model.get_decoder() if hasattr(base_model, "get_decoder") else None
 causal_mask_creation_function = getattr(
 base_model, "_prepare_4d_causal_attention_mask_with_cache_position", None
 )
 if causal_mask_creation_function is None and decoder is not None: # it may be in the decoder
 causal_mask_creation_function = getattr(
 decoder, "_prepare_4d_causal_attention_mask_with_cache_position", None
 )
# If it's not defined, it means the model uses the new general mask API
 if causal_mask_creation_function is None: # can't be found
 token_type_ids = getattr(model_input, "token_type_ids", None)
 # Some models may overwrite the general one
 causal_mask_creation_function = getattr(self, "create_masks_for_generate", create_masks_for_generate)
 attention_mask = causal_mask_creation_function(
 config=self.config,
 # we only need batch size, seq_length and dtype here - we don't care about the values of the embeddings
 input_embeds=torch.empty((batch_size, sequence_length), dtype=self.dtype),
 attention_mask=attention_mask,
 cache_position=cache_position,
 past_key_values=past_key_values,
 token_type_ids=token_type_ids,
 )
 else:
 attention_mask = causal_mask_creation_function(
 attention_mask,
 sequence_length=sequence_length,
 target_length=past_key_values.get_max_cache_shape(),
 dtype=self.dtype,
 cache_position=cache_position,
 batch_size=batch_size,
 config=self.config,
 past_key_values=past_key_values,
 )
 if attention_mask is not None:
 model_inputs[attention_mask_key] = attention_mask
if encoder_attention_mask is not None:
 model_inputs["attention_mask"] = encoder_attention_mask
# 7. Forward ALL kwargs that are uninitialized (e.g. `use_cache`).
 for key, value in kwargs.items():
 if key not in model_inputs:
 model_inputs[key] = value
# 8. Remove unexpected `generate` inputs (TODO @joao: fix trainer and examples)
 model_inputs.pop("labels", None)
 return model_inputs
def _prepare_model_inputs(
 self,
 inputs: Optional[torch.Tensor] = None,
 bos_token_id: Optional[torch.Tensor] = None,
 model_kwargs: Optional[dict[str, torch.Tensor]] = None,
 ) -> tuple[torch.Tensor, Optional[str], dict[str, torch.Tensor]]:
 """
 This function extracts the model-specific `inputs` for generation.
 """
 # 1. retrieve all kwargs that are non-None or non-model input related.
 # some encoder-decoder models have different names for model and encoder
 if (
 self.config.is_encoder_decoder
 and hasattr(self, "encoder")
 and self.encoder.main_input_name != self.main_input_name
 ):
 input_name = self.encoder.main_input_name
 else:
 input_name = self.main_input_name
model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None or k != input_name}
# 2. check whether model_input_name is passed as kwarg
 # if yes and `inputs` is None use kwarg inputs
 inputs_kwarg = model_kwargs.pop(input_name, None)
 if inputs_kwarg is not None and inputs is not None:
 raise ValueError(
 f"`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. "
 f"Make sure to either pass {inputs} or {input_name}=..."
 )
 elif inputs_kwarg is not None:
 inputs = inputs_kwarg
# 3. In the presence of `inputs_embeds` for text models:
 # - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
 # doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
 # input_ids (`inputs_embeds` will be used in the 1st generation step, as opposed to `input_ids`)
 # - encoder-decoder models should complain if the user attempts to pass `inputs_embeds` and `input_ids`, and
 # pull the former to inputs. It will be used in place of `input_ids` to get the encoder hidden states.
 if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
 if model_kwargs["inputs_embeds"] is None:
 model_kwargs.pop("inputs_embeds")
 elif not self.config.is_encoder_decoder:
 has_inputs_embeds_forwarding = "inputs_embeds" in set(
 inspect.signature(self.prepare_inputs_for_generation).parameters.keys()
 )
 if not has_inputs_embeds_forwarding:
 raise ValueError(
 f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
 "doesn't have its forwarding implemented. See the GPT2 implementation for an example "
 "(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with it!"
 )
 # In this case, `input_ids` is moved to the `model_kwargs`, so a few automations (like the creation of
 # the attention mask) can rely on the actual model input.
 model_kwargs["input_ids"] = self._maybe_initialize_input_ids_for_generation(
 inputs, bos_token_id, model_kwargs=model_kwargs
 )
 inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
 else:
 if inputs is not None:
 raise ValueError("You passed `inputs_embeds` and `input_ids` to `.generate()`. Please pick one.")
 inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
# 4. if `inputs` is still None, try to create `input_ids` from BOS token
 inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
 return inputs, input_name, model_kwargs
def _maybe_initialize_input_ids_for_generation(
 self,
 inputs: Optional[torch.Tensor] = None,
 bos_token_id: Optional[torch.Tensor] = None,
 model_kwargs: Optional[dict[str, torch.Tensor]] = None,
 ) -> torch.LongTensor:
 """Initializes input ids for generation, if necessary."""
 if inputs is not None:
 return inputs
encoder_outputs = model_kwargs.get("encoder_outputs")
 if self.config.is_encoder_decoder and encoder_outputs is not None:
 # make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
 shape = encoder_outputs.last_hidden_state.size()[:-1]
 return torch.ones(shape, dtype=torch.long, device=self.device) * -100
# If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
 # soft-prompting or in multimodal implementations built on top of decoder-only language models.
 batch_size = 1
 for value in model_kwargs.values():
 if isinstance(value, torch.Tensor):
 batch_size = value.shape[0]
 break
if "inputs_embeds" in model_kwargs:
 return torch.ones((batch_size, 0), dtype=torch.long, device=self.device)
if bos_token_id is None:
 raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id
def _prepare_attention_mask_for_generation(
 self,
 inputs_tensor: torch.Tensor,
 generation_config: GenerationConfig,
 model_kwargs: dict[str, Any],
 ) -> torch.LongTensor:
 pad_token_id = generation_config._pad_token_tensor
 eos_token_id = generation_config._eos_token_tensor
# `input_ids` may be present in the model kwargs, instead of being the main input (e.g. multimodal model)
 if "input_ids" in model_kwargs and model_kwargs["input_ids"].shape[1] > 0:
 inputs_tensor = model_kwargs["input_ids"]
# No information for attention mask inference -> return default attention mask
 default_attention_mask = torch.ones(inputs_tensor.shape[:2], dtype=torch.long, device=inputs_tensor.device)
 if pad_token_id is None:
 return default_attention_mask
is_input_ids = len(inputs_tensor.shape) == 2 and inputs_tensor.dtype in [torch.int, torch.long]
 if not is_input_ids:
 return default_attention_mask
is_pad_token_in_inputs = (pad_token_id is not None) and (
 isin_mps_friendly(elements=inputs_tensor, test_elements=pad_token_id).any()
 )
 is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or ~(
 isin_mps_friendly(elements=eos_token_id, test_elements=pad_token_id).any()
 )
 can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
 attention_mask_from_padding = inputs_tensor.ne(pad_token_id).long()
attention_mask = (
 attention_mask_from_padding * can_infer_attention_mask + default_attention_mask * ~can_infer_attention_mask
 )
 return attention_mask
def _prepare_encoder_decoder_kwargs_for_generation(
 self,
 inputs_tensor: torch.Tensor,
 model_kwargs,
 model_input_name: Optional[str],
 generation_config: GenerationConfig,
 ) -> dict[str, Any]:
 # 1. get encoder
 encoder = self.get_encoder()
 # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
 # as the inputs.
 if hasattr(self, "hf_device_map"):
 if hasattr(encoder, "_hf_hook"):
 encoder._hf_hook.io_same_device = True
 else:
 add_hook_to_module(encoder, AlignDevicesHook(io_same_device=True))
# 2. Prepare encoder args and encoder kwargs from model kwargs and generation config.
 irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
 encoder_kwargs = {
 argument: value
 for argument, value in model_kwargs.items()
 if not any(argument.startswith(p) for p in irrelevant_prefix)
 }
 encoder_signature = set(inspect.signature(encoder.forward).parameters)
 encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
 if not encoder_accepts_wildcard:
 encoder_kwargs = {
 argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
 }
 encoder_kwargs["output_attentions"] = generation_config.output_attentions
 encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states
# 3. make sure that encoder returns `ModelOutput`
 model_input_name = model_input_name if model_input_name is not None else self.main_input_name
 encoder_kwargs["return_dict"] = True
 encoder_kwargs[model_input_name] = inputs_tensor
 model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs) # type: ignore
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
 self,
 batch_size: int,
 model_input_name: str,
 model_kwargs: dict[str, torch.Tensor],
 decoder_start_token_id: torch.Tensor,
 device: Optional[torch.device] = None,
 ) -> tuple[torch.LongTensor, dict[str, torch.Tensor]]:
 """Prepares `decoder_input_ids` for generation with encoder-decoder models"""
 # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
 # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
 if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
 decoder_input_ids = model_kwargs.pop("decoder_input_ids")
 elif "input_ids" in model_kwargs and model_input_name != "input_ids":
 decoder_input_ids = model_kwargs.pop("input_ids")
 else:
 decoder_input_ids = None
# 2. `decoder_start_token_id` must have shape (batch_size, 1)
 if device is None:
 device = self.device
 if decoder_start_token_id.ndim == 1:
 if decoder_start_token_id.shape[0] != batch_size:
 raise ValueError(
 f"`decoder_start_token_id` expected to have length {batch_size} but got {decoder_start_token_id.shape[0]}"
 )
 decoder_start_token_id = decoder_start_token_id.view(-1, 1)
 else:
 decoder_start_token_id = (
 torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
 )
# 3. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
 # no user input -> use decoder_start_token_id as decoder_input_ids
 if decoder_input_ids is None:
 decoder_input_ids = decoder_start_token_id
 # exception: Donut checkpoints have task-specific decoder starts and don't expect a BOS token. Note that the
 # original checkpoints can't be detected through `self.__class__.__name__.lower()`, needing custom logic.
 # See: https://github.com/huggingface/transformers/pull/31470
 elif "donut" in self.__class__.__name__.lower() or (
 self.config.model_type == "vision-encoder-decoder" and "donut" in self.config.encoder.model_type.lower()
 ):
 pass
 elif self.config.model_type in ["whisper"]:
 pass
 # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
 # decoder_attention_mask if provided)
 elif (decoder_input_ids[:, 0] != decoder_start_token_id[:, 0]).all().item():
 decoder_input_ids = torch.cat([decoder_start_token_id, decoder_input_ids], dim=-1)
 if "decoder_attention_mask" in model_kwargs:
 decoder_attention_mask = model_kwargs["decoder_attention_mask"]
 decoder_attention_mask = torch.cat(
 (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
 dim=-1,
 )
 model_kwargs["decoder_attention_mask"] = decoder_attention_mask
return decoder_input_ids, model_kwargs
@staticmethod
 def _expand_inputs_for_generation(
 expand_size: int = 1,
 is_encoder_decoder: bool = False,
 input_ids: Optional[torch.LongTensor] = None,
 **model_kwargs,
 ) -> tuple[torch.LongTensor, dict[str, Any]]:
 """Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
 # Do not call torch.repeat_interleave if expand_size is 1 because it clones
 # the input tensor and thus requires more memory although no change is applied
 if expand_size == 1:
 return input_ids, model_kwargs
def _expand_dict_for_generation(dict_to_expand):
 for key in dict_to_expand:
 if (
 key != "cache_position"
 and dict_to_expand[key] is not None
 and isinstance(dict_to_expand[key], torch.Tensor)
 ):
 dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
 return dict_to_expand
if input_ids is not None:
 input_ids = input_ids.repeat_interleave(expand_size, dim=0)
model_kwargs = _expand_dict_for_generation(model_kwargs)
if is_encoder_decoder:
 if model_kwargs.get("encoder_outputs") is None:
 raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
 model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
return input_ids, model_kwargs
def _update_model_kwargs_for_generation(
 self,
 outputs: ModelOutput,
 model_kwargs: dict[str, Any],
 is_encoder_decoder: bool = False,
 num_new_tokens: int = 1,
 ) -> dict[str, Any]:
 # update past_key_values keeping its naming used in model code
 for possible_cache_name in ALL_CACHE_NAMES:
 if possible_cache_name in outputs:
 # TODO (joao): remove output/input mismatch when these old models (xlnet, reformer) are deprecated
 if possible_cache_name in ("past_buckets_states", "mems"):
 cache_name = "past_key_values"
 else:
 cache_name = possible_cache_name
 model_kwargs[cache_name] = getattr(outputs, possible_cache_name)
 break
# update token_type_ids with last value
 if "token_type_ids" in model_kwargs:
 token_type_ids = model_kwargs["token_type_ids"]
 model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
if not is_encoder_decoder:
 # update attention mask
 if "attention_mask" in model_kwargs:
 attention_mask = model_kwargs["attention_mask"]
 model_kwargs["attention_mask"] = torch.cat(
 [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
 )
 else:
 # update decoder attention mask
 if "decoder_attention_mask" in model_kwargs:
 decoder_attention_mask = model_kwargs["decoder_attention_mask"]
 model_kwargs["decoder_attention_mask"] = torch.cat(
 [decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
 dim=-1,
 )
if model_kwargs.get("use_cache", True):
 model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
 else:
 past_positions = model_kwargs.pop("cache_position")
 new_positions = torch.arange(
 past_positions[-1] + 1, past_positions[-1] + num_new_tokens + 1, dtype=past_positions.dtype
 ).to(past_positions.device)
 model_kwargs["cache_position"] = torch.cat((past_positions, new_positions))
 return model_kwargs
def _reorder_cache(self, past_key_values, beam_idx):
 raise NotImplementedError(
 f"Make sure that a `_reorder_cache` function is correctly implemented in {self.__class__.__module__} to"
 f" enable beam search for {self.__class__}"
 )
def _get_candidate_generator(
 self,
 generation_config: GenerationConfig,
 input_ids: torch.LongTensor,
 inputs_tensor: torch.Tensor,
 assistant_model: "PreTrainedModel",
 logits_processor: LogitsProcessorList,
 target_tokenizer: "PreTrainedTokenizerBase",
 assistant_tokenizer: "PreTrainedTokenizerBase",
 model_kwargs: dict,
 ) -> CandidateGenerator:
 """
 Returns the candidate generator to be used in `assisted_generation`
 """
 different_tokenizers = all(v is not None for v in (assistant_model, target_tokenizer, assistant_tokenizer))
if generation_config.assistant_early_exit is not None:
 candidate_generator = EarlyExitCandidateGenerator(
 input_ids=input_ids,
 assistant_model=self,
 generation_config=generation_config,
 model_kwargs=model_kwargs,
 inputs_tensor=inputs_tensor,
 logits_processor=logits_processor,
 )
 elif generation_config.prompt_lookup_num_tokens is not None:
 candidate_generator = PromptLookupCandidateGenerator(
 eos_token_id=generation_config._eos_token_tensor,
 num_output_tokens=generation_config.prompt_lookup_num_tokens,
 max_matching_ngram_size=generation_config.max_matching_ngram_size,
 max_length=generation_config.max_length,
 )
 elif different_tokenizers:
 if generation_config.do_sample is True:
 atm_translator = AssistantVocabTranslatorCache.get_translator(
 target_tokenizer,
 assistant_tokenizer,
 self.config.get_text_config().vocab_size,
 assistant_model=assistant_model,
 assistant_prune_lm_head=True, # prune LM head of assistant model
 )
 # Since we prune the LM head, we cannot use the repetition penalty on the assistant model due to mismatches between token ids and logits index
 assistant_model.generation_config.repetition_penalty = None
 candidate_generator = UniversalSpeculativeDecodingGenerator(
 input_ids=input_ids,
 assistant_model=assistant_model,
 generation_config=generation_config,
 model_kwargs=model_kwargs,
 inputs_tensor=inputs_tensor,
 logits_processor=logits_processor,
 target_tokenizer=target_tokenizer,
 assistant_tokenizer=assistant_tokenizer,
 atm_translator=atm_translator,
 )
 elif generation_config.do_sample is False:
 candidate_generator = AssistedCandidateGeneratorDifferentTokenizers(
 input_ids=input_ids,
 assistant_model=assistant_model,
 generation_config=generation_config,
 model_kwargs=model_kwargs,
 inputs_tensor=inputs_tensor,
 logits_processor=logits_processor,
 target_tokenizer=target_tokenizer,
 assistant_tokenizer=assistant_tokenizer,
 )
 else:
 raise ValueError(
 f"Invalid value for `do_sample`: expected a boolean, got {type(generation_config.do_sample).__name__}"
 )
 else:
 candidate_generator = AssistedCandidateGenerator(
 input_ids=input_ids,
 assistant_model=assistant_model,
 generation_config=generation_config,
 model_kwargs=model_kwargs,
 inputs_tensor=inputs_tensor,
 logits_processor=logits_processor,
 )
 return candidate_generator
def _get_logits_processor(
 self,
 generation_config: GenerationConfig,
 input_ids_seq_length: Optional[int] = None,
 encoder_input_ids: torch.LongTensor = None,
 prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], list[int]]] = None,
 logits_processor: Optional[LogitsProcessorList] = None,
 device: Optional[str] = None,
 model_kwargs: Optional[dict[str, Any]] = None,
 negative_prompt_ids: Optional[torch.Tensor] = None,
 negative_prompt_attention_mask: Optional[torch.Tensor] = None,
 ) -> LogitsProcessorList:
 """
 This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
 instances used to modify the scores of the language model head.
 """
 # instantiate processors list
 processors = LogitsProcessorList()
 if logits_processor is None:
 logits_processor = []
if generation_config.guidance_scale is not None and generation_config.guidance_scale != 1:
 processors.append(
 UnbatchedClassifierFreeGuidanceLogitsProcessor(
 generation_config.guidance_scale,
 self,
 unconditional_ids=negative_prompt_ids,
 unconditional_attention_mask=negative_prompt_attention_mask,
 use_cache=generation_config.use_cache,
 )
 )
 if generation_config.sequence_bias is not None:
 processors.append(SequenceBiasLogitsProcessor(sequence_bias=generation_config.sequence_bias))
if generation_config.diversity_penalty is not None and generation_config.diversity_penalty > 0.0:
 processors.append(
 HammingDiversityLogitsProcessor(
 diversity_penalty=generation_config.diversity_penalty,
 num_beams=generation_config.num_beams,
 num_beam_groups=generation_config.num_beam_groups,
 )
 )
 if (
 generation_config.encoder_repetition_penalty is not None
 and generation_config.encoder_repetition_penalty != 1.0
 ):
 if len(encoder_input_ids.shape) == 2:
 processors.append(
 EncoderRepetitionPenaltyLogitsProcessor(
 penalty=generation_config.encoder_repetition_penalty,
 encoder_input_ids=encoder_input_ids,
 )
 )
 else:
 warnings.warn(
 "Passing `encoder_repetition_penalty` requires some form of `input_ids` to be passed to "
 "`generate`, ignoring the argument.",
 UserWarning,
 )
 if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
 processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
 if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
 processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
 if (
 generation_config.encoder_no_repeat_ngram_size is not None
 and generation_config.encoder_no_repeat_ngram_size > 0
 ):
 if len(encoder_input_ids.shape) == 2:
 processors.append(
 EncoderNoRepeatNGramLogitsProcessor(
 generation_config.encoder_no_repeat_ngram_size,
 encoder_input_ids,
 )
 )
 else:
 warnings.warn(
 "Passing `encoder_no_repeat_ngram_size` requires some form of `input_ids` to be passed to "
 "`generate`, ignoring the argument.",
 UserWarning,
 )
 if generation_config.bad_words_ids is not None:
 processors.append(
 NoBadWordsLogitsProcessor(
 generation_config.bad_words_ids,
 generation_config._eos_token_tensor,
 )
 )
 if (
 generation_config.min_length is not None
 and getattr(generation_config, "_eos_token_tensor", None) is not None
 and generation_config.min_length > 0
 ):
 processors.append(
 MinLengthLogitsProcessor(
 generation_config.min_length,
 generation_config._eos_token_tensor,
 device=device,
 )
 )
 if (
 generation_config.min_new_tokens is not None
 and getattr(generation_config, "_eos_token_tensor", None) is not None
 and generation_config.min_new_tokens > 0
 ):
 processors.append(
 MinNewTokensLengthLogitsProcessor(
 input_ids_seq_length,
 generation_config.min_new_tokens,
 generation_config._eos_token_tensor,
 device=device,
 )
 )
 if prefix_allowed_tokens_fn is not None:
 processors.append(
 PrefixConstrainedLogitsProcessor(
 prefix_allowed_tokens_fn,
 generation_config.num_beams // generation_config.num_beam_groups,
 )
 )
 if generation_config.forced_bos_token_id is not None:
 processors.append(
 ForcedBOSTokenLogitsProcessor(
 generation_config.forced_bos_token_id,
 )
 )
 if generation_config.forced_eos_token_id is not None:
 processors.append(
 ForcedEOSTokenLogitsProcessor(
 generation_config.max_length,
 generation_config.forced_eos_token_id,
 device=device,
 )
 )
 if generation_config.remove_invalid_values is True:
 processors.append(InfNanRemoveLogitsProcessor())
 if generation_config.exponential_decay_length_penalty is not None:
 processors.append(
 ExponentialDecayLengthPenalty(
 generation_config.exponential_decay_length_penalty,
 generation_config._eos_token_tensor,
 input_ids_seq_length,
 )
 )
 if generation_config.suppress_tokens is not None:
 processors.append(
 SuppressTokensLogitsProcessor(
 generation_config.suppress_tokens,
 device=device,
 )
 )
 if generation_config.begin_suppress_tokens is not None:
 begin_index = input_ids_seq_length
 begin_index = (
 begin_index
 if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
 else begin_index + 1
 )
 processors.append(
 SuppressTokensAtBeginLogitsProcessor(
 generation_config.begin_suppress_tokens,
 begin_index,
 device=device,
 )
 )
# TODO (joao): find a strategy to specify the order of the processors
 processors = self._merge_criteria_processor_list(processors, logits_processor)
# Processors previously known as `LogitsWarpers`, only applied with sampling strategies
 if generation_config.do_sample:
 # In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
 # better score (i.e. keep len(list(generation_config._eos_token_tensor)) + 1)
 if generation_config.num_beams > 1:
 if isinstance(generation_config._eos_token_tensor, list):
 min_tokens_to_keep = len(generation_config._eos_token_tensor) + 1
 elif isinstance(generation_config._eos_token_tensor, torch.Tensor):
 min_tokens_to_keep = generation_config._eos_token_tensor.shape[0] + 1
 else:
 min_tokens_to_keep = 2
 else:
 min_tokens_to_keep = 1
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
 # all samplers can be found in `generation_utils_samplers.py`
 if generation_config.temperature is not None and generation_config.temperature != 1.0:
 processors.append(TemperatureLogitsWarper(generation_config.temperature))
 if generation_config.top_k is not None and generation_config.top_k != 0:
 processors.append(
 TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep)
 )
 if generation_config.top_p is not None and generation_config.top_p < 1.0:
 processors.append(
 TopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=min_tokens_to_keep)
 )
 if generation_config.min_p is not None:
 # Applied after temperature scaling (see https://github.com/ggerganov/llama.cpp/pull/3841#issuecomment-2073826084)
 processors.append(
 MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep)
 )
 if generation_config.typical_p is not None and generation_config.typical_p < 1.0:
 processors.append(
 TypicalLogitsWarper(mass=generation_config.typical_p, min_tokens_to_keep=min_tokens_to_keep)
 )
 if generation_config.epsilon_cutoff is not None and 0.0 < generation_config.epsilon_cutoff < 1.0:
 processors.append(
 EpsilonLogitsWarper(
 epsilon=generation_config.epsilon_cutoff, min_tokens_to_keep=min_tokens_to_keep
 )
 )
 if generation_config.eta_cutoff is not None and 0.0 < generation_config.eta_cutoff < 1.0:
 processors.append(
 EtaLogitsWarper(
 epsilon=generation_config.eta_cutoff, min_tokens_to_keep=min_tokens_to_keep, device=device
 )
 )
# Watermarking should be after all logits processing is finished (see #34630)
 if generation_config.watermarking_config is not None:
 processors.append(
 generation_config.watermarking_config.construct_processor(
 self.config.get_text_config().vocab_size, device
 )
 )
# `LogitNormalization` should always be the last logit processor, when present
 if generation_config.renormalize_logits is True:
 processors.append(LogitNormalization())
 return processors
def _get_stopping_criteria(
 self,
 generation_config: GenerationConfig,
 stopping_criteria: Optional[StoppingCriteriaList],
 tokenizer: Optional["PreTrainedTokenizerBase"] = None,
 **kwargs,
 ) -> StoppingCriteriaList:
 criteria = StoppingCriteriaList()
 if generation_config.max_length is not None:
 max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
 criteria.append(
 MaxLengthCriteria(
 max_length=generation_config.max_length,
 max_position_embeddings=max_position_embeddings,
 )
 )
 if generation_config.max_time is not None:
 criteria.append(MaxTimeCriteria(max_time=generation_config.max_time))
 if generation_config.stop_strings is not None:
 if tokenizer is None:
 raise ValueError(
 "There are one or more stop strings, either in the arguments to `generate` or in the "
 "model's generation config, but we could not locate a tokenizer. When generating with "
 "stop strings, you must pass the model's tokenizer to the `tokenizer` argument of `generate`."
 )
 criteria.append(StopStringCriteria(stop_strings=generation_config.stop_strings, tokenizer=tokenizer))
 if generation_config._eos_token_tensor is not None:
 criteria.append(EosTokenCriteria(eos_token_id=generation_config._eos_token_tensor))
 if (
 generation_config.is_assistant
 and generation_config.assistant_confidence_threshold is not None
 and generation_config.assistant_confidence_threshold > 0
 ):
 criteria.append(
 ConfidenceCriteria(assistant_confidence_threshold=generation_config.assistant_confidence_threshold)
 )
 criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
 return criteria
def _merge_criteria_processor_list(
 self,
 default_list: Union[LogitsProcessorList, StoppingCriteriaList],
 custom_list: Union[LogitsProcessorList, StoppingCriteriaList],
 ) -> Union[LogitsProcessorList, StoppingCriteriaList]:
 """
 Merge user-defined processors/criteria with the ones instantiated inside `generate`. In case the same
 processor/criteria is present on both lists, use the user-defined one.
(Note: up to v4.49.0, this function threw an exception is the same logit processor was found twice.)
 """
 if len(custom_list) == 0:
 return default_list
final_list = type(default_list)()
 for default in default_list:
 using_custom = False
 for custom in custom_list:
 if type(custom) is type(default):
 object_type = "stopping criteria" if isinstance(custom, StoppingCriteria) else "logits processor"
 logger.warning_once(
 f"A custom {object_type} of type {type(custom)} has been passed to `.generate()`, but it "
 f"was also created in `.generate()`, given its parameterization. The custom {type(custom)} "
 f"will take precedence. Please check the docstring of {type(custom)} to see related "
 "`.generate()` flags."
 )
 final_list.append(custom)
 using_custom = True
 break
 if not using_custom:
 final_list.append(default)
for custom in custom_list:
 if custom not in final_list:
 final_list.append(custom)
 return final_list
def compute_transition_scores(
 self,
 sequences: torch.Tensor,
 scores: tuple[torch.Tensor],
 beam_indices: Optional[torch.Tensor] = None,
 normalize_logits: bool = False,
 ) -> torch.Tensor:
 """
 Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was
 used). This is a convenient method to quickly obtain the scores of the selected tokens at generation time.
Parameters:
 sequences (`torch.LongTensor`):
 The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or
 shorter if all batches finished early due to the `eos_token_id`.
 scores (`tuple(torch.FloatTensor)`):
 Transition scores for each vocabulary token at each generation step. Beam transition scores consisting
 of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
 Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
 with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
 beam_indices (`torch.LongTensor`, *optional*):
 Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
 `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at
 generate-time.
 normalize_logits (`bool`, *optional*, defaults to `False`):
 Whether to normalize the logits (which, for legacy reasons, may be unnormalized).
Return:
 `torch.Tensor`: A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
 the transition scores (logits)
Examples:
```python
 >>> from transformers import GPT2Tokenizer, AutoModelForCausalLM
 >>> import numpy as np
>>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
 >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
 >>> tokenizer.pad_token_id = tokenizer.eos_token_id
 >>> inputs = tokenizer(["Today is"], return_tensors="pt")
>>> # Example 1: Print the scores for each token generated with Greedy Search
 >>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
 >>> transition_scores = model.compute_transition_scores(
 ... outputs.sequences, outputs.scores, normalize_logits=True
 ... )
 >>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
 >>> # encoder-decoder models, like BART or T5.
 >>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
 >>> generated_tokens = outputs.sequences[:, input_length:]
 >>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
 ... # | token | token string | log probability | probability
 ... print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
 | 262 | the | -1.414 | 24.33%
 | 1110 | day | -2.609 | 7.36%
 | 618 | when | -2.010 | 13.40%
 | 356 | we | -1.859 | 15.58%
 | 460 | can | -2.508 | 8.14%
>>> # Example 2: Reconstruct the sequence scores from Beam Search
 >>> outputs = model.generate(
 ... **inputs,
 ... max_new_tokens=5,
 ... num_beams=4,
 ... num_return_sequences=4,
 ... return_dict_in_generate=True,
 ... output_scores=True,
 ... )
 >>> transition_scores = model.compute_transition_scores(
 ... outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
 ... )
 >>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
 >>> # Tip 1: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
 >>> # use case, you might want to recompute it with `normalize_logits=True`.
 >>> # Tip 2: the output length does NOT include the input length
 >>> output_length = np.sum(transition_scores.numpy() < 0, axis=1)
 >>> length_penalty = model.generation_config.length_penalty
 >>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
 >>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
 True
 ```"""
 # 1. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent
 # to a beam search approach were the first (and only) beam is always selected
 if beam_indices is None:
 beam_indices = torch.arange(scores[0].shape[0]).view(-1, 1).to(sequences.device)
 beam_indices = beam_indices.expand(-1, len(scores))
# 2. reshape scores as [batch_size*vocab_size, # generation steps] with # generation steps being
 # seq_len - input_length
 scores = torch.stack(scores).reshape(len(scores), -1).transpose(0, 1)
# 3. Optionally normalize the logits (across the vocab dimension)
 if normalize_logits:
 scores = scores.reshape(-1, self.config.get_text_config().vocab_size, scores.shape[-1])
 scores = torch.nn.functional.log_softmax(scores, dim=1)
 scores = scores.reshape(-1, scores.shape[-1])
# 4. cut beam_indices to longest beam length
 beam_indices_mask = beam_indices < 0
 max_beam_length = (1 - beam_indices_mask.long()).sum(-1).max()
 beam_indices = beam_indices.clone()[:, :max_beam_length]
 beam_indices_mask = beam_indices_mask[:, :max_beam_length]
# 5. Set indices of beams that finished early to 0; such indices will be masked correctly afterwards
 beam_indices[beam_indices_mask] = 0
# 6. multiply beam_indices with vocab size to gather correctly from scores
 beam_sequence_indices = beam_indices * self.config.get_text_config().vocab_size
# 7. Define which indices contributed to scores
 cut_idx = sequences.shape[-1] - max_beam_length
 indices = sequences[:, cut_idx:] + beam_sequence_indices
# 8. Compute scores
 transition_scores = scores.gather(0, indices)
# 9. Mask out transition_scores of beams that stopped early
 transition_scores[beam_indices_mask] = 0
return transition_scores
def _validate_assistant(self, assistant_model, tokenizer, assistant_tokenizer):
 if assistant_model is None:
 return
if self.config.is_encoder_decoder and not assistant_model.config.is_encoder_decoder:
 attributes_to_check = ["encoder_attention_heads", "encoder_ffn_dim", "encoder_layers"]
 attributes_to_check = [attr for attr in dir(assistant_model.config) if attr in attributes_to_check]
 are_equal = all(
 getattr(self.config, attr) == getattr(assistant_model.config, attr) for attr in attributes_to_check
 )
 if not are_equal:
 raise ValueError(
 "The main model and the assistant don't have compatible encoder-dependent input shapes. "
 "Ensure you load the assistant with the correct encoder-decoder class, e.g. `AutoModelForSpeechSeq2Seq` for Whisper."
 )
doc_reference = (
 "(see https://huggingface.co/docs/transformers/en/generation_strategies#universal-assisted-decoding)"
 )
 if self.config.get_text_config().vocab_size == assistant_model.config.get_text_config().vocab_size:
 if assistant_tokenizer is not None:
 raise ValueError(
 f"`assistant_tokenizer` is not required when the main and assistant models use the same tokenizer. Please omit `assistant_tokenizer` from `generate()` {doc_reference}."
 )
 else:
 if tokenizer is None or assistant_tokenizer is None:
 raise ValueError(
 f"The main and assistant moedels have different tokenizers. Please provide `tokenizer` and `assistant_tokenizer` to `generate()` {doc_reference}."
 )
def _validate_model_kwargs(self, model_kwargs: dict[str, Any]):
 """Validates model kwargs for generation. Generate argument typos will also be caught here."""
 # If a `Cache` instance is passed, checks whether the model is compatible with it
 if isinstance(model_kwargs.get("past_key_values", None), Cache) and not self._supports_cache_class:
 raise ValueError(
 f"{self.__class__.__name__} does not support an instance of `Cache` as `past_key_values`. Please "
 "check the model documentation for supported cache formats."
 )
# Excludes arguments that are handled before calling any model function
 if self.config.is_encoder_decoder:
 for key in ["decoder_input_ids"]:
 model_kwargs.pop(key, None)
unused_model_args = []
 model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
 # `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
 # `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
 if "kwargs" in model_args or "model_kwargs" in model_args:
 model_args |= set(inspect.signature(self.forward).parameters)
# Encoder-Decoder models may also need Encoder arguments from `model_kwargs`
 if self.config.is_encoder_decoder:
 base_model = getattr(self, self.base_model_prefix, None)
# allow encoder kwargs
 encoder = getattr(self, "encoder", None)
 # `MusicgenForConditionalGeneration` has `text_encoder` and `audio_encoder`.
 # Also, it has `base_model_prefix = "encoder_decoder"` but there is no `self.encoder_decoder`
 # TODO: A better way to handle this.
 if encoder is None and base_model is not None:
 encoder = getattr(base_model, "encoder", None)
if encoder is not None:
 encoder_model_args = set(inspect.signature(encoder.forward).parameters)
 model_args |= encoder_model_args
# allow decoder kwargs
 decoder = getattr(self, "decoder", None)
 if decoder is None and base_model is not None:
 decoder = getattr(base_model, "decoder", None)
if decoder is not None:
 decoder_model_args = set(inspect.signature(decoder.forward).parameters)
 model_args |= {f"decoder_{x}" for x in decoder_model_args}
for key, value in model_kwargs.items():
 if value is not None and key not in model_args:
 unused_model_args.append(key)
if unused_model_args:
 raise ValueError(
 f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
 " generate arguments will also show up in this list)"
 )
def _validate_generated_length(self, generation_config, input_ids_length, has_default_max_length):
 """Performs validation related to the resulting generated length"""
 # 1. Max length warnings related to poor parameterization
 if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
 # 20 is the default max_length of the generation config
 warnings.warn(
 f"Using the model-agnostic default `max_length` (={generation_config.max_length}) to control the "
 "generation length. We recommend setting `max_new_tokens` to control the maximum length of the "
 "generation.",
 UserWarning,
 )
 if input_ids_length >= generation_config.max_length:
 input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
 raise ValueError(
 f"Input length of {input_ids_string} is {input_ids_length}, but `max_length` is set to"
 f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
 " increasing `max_length` or, better yet, setting `max_new_tokens`."
 )
# 2. Min length warnings due to unfeasible parameter combinations
 min_length_error_suffix = (
 " Generation will stop at the defined maximum length. You should decrease the minimum length and/or "
 "increase the maximum length."
 )
 if has_default_max_length:
 min_length_error_suffix += (
 f" Note that `max_length` is set to {generation_config.max_length}, its default value."
 )
 if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
 warnings.warn(
 f"Unfeasible length constraints: `min_length` ({generation_config.min_length}) is larger than"
 f" the maximum possible length ({generation_config.max_length})." + min_length_error_suffix,
 UserWarning,
 )
 if generation_config.min_new_tokens is not None:
 min_length = generation_config.min_new_tokens + input_ids_length
 if min_length > generation_config.max_length:
 warnings.warn(
 f"Unfeasible length constraints: `min_new_tokens` ({generation_config.min_new_tokens}), when "
 f"added to the prompt length ({input_ids_length}), is larger than"
 f" the maximum possible length ({generation_config.max_length})." + min_length_error_suffix,
 UserWarning,
 )
def _prepare_generated_length(
 self,
 generation_config,
 has_default_max_length,
 has_default_min_length,
 model_input_name,
 input_ids_length,
 inputs_tensor,
 ):
 """Prepared max and min length in generation configs to avoid clashes between similar attributes"""
if generation_config.max_new_tokens is not None:
 if not has_default_max_length and generation_config.max_length is not None:
 logger.warning(
 f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
 f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
 "Please refer to the documentation for more information. "
 "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
 )
 generation_config.max_length = generation_config.max_new_tokens + input_ids_length
# if both `inputs_embeds` and `input_ids` are passed, we do not correct the length
 # otherwise we need total length [inputs-embeds-len + new-tokens-len] to not go beyond indicated `max_length``
 elif (
 model_input_name == "inputs_embeds"
 and input_ids_length != inputs_tensor.shape[1]
 and not self.config.is_encoder_decoder
 ):
 generation_config.max_length -= inputs_tensor.shape[1]
 elif has_default_max_length: # by default let's always generate 20 new tokens
 if generation_config.max_length == GenerationConfig().max_length:
 generation_config.max_length = generation_config.max_length + input_ids_length
 max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
 if max_position_embeddings is not None:
 generation_config.max_length = min(generation_config.max_length, max_position_embeddings)
# same for min length
 if generation_config.min_new_tokens is not None:
 if not has_default_min_length:
 logger.warning(
 f"Both `min_new_tokens` (={generation_config.min_new_tokens}) and `min_length`(="
 f"{generation_config.min_length}) seem to have been set. `min_new_tokens` will take precedence. "
 "Please refer to the documentation for more information. "
 "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
 )
 generation_config.min_length = generation_config.min_new_tokens + input_ids_length
elif (
 model_input_name == "inputs_embeds"
 and input_ids_length != inputs_tensor.shape[1]
 and not self.config.is_encoder_decoder
 ):
 generation_config.min_length = max(generation_config.min_length - inputs_tensor.shape[1], 0)
return generation_config
def _prepare_generation_config(
 self, generation_config: Optional[GenerationConfig], use_model_defaults: Optional[bool] = None, **kwargs: dict
 ) -> tuple[GenerationConfig, dict]:
 """
 Prepares the base generation config, then applies any generation configuration options from kwargs. This
 function handles retrocompatibility with respect to configuration files.
 """
 # parameterization priority:
 # kwargs > non-global default values in `generation_config` > `model.generation_config` > GenerationConfig()
 # TODO (joao): per-model generation config classes.
using_model_generation_config = False
 if generation_config is None:
 # legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
 # the following conditions must be met
 # 1) the generation config must have been created from the model config (`_from_model_config` field);
 # 2) the generation config must have seen no modification since its creation (the hash is the same);
 # 3) there are non-default generation parameters in the model config.
 # 4) the user must have set new generation parameters in the model config.
 if (
 self.generation_config._from_model_config # 1)
 and self.generation_config._original_object_hash == hash(self.generation_config) # 2)
 and len(self.config._get_non_default_generation_parameters()) > 0 # 3)
 ):
 new_generation_config = GenerationConfig.from_model_config(self.config)
 if new_generation_config != self.generation_config: # 4)
 warnings.warn(
 "You have modified the pretrained model configuration to control generation. This is a"
 " deprecated strategy to control generation and will be removed in v5."
 " Please use and modify the model generation configuration (see"
 " https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )",
 UserWarning,
 )
 self.generation_config = new_generation_config
generation_config = self.generation_config
 using_model_generation_config = True
# `torch.export.export` usually raises an exception if it is called
 # with ``strict=True``. deepcopy can only be processed if ``strict=False``.
 generation_config = copy.deepcopy(generation_config)
if not using_model_generation_config:
 # If `generation_config` is provided:
 # - `use_model_defaults`: let's fallback ALL default values to the model's generation config
 # - otherwise: legacy behavior, let's just make sure we have the tokens defined
 model_base_version = version.parse(version.parse(self.generation_config.transformers_version).base_version)
 if use_model_defaults is True or (
 use_model_defaults is None and model_base_version >= version.parse("4.50.0")
 ):
 modified_values = {}
 global_default_generation_config = GenerationConfig()
 model_generation_config = self.generation_config
 # we iterate over the model's generation config: it may hold custom keys, which we'll want to copy
 for key, model_gen_config_value in model_generation_config.__dict__.items():
 if key.startswith("_") or key == "transformers_version": # metadata
 continue
 global_default_value = getattr(global_default_generation_config, key, None)
 custom_gen_config_value = getattr(generation_config, key, None)
 if (
 custom_gen_config_value == global_default_value
 and model_gen_config_value != global_default_value
 ):
 modified_values[key] = model_gen_config_value
 setattr(generation_config, key, model_gen_config_value)
 if use_model_defaults is None and len(modified_values) > 0:
 logger.warning_once(
 f"`generation_config` default values have been modified to match model-specific defaults: "
 f"{modified_values}. If this is not desired, please set these values explicitly."
 )
 else:
 if generation_config.bos_token_id is None:
 generation_config.bos_token_id = self.generation_config.bos_token_id
 if generation_config.eos_token_id is None:
 generation_config.eos_token_id = self.generation_config.eos_token_id
 if generation_config.pad_token_id is None:
 generation_config.pad_token_id = self.generation_config.pad_token_id
 if generation_config.decoder_start_token_id is None:
 generation_config.decoder_start_token_id = self.generation_config.decoder_start_token_id
# Finally, apply any passed kwargs
 model_kwargs = generation_config.update(**kwargs)
return generation_config, model_kwargs
def _get_initial_cache_position(self, seq_length, device, model_kwargs):
 """Calculates `cache_position` for the pre-fill stage based on `input_ids` and optionally past length"""
 # `torch.compile`-friendly `torch.arange` from a shape -- the lines below are equivalent to `torch.arange`
 if "cache_position" in model_kwargs and model_kwargs["cache_position"]:
 return model_kwargs
 if "inputs_embeds" in model_kwargs and not self.config.is_encoder_decoder:
 cache_position = torch.ones_like(model_kwargs["inputs_embeds"][0, :, 0], dtype=torch.int64).cumsum(0) - 1
 elif "decoder_inputs_embeds" in model_kwargs and self.config.is_encoder_decoder:
 cache_position = (
 torch.ones_like(model_kwargs["decoder_inputs_embeds"][0, :, 0], dtype=torch.int64).cumsum(0) - 1
 )
 else:
 cache_position = torch.ones(seq_length, dtype=torch.int64, device=device).cumsum(0) - 1
past_length = 0
 if model_kwargs.get("past_key_values") is not None:
 cache = model_kwargs["past_key_values"]
 past_length = 0
 if not isinstance(cache, Cache):
 past_length = cache[0][0].shape[2]
 elif hasattr(cache, "get_seq_length") and cache.get_seq_length() is not None:
 past_length = cache.get_seq_length()
cache_position = cache_position[past_length:]
model_kwargs["cache_position"] = cache_position
 return model_kwargs
def _get_layer_device_map_for_cache_init(self) -> Optional[dict[int, Union[str, int]]]:
 """
 Returns the device map for each decoder layer, to allocate the cache on the right device.
 Inspired from `dispatch_model` in accelerate.
 """
 execution_device_map = None
if hasattr(self, "hf_device_map"):
 if set(self.hf_device_map.values()) == {"cpu"} or set(self.hf_device_map.values()) == {"cpu", "disk"}:
 main_device = "cpu"
 else:
 main_device = [d for d in self.hf_device_map.values() if d not in ["cpu", "disk"]][0]
 execution_device_map = {
 name: main_device if device in ["cpu", "disk"] else device
 for name, device in self.hf_device_map.items()
 }
# No `execution_device_map` -> rely on `self.device` to allocate the cache
 if execution_device_map is None:
 return None
# Single device for all layers
 num_hidden_layers = self.config.get_text_config().num_hidden_layers
 if len(execution_device_map) == 1 and "" in execution_device_map:
 return dict.fromkeys(range(num_hidden_layers), execution_device_map[""])
# Multiple devices in `execution_device_map` -> we need to map decoder layers to the correct device.
 layer_device_map = {}
 # Case 1: The model has a `get_decoder` method, we can use it to find the decoder name.
 if hasattr(self, "get_decoder"):
 decoder_name = None
 for name, module in self.named_modules():
 if module is self.get_decoder():
 decoder_name = name
 break
 if decoder_name is None:
 raise RuntimeError(
 "`model.get_decoder()` is not returning a named module of the model. This is unexpected, please "
 "open an issue on GitHub."
 )
decoder_mapped_modules = [
 module_name for module_name in execution_device_map.keys() if decoder_name in module_name
 ]
 # The decoder name may be present in `execution_device_map` in two forms:
 # a) each layer has a device mapping
 if len(decoder_mapped_modules) >= num_hidden_layers:
 for idx in range(num_hidden_layers):
 for module_name in decoder_mapped_modules:
 if f".{idx}." in f"{module_name}.":
 layer_device_map[idx] = execution_device_map[module_name]
 break
# b) the whole module is mapped to a single device. If the decoder name is NOT present in the device map,
 # then the mapping is done in a parent module
 else:
 while True:
 if decoder_name in execution_device_map:
 layer_device_map = dict.fromkeys(range(num_hidden_layers), execution_device_map[decoder_name])
 break
 elif "." in decoder_name:
 decoder_name = decoder_name.rsplit(".", 1)[0] # gets the name of the parent module
 else:
 raise RuntimeError(f"Decoder name {decoder_name} not found in execution device map")
# Case 2: Legacy code path: assume the decoder layers are named as `(...).X` (X being the layer index)
 else:
 for layer in execution_device_map:
 for idx in range(num_hidden_layers):
 if f".{idx}." in f"{layer}.":
 layer_device_map[idx] = execution_device_map[layer]
 break
for idx in range(num_hidden_layers):
 if idx not in layer_device_map:
 raise RuntimeError(f"layer {idx} has not been mapped to a device.")
 return layer_device_map
def _get_cache(
 self, cache_implementation: str, batch_size: int, max_cache_len: int, device: torch.device, model_kwargs
 ) -> Cache:
 """
 Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
 new `generate` call requires a larger cache or uses a different batch size.
Returns the resulting cache object.
 """
 if cache_implementation == "hybrid" and "llama4" in getattr(self.config, "model_type", ""):
 cache_implementation = "hybrid_chunked"
cache_cls: Cache = NEED_SETUP_CACHE_CLASSES_MAPPING[cache_implementation]
 requires_cross_attention_cache = (
 self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
 )
if hasattr(self, "_cache"):
 cache_to_check = self._cache.self_attention_cache if requires_cross_attention_cache else self._cache
if cache_implementation == "sliding_window":
 max_cache_len = min(self.config.sliding_window, max_cache_len)
need_new_cache = (
 not hasattr(self, "_cache")
 or (not isinstance(cache_to_check, cache_cls))
 or cache_to_check.max_batch_size != batch_size
 or isinstance(
 cache_to_check, (HybridChunkedCache, OffloadedHybridCache)
 ) # due to internal slicing, we always re-init
 )
 if cache_implementation != "mamba":
 need_new_cache = need_new_cache or cache_to_check.max_cache_len < max_cache_len
if requires_cross_attention_cache and hasattr(self, "_cache"):
 need_new_cache = (
 need_new_cache
 or self._cache.cross_attention_cache.max_cache_len != model_kwargs["encoder_outputs"][0].shape[1]
 )
if need_new_cache:
 if hasattr(self.config, "_pre_quantization_dtype"):
 cache_dtype = self.config._pre_quantization_dtype
 else:
 cache_dtype = self.dtype
layer_device_map = self._get_layer_device_map_for_cache_init()
 cache_kwargs = {
 "config": self.config.get_text_config(),
 "max_batch_size": batch_size,
 "max_cache_len": max_cache_len,
 "dtype": cache_dtype,
 "device": device,
 "layer_device_map": layer_device_map,
 }
 self._cache = cache_cls(**cache_kwargs)
 if requires_cross_attention_cache:
 encoder_kwargs = cache_kwargs.copy()
 encoder_kwargs["max_cache_len"] = model_kwargs["encoder_outputs"][0].shape[1]
 self._cache = EncoderDecoderCache(self._cache, cache_cls(**encoder_kwargs))
 else:
 self._cache.reset()
 return self._cache
def _supports_default_dynamic_cache(self) -> bool:
 """
 Return `True` if current model can use a `DynamicCache` instance when initializing the `past_key_values`.
 This is mostly the same as `_supports_cache_class` attribute, but add exception for `Jamba` model which
 uses its own `HybridMambaAttentionDynamicCache` and do not need to initialize the Cache in advance in
 order to save memory (because no back and forth `to_legacy_cache` and `from_legacy_cache` will be performed
 for `HybridMambaAttentionDynamicCache`).
 """
 return (
 self._supports_cache_class
 and "jamba" not in self.__class__.__name__.lower()
 and "zamba" not in self.__class__.__name__.lower()
 and "bamba" not in self.__class__.__name__.lower()
 and "minimax" not in self.__class__.__name__.lower()
 )
def _prepare_cache_for_generation(
 self,
 generation_config: GenerationConfig,
 model_kwargs: dict,
 assistant_model: "PreTrainedModel",
 batch_size: int,
 max_cache_length: int,
 device: torch.device,
 ) -> bool:
 """
 Prepares the cache for generation (if applicable), given `generate`'s parameterization. If a cache is
 instantiated, writes it to `model_kwargs`, under the name expected by the model.
 """
is_hybrid_cache = any(class_name in self.__class__.__name__.lower() for class_name in ["mamba", "falconh1"])
 cache_name = "past_key_values" if not is_hybrid_cache else "cache_params"
requires_cross_attention_cache = (
 self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
 )
# Quick escape route 1: if the user specifies a cache, we only need to:
 # a) check for conflicting `generate` arguments
 # b) convert to the new cache format (if the user passes a legacy cache and model supports it)
 user_defined_cache = model_kwargs.get(cache_name)
 if user_defined_cache is not None:
 if generation_config.cache_implementation is not None:
 raise ValueError(
 f"Passing both `cache_implementation` (used to initialize certain caches) and `{cache_name}` (a "
 "Cache object) is unsupported. Please use only one of the two."
 )
 if isinstance(user_defined_cache, tuple) and self._supports_default_dynamic_cache():
 model_kwargs[cache_name] = (
 DynamicCache.from_legacy_cache(user_defined_cache)
 if not requires_cross_attention_cache
 else EncoderDecoderCache.from_legacy_cache(user_defined_cache)
 )
 return
# Quick escape route 2: if the user specifies no cache is to be used. (conflicting arguments are handled in
 # `generation_config.validate()`)
 if generation_config.use_cache is False:
 return
# Quick escape route 3: model that only supports legacy caches = nothing to prepare
 if not self._supports_default_dynamic_cache():
 if generation_config.cache_implementation is not None:
 warnings.warn(
 "This model does not support `Cache` instances, it only supports the legacy cache format (tuple "
 f"of tuples). `cache_implementation` (set to {generation_config.cache_implementation}) will be "
 "ignored.",
 UserWarning,
 )
 return
# Otherwise we NEED to prepare a cache, based on `generation_config.cache_implementation`
# TODO(joao): support static caches in assisted generation. assisted generation needs to roll back caches,
 # which is only supported in dynamic caches atm
 if assistant_model is not None and generation_config.cache_implementation is not None:
 logger.warning_once(
 "An assistant model is provided, using a dynamic cache instead of a cache of type="
 f"'{generation_config.cache_implementation}'."
 )
 generation_config.cache_implementation = None
generation_config.cache_implementation = generation_config.cache_implementation or getattr(
 self.config.get_text_config(), "cache_implementation", None
 )
 if generation_config.cache_implementation is not None:
 if generation_config.cache_implementation in NEED_SETUP_CACHE_CLASSES_MAPPING:
 if generation_config.cache_implementation == "static" and not self._supports_static_cache:
 raise ValueError(
 "This model does not support `cache_implementation='static'`. Please check the following "
 "issue: https://github.com/huggingface/transformers/issues/28981"
 )
 model_kwargs[cache_name] = self._get_cache(
 cache_implementation=generation_config.cache_implementation,
 batch_size=max(generation_config.num_beams, generation_config.num_return_sequences) * batch_size,
 max_cache_len=max_cache_length,
 device=device,
 model_kwargs=model_kwargs,
 )
 elif generation_config.cache_implementation == "quantized":
 if not self._supports_quantized_cache:
 raise ValueError(
 "This model does not support the quantized cache. If you want your model to support quantized "
 "cache, please open an issue and tag @zucchini-nlp."
 )
cache_config = (
 generation_config.cache_config
 if generation_config.cache_config is not None
 else QuantizedCacheConfig()
 )
 cache_class = QUANT_BACKEND_CLASSES_MAPPING[cache_config.backend]
if cache_config.backend == "quanto" and not is_optimum_quanto_available():
 raise ImportError(
 "You need to install optimum-quanto in order to use KV cache quantization with optimum-quanto backend. "
 "Please install it via with `pip install optimum-quanto`"
 )
 elif cache_config.backend == "HQQ" and not is_hqq_available():
 raise ImportError(
 "You need to install `HQQ` in order to use KV cache quantization with HQQ backend. "
 "Please install it via with `pip install hqq`"
 )
model_kwargs[cache_name] = cache_class(cache_config)
 elif generation_config.cache_implementation == "offloaded":
 model_kwargs[cache_name] = OffloadedCache()
 elif generation_config.cache_implementation == "dynamic":
 model_kwargs[cache_name] = DynamicCache()
# Use DynamicCache() instance by default. This will avoid back and forth from legacy format that
 # keeps copying the cache thus using much more memory
 else:
 model_kwargs[cache_name] = (
 DynamicCache()
 if not requires_cross_attention_cache
 else EncoderDecoderCache(DynamicCache(), DynamicCache())
 )
def _supports_logits_to_keep(self) -> bool:
 """
 Return True if the current model supports the keyword argument `logits_to_keep` in forward()
 to save memory. Checking it in this way allows to avoid using a new model attribute.
 """
 return "logits_to_keep" in set(inspect.signature(self.forward).parameters.keys())
def _prepare_special_tokens(
 self,
 generation_config: GenerationConfig,
 kwargs_has_attention_mask: Optional[bool] = None,
 device: Optional[Union[torch.device, str]] = None,
 ):
 """
 Prepares the special tokens for generation, overwriting the generation config with their processed versions
 converted to tensor.
Note that `generation_config` is changed in place and stops being serializable after this method is called.
 That is no problem if called within `generate` (`generation_config` is a local copy that doesn't leave the
 function). However, if called outside `generate`, consider creating a copy of `generation_config` first.
 """
# Convert special tokens to tensors
 def _tensor_or_none(token, device=None):
 if token is None:
 return token
device = device if device is not None else self.device
 if isinstance(token, torch.Tensor):
 return token.to(device)
 return torch.tensor(token, device=device, dtype=torch.long)
bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
 eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
 pad_token_tensor = _tensor_or_none(generation_config.pad_token_id, device=device)
 decoder_start_token_tensor = _tensor_or_none(generation_config.decoder_start_token_id, device=device)
# for BC we also try to get `decoder_start_token_id` or `bos_token_id` (#30892)
 if self.config.is_encoder_decoder:
 decoder_start_token_tensor = (
 decoder_start_token_tensor if decoder_start_token_tensor is not None else bos_token_tensor
 )
# We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
 if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
 eos_token_tensor = eos_token_tensor.unsqueeze(0)
# Set pad token if unset (and there are conditions to do so)
 if pad_token_tensor is None and eos_token_tensor is not None:
 if kwargs_has_attention_mask is not None and not kwargs_has_attention_mask:
 logger.warning(
 "The attention mask and the pad token id were not set. As a consequence, you may observe "
 "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
 )
 pad_token_tensor = eos_token_tensor[0]
 logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{pad_token_tensor} for open-end generation.")
# Sanity checks/warnings
 if self.config.is_encoder_decoder and decoder_start_token_tensor is None:
 raise ValueError(
 "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
 )
 if (
 eos_token_tensor is not None
 and isin_mps_friendly(elements=eos_token_tensor, test_elements=pad_token_tensor).any()
 ):
 if kwargs_has_attention_mask is not None and not kwargs_has_attention_mask:
 logger.warning_once(
 "The attention mask is not set and cannot be inferred from input because pad token is same as "
 "eos token. As a consequence, you may observe unexpected behavior. Please pass your input's "
 "`attention_mask` to obtain reliable results."
 )
 if eos_token_tensor is not None and (
 torch.is_floating_point(eos_token_tensor) or (eos_token_tensor < 0).any()
 ):
 logger.warning(
 f"`eos_token_id` should consist of positive integers, but is {eos_token_tensor}. Your generation "
 "will not stop until the maximum length is reached. Depending on other flags, it may even crash."
 )
# Update generation config with the updated special tokens tensors
 # NOTE: this must be written into a different attribute name than the one holding the original special tokens
 # (in their non-tensor form), in order to enable end-to-end compilation. See
 # https://pytorch.org/docs/stable/torch.compiler_cudagraph_trees.html#limitations
 generation_config._bos_token_tensor = bos_token_tensor
 generation_config._eos_token_tensor = eos_token_tensor
 generation_config._pad_token_tensor = pad_token_tensor
 generation_config._decoder_start_token_tensor = decoder_start_token_tensor
def _valid_auto_compile_criteria(self, model_kwargs: dict, generation_config: GenerationConfig) -> bool:
 """
 Determines whether to trigger auto-compilation of the model's forward pass at generation time.
 """
 # Override: honor `disable_compile` flag
 if generation_config.disable_compile:
 return False
# Base logic
 valid_hardware = self.device.type == "cuda" or bool(
 generation_config.compile_config is not None and generation_config.compile_config._compile_all_devices
 )
 using_compilable_cache = (
 isinstance(model_kwargs.get("past_key_values"), Cache) and model_kwargs["past_key_values"].is_compileable
 )
 can_compile = valid_hardware and using_compilable_cache and self._supports_static_cache
# Exception 1: Some quantization methods do not support compilation
 if getattr(self, "hf_quantizer", None) is not None:
 can_compile &= self.hf_quantizer.is_compileable
if hasattr(self, "hf_device_map"):
 all_model_devices = set(self.hf_device_map.values())
 # Exception 2: Don't compile if the model is using CPU offload (as of April 2025, this results in a crash)
 has_cpu_offload = "cpu" in all_model_devices and len(all_model_devices) > 1
 can_compile &= not has_cpu_offload
# Exception 3: Disk offload is not supported for compilation
 has_disk_offload = "disk" in all_model_devices
 can_compile &= not has_disk_offload
# Finally: if the user has manually specified compilation options, but compilation is not possible, let's warn
 # them
 if generation_config.compile_config is not None and not can_compile:
 logger.warning_once(
 "You have set `compile_config`, but we are unable to meet the criteria for compilation. Compilation "
 "will be skipped."
 )
return can_compile
@torch.no_grad()
 def generate(
 self,
 inputs: Optional[torch.Tensor] = None,
 generation_config: Optional[GenerationConfig] = None,
 logits_processor: Optional[LogitsProcessorList] = None,
 stopping_criteria: Optional[StoppingCriteriaList] = None,
 prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], list[int]]] = None,
 synced_gpus: Optional[bool] = None,
 assistant_model: Optional["PreTrainedModel"] = None,
 streamer: Optional["BaseStreamer"] = None,
 negative_prompt_ids: Optional[torch.Tensor] = None,
 negative_prompt_attention_mask: Optional[torch.Tensor] = None,
 use_model_defaults: Optional[bool] = None,
 custom_generate: Optional[str] = None,
 **kwargs,
 ) -> Union[GenerateOutput, torch.LongTensor]:
 r"""
Generates sequences of token ids for models with a language modeling head.
<Tip warning={true}>
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
 model's default generation configuration. You can override any `generation_config` by passing the corresponding
 parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
For an overview of generation strategies and code examples, check out the [following
 guide](../generation_strategies).
</Tip>
Parameters:
 inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
 The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
 method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
 should be in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
 `input_ids`, `input_values`, `input_features`, or `pixel_values`.
 generation_config ([`~generation.GenerationConfig`], *optional*):
 The generation configuration to be used as base parametrization for the generation call. `**kwargs`
 passed to generate matching the attributes of `generation_config` will override them. If
 `generation_config` is not provided, the default will be used, which has the following loading
 priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
 configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
 default values, whose documentation should be checked to parameterize generation.
 logits_processor (`LogitsProcessorList`, *optional*):
 Custom logits processors that complement the default logits processors built from arguments and
 generation config. If a logit processor is passed that is already created with the arguments or a
 generation config an error is thrown. This feature is intended for advanced users.
 stopping_criteria (`StoppingCriteriaList`, *optional*):
 Custom stopping criteria that complements the default stopping criteria built from arguments and a
 generation config. If a stopping criteria is passed that is already created with the arguments or a
 generation config an error is thrown. If your stopping criteria depends on the `scores` input, make
 sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is
 intended for advanced users.
 prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*):
 If provided, this function constraints the beam search to allowed tokens only at each step. If not
 provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
 `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
 on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
 for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
 Retrieval](https://huggingface.co/papers/2010.00904).
 synced_gpus (`bool`, *optional*):
 Whether to continue running the while loop until max_length. Unless overridden, this flag will be set
 to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid
 deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to `False`.
 assistant_model (`PreTrainedModel`, *optional*):
 An assistant model that can be used to accelerate generation. The assistant model must have the exact
 same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistant model
 is much faster than running generation with the model you're calling generate from. As such, the
 assistant model should be much smaller.
 streamer (`BaseStreamer`, *optional*):
 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
 negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
 The negative prompt needed for some processors such as CFG. The batch size must match the input batch
 size. This is an experimental feature, subject to breaking API changes in future versions.
 negative_prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
 Attention_mask for `negative_prompt_ids`.
 use_model_defaults (`bool`, *optional*):
 When it is `True`, unset parameters in `generation_config` will be set to the model-specific default
 generation configuration (`model.generation_config`), as opposed to the global defaults
 (`GenerationConfig()`). If unset, models saved starting from `v4.50` will consider this flag to be
 `True`.
 custom_generate (`str`, *optional*):
 A string containing the name of a huggingface.co repository. If provided, the custom `generate`
 function defined in that reposity's `custom_generate/generate.py` file will be executed instead of the
 standard `generate` method. Note that the logic is for generation is entirely defined in that
 repository, and the return type may be different from the standard `generate` method.
 kwargs (`dict[str, Any]`, *optional*):
 Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be
 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
 [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
 or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
 [`~utils.ModelOutput`] types are:
- [`~generation.GenerateDecoderOnlyOutput`],
 - [`~generation.GenerateBeamDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
 [`~utils.ModelOutput`] types are:
- [`~generation.GenerateEncoderDecoderOutput`],
 - [`~generation.GenerateBeamEncoderDecoderOutput`]
 """
 # 0. If requested, load an arbitrary generation recipe from the Hub and run it instead
 trust_remote_code = kwargs.pop("trust_remote_code", None)
 if custom_generate is not None:
 # Get all `generate` arguments in a single variable. Custom functions are responsible for handling them:
 # they receive the same inputs as `generate`, with `model` instead of `self` and excluding the arguments to
 # trigger the custom generation. They can access to methods from `GenerationMixin` through `model`.
 global_keys_to_exclude = {
 "self",
 "kwargs",
 "global_keys_to_exclude",
 "trust_remote_code",
 "custom_generate",
 }
 generate_arguments = {key: value for key, value in locals().items() if key not in global_keys_to_exclude}
 generate_arguments.update(kwargs)
custom_generate_function = self.load_custom_generate(
 custom_generate, trust_remote_code=trust_remote_code, **kwargs
 )
 return custom_generate_function(model=self, **generate_arguments)
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
 tokenizer = kwargs.pop("tokenizer", None) # Pull this out first, we only use it for stopping criteria
 assistant_tokenizer = kwargs.pop("assistant_tokenizer", None) # only used for assisted generation
generation_config, model_kwargs = self._prepare_generation_config(
 generation_config, use_model_defaults, **kwargs
 )
 self._validate_model_kwargs(model_kwargs.copy())
 self._validate_assistant(assistant_model, tokenizer, assistant_tokenizer)
# 2. Set generation parameters if not already defined
 if synced_gpus is None:
 synced_gpus = (is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)) and dist.get_world_size() > 1
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
 stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
 requires_attention_mask = "encoder_outputs" not in model_kwargs
 kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
# 3. Define model inputs
 inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
 inputs, generation_config.bos_token_id, model_kwargs
 )
 batch_size = inputs_tensor.shape[0]
device = inputs_tensor.device
 self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
# decoder-only models must use left-padding for batched generation.
 if not self.config.is_encoder_decoder:
 # If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
 # Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
 if (
 generation_config._pad_token_tensor is not None
 and batch_size > 1
 and len(inputs_tensor.shape) == 2
 and torch.sum(inputs_tensor[:, -1] == generation_config._pad_token_tensor) > 0
 ):
 logger.warning(
 "A decoder-only architecture is being used, but right-padding was detected! For correct "
 "generation results, please set `padding_side='left'` when initializing the tokenizer."
 )
# 4. Define other model kwargs
 # decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
 # generating the first new token or not, and we only want to use the embeddings for the first new token)
 if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
 generation_config.use_cache = True
if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
 model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
 inputs_tensor, generation_config, model_kwargs
 )
 elif kwargs_has_attention_mask:
 # TODO (joao): generalize this check with other types of inputs
 if model_input_name == "input_ids" and len(model_kwargs["attention_mask"].shape) > 2:
 raise ValueError("`attention_mask` passed to `generate` must be 2D.")
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
 # if model is encoder decoder encoder_outputs are created and added to `model_kwargs`
 model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
 inputs_tensor, model_kwargs, model_input_name, generation_config
 )
# 5. Prepare `input_ids` which will be used for auto-regressive generation
 if self.config.is_encoder_decoder:
 input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
 batch_size=batch_size,
 model_input_name=model_input_name,
 model_kwargs=model_kwargs,
 decoder_start_token_id=generation_config._decoder_start_token_tensor,
 device=inputs_tensor.device,
 )
 else:
 input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
if generation_config.token_healing:
 input_ids = self.heal_tokens(input_ids, tokenizer)
if streamer is not None:
 streamer.put(input_ids.cpu())
# 6. Prepare `max_length` depending on other stopping criteria.
 input_ids_length = input_ids.shape[1]
 has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
 has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
 generation_config = self._prepare_generated_length(
 generation_config=generation_config,
 has_default_max_length=has_default_max_length,
 has_default_min_length=has_default_min_length,
 model_input_name=model_input_name,
 inputs_tensor=inputs_tensor,
 input_ids_length=input_ids_length,
 )
# If the model supports `logits_to_keep` in forward(), set it to 1 to avoid computing the whole
 # logit matrix. This can save a lot of memory during the first forward pass. Note that assisted decoding
 # dynamically overrides this value as it can need more than the last token logits
 if self._supports_logits_to_keep() and "logits_to_keep" not in model_kwargs:
 model_kwargs["logits_to_keep"] = 1
self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
# 7. Prepare the cache.
 # - `model_kwargs` may be updated in place with a cache as defined by the parameters in `generation_config`.
 # - different models have a different cache name expected by the model (default = "past_key_values")
 # - `max_length`, prepared above, is used to determine the maximum cache length
 max_cache_length = generation_config.max_length - 1
 if (
 inputs_tensor.shape[1] != input_ids_length
 and model_input_name == "inputs_embeds"
 and not self.config.is_encoder_decoder
 ):
 max_cache_length += inputs_tensor.shape[1]
 self._prepare_cache_for_generation(
 generation_config, model_kwargs, assistant_model, batch_size, max_cache_length, device
 )
# 8. determine generation mode
 generation_mode = generation_config.get_generation_mode(assistant_model)
if streamer is not None and (generation_config.num_beams > 1):
 raise ValueError(
 "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
 )
if self.device.type != input_ids.device.type:
 warnings.warn(
 "You are calling .generate() with the `input_ids` being on a device type different"
 f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
 f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
 " Please make sure that you have put `input_ids` to the"
 f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
 " running `.generate()`.",
 UserWarning,
 )
# 9. prepare logits processors and stopping criteria
 prepared_logits_processor = self._get_logits_processor(
 generation_config=generation_config,
 input_ids_seq_length=input_ids_length,
 encoder_input_ids=inputs_tensor,
 prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
 logits_processor=logits_processor,
 device=inputs_tensor.device,
 model_kwargs=model_kwargs,
 negative_prompt_ids=negative_prompt_ids,
 negative_prompt_attention_mask=negative_prompt_attention_mask,
 )
 prepared_stopping_criteria = self._get_stopping_criteria(
 generation_config=generation_config, stopping_criteria=stopping_criteria, tokenizer=tokenizer, **kwargs
 )
# Set model_kwargs `use_cache` so we can use it later in forward runs
 model_kwargs["use_cache"] = generation_config.use_cache
# 10. go into different generation modes
 if generation_mode == GenerationMode.ASSISTED_GENERATION:
 if generation_config.num_return_sequences > 1:
 raise ValueError(
 "num_return_sequences has to be 1 when doing assisted generate, "
 f"but is {generation_config.num_return_sequences}."
 )
 if batch_size > 1:
 raise ValueError("assisted generate is only supported for batch_size = 1")
 if not model_kwargs["use_cache"]:
 raise ValueError("assisted generate requires `use_cache=True`")
 if generation_config.cache_implementation in ["static", "hybrid", "sliding_window"]:
 raise ValueError("assisted generate is not supported with Static cache classes`")
 if self._is_stateful:
 # In assisted generation we need the ability to confirm whether the model would pick certain tokens,
 # which is not possible with stateful models (they can't reset to a previous subset of generated text)
 raise ValueError(
 f"assisted generation is not supported with stateful models, such as {self.__class__.__name__}"
 )
# 11. Get the candidate generator, given the parameterization
 candidate_generator = self._get_candidate_generator(
 generation_config=generation_config,
 input_ids=input_ids,
 inputs_tensor=inputs_tensor,
 assistant_model=assistant_model,
 logits_processor=logits_processor,
 target_tokenizer=tokenizer,
 assistant_tokenizer=assistant_tokenizer,
 model_kwargs=model_kwargs,
 )
# 12. run assisted generate
 result = self._assisted_decoding(
 input_ids,
 candidate_generator=candidate_generator,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 streamer=streamer,
 **model_kwargs,
 )
 elif generation_mode == GenerationMode.DOLA_GENERATION:
 if not trust_remote_code:
 logger.warning_once(
 "DoLa Decoding is scheduled to be moved to a `custom_generate` repository in v4.55.0. "
 "To prevent loss of backward compatibility, add `trust_remote_code=True` to your `generate` call."
 )
 if self._is_stateful:
 # DoLa decoding was not designed for stateful models, and would require some changes
 raise ValueError(
 f"dola decoding is not supported with stateful models, such as {self.__class__.__name__}"
 )
 result = self._dola_decoding(
 input_ids,
 dola_layers=generation_config.dola_layers,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 streamer=streamer,
 **model_kwargs,
 )
elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
 if not trust_remote_code:
 logger.warning_once(
 "Contrastive Search is scheduled to be moved to a `custom_generate` repository in v4.55.0. "
 "To prevent loss of backward compatibility, add `trust_remote_code=True` to your `generate` call."
 )
 if not model_kwargs["use_cache"]:
 raise ValueError("Contrastive search requires `use_cache=True`")
 if self._is_stateful:
 # Just like assisted generation, we need to be able to rollback to a previous state (see comment above)
 raise ValueError(
 f"contrastive search is not supported with stateful models, such as {self.__class__.__name__}"
 )
result = self._contrastive_search(
 input_ids,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 streamer=streamer,
 **model_kwargs,
 )
elif generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
 # 11. expand input_ids with `num_return_sequences` additional sequences per batch
 input_ids, model_kwargs = self._expand_inputs_for_generation(
 input_ids=input_ids,
 expand_size=generation_config.num_return_sequences,
 is_encoder_decoder=self.config.is_encoder_decoder,
 **model_kwargs,
 )
# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)
 result = self._sample(
 input_ids,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 streamer=streamer,
 **model_kwargs,
 )
elif generation_mode in (GenerationMode.BEAM_SAMPLE, GenerationMode.BEAM_SEARCH):
 # 11. interleave input_ids with `num_beams` additional sequences per batch
 input_ids, model_kwargs = self._expand_inputs_for_generation(
 input_ids=input_ids,
 expand_size=generation_config.num_beams,
 is_encoder_decoder=self.config.is_encoder_decoder,
 **model_kwargs,
 )
 # 12. run beam sample
 result = self._beam_search(
 input_ids,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 **model_kwargs,
 )
elif generation_mode == GenerationMode.GROUP_BEAM_SEARCH:
 logger.warning_once(
 "Group Beam Search is scheduled to be moved to a `custom_generate` repository in v4.55.0. "
 "To prevent loss of backward compatibility, add `trust_remote_code=True` to your `generate` call."
 )
 # 11. prepare beam search scorer
 beam_scorer = BeamSearchScorer(
 batch_size=batch_size,
 num_beams=generation_config.num_beams,
 device=inputs_tensor.device,
 length_penalty=generation_config.length_penalty,
 do_early_stopping=generation_config.early_stopping,
 num_beam_hyps_to_keep=generation_config.num_return_sequences,
 num_beam_groups=generation_config.num_beam_groups,
 max_length=generation_config.max_length,
 )
 # 12. interleave input_ids with `num_beams` additional sequences per batch
 input_ids, model_kwargs = self._expand_inputs_for_generation(
 input_ids=input_ids,
 expand_size=generation_config.num_beams,
 is_encoder_decoder=self.config.is_encoder_decoder,
 **model_kwargs,
 )
 # 13. run beam search
 result = self._group_beam_search(
 input_ids,
 beam_scorer,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 **model_kwargs,
 )
elif generation_mode == GenerationMode.CONSTRAINED_BEAM_SEARCH:
 logger.warning_once(
 "Constrained Beam Search is scheduled to be moved to a `custom_generate` repository in v4.55.0. "
 "To prevent loss of backward compatibility, add `trust_remote_code=True` to your `generate` call."
 )
 final_constraints = []
 if generation_config.constraints is not None:
 final_constraints = generation_config.constraints
if generation_config.force_words_ids is not None:
def typeerror():
 raise ValueError(
 "`force_words_ids` has to either be a `list[list[list[int]]]` or `list[list[int]]` "
 f"of positive integers, but is {generation_config.force_words_ids}."
 )
if (
 not isinstance(generation_config.force_words_ids, list)
 or len(generation_config.force_words_ids) == 0
 ):
 typeerror()
for word_ids in generation_config.force_words_ids:
 if isinstance(word_ids[0], list):
 if not isinstance(word_ids, list) or len(word_ids) == 0:
 typeerror()
 if any(not isinstance(token_ids, list) for token_ids in word_ids):
 typeerror()
 if any(
 any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
 for token_ids in word_ids
 ):
 typeerror()
constraint = DisjunctiveConstraint(word_ids)
 else:
 if not isinstance(word_ids, list) or len(word_ids) == 0:
 typeerror()
 if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
 typeerror()
constraint = PhrasalConstraint(word_ids)
 final_constraints.append(constraint)
# 11. prepare beam search scorer
 constrained_beam_scorer = ConstrainedBeamSearchScorer(
 constraints=final_constraints,
 batch_size=batch_size,
 num_beams=generation_config.num_beams,
 device=inputs_tensor.device,
 length_penalty=generation_config.length_penalty,
 do_early_stopping=generation_config.early_stopping,
 num_beam_hyps_to_keep=generation_config.num_return_sequences,
 max_length=generation_config.max_length,
 )
 # 12. interleave input_ids with `num_beams` additional sequences per batch
 input_ids, model_kwargs = self._expand_inputs_for_generation(
 input_ids=input_ids,
 expand_size=generation_config.num_beams,
 is_encoder_decoder=self.config.is_encoder_decoder,
 **model_kwargs,
 )
 # 13. run beam search
 result = self._constrained_beam_search(
 input_ids,
 constrained_beam_scorer=constrained_beam_scorer,
 logits_processor=prepared_logits_processor,
 stopping_criteria=prepared_stopping_criteria,
 generation_config=generation_config,
 synced_gpus=synced_gpus,
 **model_kwargs,
 )
# Convert to legacy cache format if requested
 if (
 generation_config.return_legacy_cache is True
 and hasattr(result, "past_key_values")
 and getattr(result.past_key_values, "to_legacy_cache") is not None
 ):
 result.past_key_values = result.past_key_values.to_legacy_cache()
 return result
def _has_unfinished_sequences(self, this_peer_finished: bool, synced_gpus: bool, device: torch.device) -> bool:
 """
 Returns whether there are still unfinished sequences in the device. The existence of unfinished sequences is
 fed through `this_peer_finished`. ZeRO stage 3-friendly.
 """
 if synced_gpus:
 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
 # The following logic allows an early break if all peers finished generating their sequence
 this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0, device=device)
 # send 0.0 if we finished, 1.0 otherwise
 dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
 # did all peers finish? the reduced sum will be 0.0 then
 if this_peer_finished_flag.item() == 0.0:
 return False
 elif this_peer_finished:
 return False
 return True
def heal_tokens(
 self, input_ids: torch.LongTensor, tokenizer: Optional["PreTrainedTokenizerBase"] = None
 ) -> torch.LongTensor:
 r"""
 Generates sequences of token ids for models with a language modeling head.
 Parameters:
 input_ids (`torch.LongTensor`): The sequence used as a prompt for the generation.
 tokenizer (`PreTrainedTokenizerBase`, *optional*): The tokenizer used to decode the input ids.
 Return:
 `torch.LongTensor` where each sequence has its tail token replaced with its appropriate extension.
 """
 if tokenizer is None:
 raise ValueError(
 " When generating with token healing, you must pass the model's tokenizer to the `tokenizer` "
 "argument of `generate`."
 )
bos_token_id, pad_token_id = tokenizer.bos_token_id, tokenizer.pad_token_id
 vocab_trie = ExtensionsTrie(tokenizer.get_vocab())
 generation_config = GenerationConfig(max_new_tokens=1, pad_token_id=pad_token_id)
# assumption: leading/trailing whitespace is not meaningful, so the prompts are
 # stripped before re-tokenizing to desensitize generation to whitespace artefacts
 prompts = [p.strip() for p in tokenizer.batch_decode(input_ids, skip_special_tokens=True)]
 input_ids = tokenizer(
 prompts,
 return_tensors="pt",
 padding=True,
 ).input_ids.to(input_ids.device)
# replace bos with pad to not condition healing on it
 input_ids = torch.where(input_ids == bos_token_id, pad_token_id, input_ids)
"""
 the latter code assumes the input_ids is not empty,
 input_id has to be checked if contains elements
 """
 if input_ids.numel() == 0:
 return input_ids
tail_ids = input_ids[:, -1].tolist()
space_tok = tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(" "))[0]
 # tail tokens are used for a prefix search, thus, whitespaces are replaced with
 # their tokenization (e.g. 'Δ ') to enable search for tokens prefixed with a whitespace
 tail_toks = (tokenizer.decode(t).replace(" ", space_tok) for t in tail_ids)
for batch_idx, (tail_id, tail_tok) in enumerate(zip(tail_ids, tail_toks)):
 batch_ids = input_ids[batch_idx]
 if torch.all(batch_ids == pad_token_id).item():
 continue # skip empty sequences (all pad ids)
# apply bias for alternatives (extensions) to the tail token
 """
 seq_bias key has to be tuple with int so have to use
 tokenizer function to convert str to int
 """
 seq_bias = {
 (tokenizer.convert_tokens_to_ids(alt_tok),): 10.0 for alt_tok in vocab_trie.extensions(prefix=tail_tok)
 }
if len(seq_bias) == 1:
 continue # skip if there are no token alternatives to heal with
# slightly favor original token to limit aggressive healing e.g. 'http' -> 'https'
 seq_bias[(tail_id,)] += 1.0
 generation_config.update(sequence_bias=seq_bias)
trimmed_ids = batch_ids[:-1]
"""
 the latter code assumes trimmed_ids is not empty
 so have to check the its element count
 """
 if trimmed_ids.numel() == 0:
 continue
# if the prompt is a single (non-pad) token, regenerate from bos
 if len(batch_ids[batch_ids != pad_token_id]) == 1:
 trimmed_ids[-1] = bos_token_id
input_ids[batch_idx] = self.generate(trimmed_ids.unsqueeze(0), generation_config=generation_config)
return input_ids
def _dola_decoding(
 self,
 input_ids: torch.LongTensor,
 dola_layers: Union[str, list[int]],
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 streamer: "BaseStreamer",
 **model_kwargs,
 ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **dola decoding** and can be
 used for decoder-only text models.
 The method is based on the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language
 Models" (https://huggingface.co/papers/2309.03883) in ICLR 2024.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
 The sequence used as a prompt for the generation.
 dola_layers (`Union[str, list[int]]`):
 The candidate layers used in contrasting layers of DoLa. It can be either 1) 'low' or 'high', which
 means the lower part or higher part of the model layers, respectively, or 2) a list of layer indices
 to be used for candidate layers. The 0-th layer is the word embedding layer of the model.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`, *optional*):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 streamer (`BaseStreamer`, *optional*):
 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
 model_kwargs:
 Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
 If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`]
 or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
if self.config.is_encoder_decoder:
 raise ValueError("DoLa decoding is only available for decoder-only models.")
 # init values
pad_token_id = generation_config._pad_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
 has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
 do_sample = generation_config.do_sample
# init attention / hidden states / scores tuples
 scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# keep track of which sequences are already finished
 batch_size, cur_length = input_ids.shape[:2]
 unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
 model_kwargs = self._get_initial_cache_position(cur_length, input_ids.device, model_kwargs)
this_peer_finished = False
# prepare layers for DoLa decoding
 final_layer = self.config.get_text_config().num_hidden_layers
 # if the model has tied word embeddings, we skip the word embeddings (0-th) layer and start from the 2nd layer,
 # as the early exit from word embeddings will become identity function
 # if the model is really shallow (<=2 layers), we use the 1st layer if it's not the final layer and the 0-th
 # layer otherwise. Notice that DoLa does not help shallow models much.
 if not self.config.tie_word_embeddings:
 start_layer = 0
 elif final_layer > 2:
 start_layer = 2
 elif final_layer == 2:
 start_layer = 1
 else:
 start_layer = 0
# For `N`-layer models with `N <= 40` layers, the layers of `range(0, N // 2, 2)` and `range(N // 2, N, 2)`
 # are used for `'low'` and `'high'` layers, respectively.
 # For models with `N > 40` layers, the layers of `range(0, 20, 2)` and `range(N - 20, N, 2)` are used for
 # `'low'` and `'high'` layers, respectively.
 if isinstance(dola_layers, str) and dola_layers == "low":
 if start_layer == final_layer // 2:
 candidate_premature_layers = [start_layer]
 else:
 candidate_premature_layers = (
 list(range(start_layer, final_layer // 2, 2))
 if final_layer <= 40
 else list(range(start_layer, 20, 2))
 )
 elif isinstance(dola_layers, str) and dola_layers == "high":
 candidate_premature_layers = (
 list(range(final_layer // 2, final_layer, 2))
 if final_layer <= 40
 else list(range(final_layer - 20, final_layer, 2))
 )
 # Set the `dola_layers` to a list of integers for layer indices to contrast manually specified layers.
 elif isinstance(dola_layers, list):
 candidate_premature_layers = [i for i in dola_layers if i < final_layer]
 else:
 raise ValueError("dola_layers must be either 'low', 'high' or a list of integers.")
lm_head = self.get_output_embeddings()
 if lm_head is None:
 raise ValueError("DoLa is not supported for models that don't have output embeddings.")
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 # prepare model inputs
 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
 outputs = self(
 **model_inputs,
 return_dict=True,
 output_attentions=output_attentions,
 output_hidden_states=True,
 )
# .float() is needed to retain precision for later logits manipulations
 final_layer_next_token_logits = outputs.logits[:, -1, :].detach().to(copy=True, dtype=torch.float32)
 final_logits = outputs.logits[:, -1, :].float()
 candidate_premature_logits = {}
 for candidate_premature_layer in candidate_premature_layers:
 candidate_premature_logits[candidate_premature_layer] = lm_head(
 outputs.hidden_states[candidate_premature_layer][:, -1, :]
 ).to(final_logits.device)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 continue
next_token_logits = _dola_select_contrast(
 candidate_premature_layers, candidate_premature_logits, final_logits
 )
 next_token_logits = next_token_logits.to(input_ids.device)
 # pre-process distribution
 next_token_scores = logits_processor(input_ids, next_token_logits)
# Store scores, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_scores:
 scores += (next_token_scores,)
 if output_logits:
 raw_logits += (final_layer_next_token_logits,)
 if output_attentions:
 decoder_attentions += (
 (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (outputs.hidden_states,)
 )
if do_sample: # sample
 probs = nn.functional.softmax(next_token_scores, dim=-1)
 next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
 else: # argmax
 next_tokens = torch.argmax(next_token_scores, dim=-1)
# finished sentences should have their next token be a padding token
 if has_eos_stopping_criteria:
 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
 input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
 if streamer is not None:
 streamer.put(next_tokens.cpu())
# stop when each sentence is finished
 unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
 this_peer_finished = unfinished_sequences.max() == 0
if streamer is not None:
 streamer.end()
if return_dict_in_generate:
 return GenerateDecoderOnlyOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return input_ids
@torch.no_grad()
 def _contrastive_search(
 self,
 input_ids: torch.LongTensor,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 streamer: Optional["BaseStreamer"],
 **model_kwargs,
 ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
 be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
 The sequence used as a prompt for the generation.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 streamer (`BaseStreamer`, *optional*):
 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
 model_kwargs:
 Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
 If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`]
 or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
 # init values
 has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
 top_k = generation_config.top_k
 penalty_alpha = generation_config.penalty_alpha
 pad_token_id = generation_config._pad_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
 sequential = generation_config.low_memory
# init attention / hidden states / scores tuples
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 scores = () if (return_dict_in_generate and output_scores) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# keep track of which sequences are already finished
 batch_size, cur_len = input_ids.shape[:2]
 unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
 model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
# Create cosine_matrix_mask based on the attention_mask
 cosine_matrix_mask = torch.ones_like(input_ids, dtype=torch.long)
 if self.config.is_encoder_decoder:
 if "decoder_attention_mask" in model_kwargs and model_kwargs["decoder_attention_mask"] is not None:
 cosine_matrix_mask = model_kwargs["decoder_attention_mask"]
 else:
 cosine_matrix_mask = model_kwargs["attention_mask"]
 cosine_matrix_mask = cosine_matrix_mask.repeat_interleave(top_k, dim=0)
this_peer_finished = False
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 # if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
 # (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
 if model_kwargs.get("past_key_values") is None or (
 isinstance(model_kwargs["past_key_values"], (Cache, EncoderDecoderCache))
 and model_kwargs["past_key_values"].get_seq_length() == 0
 ):
 # prepare inputs
 model_kwargs["use_cache"] = True
 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
 # the `encoder_outputs`
 outputs = self(
 **model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
 )
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
 # previous tokens)
 if self.config.is_encoder_decoder:
 last_hidden_states = outputs.decoder_hidden_states[-1]
 else:
 last_hidden_states = outputs.hidden_states[-1]
# next logit for contrastive search to select top-k candidate tokens
 # Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for this first iteration
 # (the clone itself is always small)
 # torch.float32 is needed to retain precision for later logits manipulations
 logit_for_next_step = outputs.logits[:, -1, :].to(
 copy=True, dtype=torch.float32, device=input_ids.device
 )
model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
if not sequential:
 # Expands model inputs top_k times, for batched forward passes (akin to beam search).
 # input_ids is required for expanding visual inputs in qwen2vl
 _, model_kwargs = self._expand_inputs_for_generation(
 input_ids=input_ids,
 expand_size=top_k,
 is_encoder_decoder=self.config.is_encoder_decoder,
 **model_kwargs,
 )
past_key_values = model_kwargs.get("past_key_values")
 if past_key_values is None:
 raise ValueError(
 f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
 "for contrastive search."
 )
 elif (
 not isinstance(past_key_values[0], (tuple, torch.Tensor))
 or past_key_values[0][0].shape[0] != batch_size
 ):
 raise ValueError(
 f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
 "used for contrastive search without further modifications."
 )
# contrastive_search main logic start:
 # contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
 # degeneration penalty
 processed_logit_for_next_step = logits_processor(input_ids, logit_for_next_step)
 next_probs = nn.functional.softmax(processed_logit_for_next_step, dim=-1)
top_k_probs, top_k_ids = torch.topk(next_probs, dim=-1, k=top_k)
# Store scores, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_logits:
 raw_logits += (logit_for_next_step,)
 if output_scores:
 scores += (processed_logit_for_next_step,)
 if output_attentions:
 decoder_attentions += (
 (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (outputs.hidden_states,)
 )
# This is needed to properly delete outputs.logits which may be very large for this first iteration
 # Otherwise a reference to outputs.logits is kept all along until after the next call to self.forward()
 del outputs
if not sequential:
 # Replicates the new past_key_values to match the `top_k` candidates
 past = model_kwargs["past_key_values"]
 # If it is a static cache, modify it in-place layer after layer to save memory
 if isinstance(past, DynamicCache) or (
 isinstance(past, EncoderDecoderCache) and isinstance(past.self_attention_cache, DynamicCache)
 ):
 past.batch_repeat_interleave(top_k)
 else:
 new_key_values = []
 for layer in past:
 items = []
 # item is either the key or the value matrix
 for item in layer:
 items.append(item.repeat_interleave(top_k, dim=0))
 new_key_values.append(tuple(items))
past = tuple(new_key_values)
model_kwargs["past_key_values"] = past
if sequential:
 all_outputs = []
 for i in range(top_k):
 # compute the candidate tokens by the language model and collect their hidden_states
 next_model_inputs = self.prepare_inputs_for_generation(top_k_ids[:, i].view(-1, 1), **model_kwargs)
outputs = self(
 **next_model_inputs,
 return_dict=True,
 output_hidden_states=True,
 output_attentions=output_attentions,
 )
 if isinstance(outputs["past_key_values"], DynamicCache) or (
 isinstance(outputs["past_key_values"], EncoderDecoderCache)
 and isinstance(outputs["past_key_values"].self_attention_cache, DynamicCache)
 ):
 # Remove past K-V from output since we don't need to stack later
 outputs["past_key_values"] = None
 # Remove last token from past K-V since we don't want to append it at this point
 model_kwargs["past_key_values"].crop(-1)
all_outputs.append(outputs)
 outputs = stack_model_outputs(all_outputs, self.config.get_text_config())
else:
 # compute the candidate tokens by the language model and collect their hidden_states
 # assembles top_k_ids into batch of size k
 next_model_inputs = self.prepare_inputs_for_generation(top_k_ids.view(-1, 1), **model_kwargs)
outputs = self(
 **next_model_inputs,
 return_dict=True,
 output_hidden_states=True,
 output_attentions=output_attentions,
 )
# This is essential to avoid having a last reference to the big past K-V and double the necessary memory
 # in the next loop
 del next_model_inputs
# name is different for encoder-decoder and decoder-only models
 if self.config.is_encoder_decoder:
 next_hidden = outputs.decoder_hidden_states[-1]
 full_hidden_states = outputs.decoder_hidden_states
 else:
 next_hidden = outputs.hidden_states[-1]
 full_hidden_states = outputs.hidden_states
# .float() is needed to retain precision for later logits manipulations
 logits = outputs.logits[:, -1, :].float()
 context_hidden = last_hidden_states.repeat_interleave(top_k, dim=0)
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
 # model confidence. Keeping `selected_idx` on CPU enables multi-device contrastive search and doesn't
 # introduce (noticeable) slowdowns on single-device runs.
 selected_idx = _ranking_fast(
 context_hidden, next_hidden, top_k_probs, cosine_matrix_mask, penalty_alpha, top_k
 )
 cosine_matrix_mask = torch.cat(
 [cosine_matrix_mask, cosine_matrix_mask.new_ones((cosine_matrix_mask.shape[0], 1))], dim=-1
 )
 selected_idx = selected_idx.to("cpu")
# This will be used instead of the previous inneficient torch.stack(torch.split())
 augmented_idx = torch.tensor([x + i * top_k for i, x in enumerate(selected_idx)])
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
 # the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
 # (model confidence minus degeneration penalty); (6) decoder hidden_states
 next_tokens = top_k_ids[range(len(top_k_ids)), selected_idx]
 next_hidden = torch.stack(torch.split(next_hidden.squeeze(dim=1), top_k))
 next_hidden = next_hidden[range(batch_size), selected_idx, :]
 last_hidden_states = torch.cat([last_hidden_states, next_hidden.unsqueeze(1)], dim=1)
next_decoder_hidden_states = ()
 for layer in full_hidden_states:
 layer = torch.stack(torch.split(layer, top_k))[range(batch_size), selected_idx, :]
 next_decoder_hidden_states += (layer,)
# generate past_key_values cache of only the selected token
 if sequential:
 next_model_input = self.prepare_inputs_for_generation(
 top_k_ids[:, selected_idx].view(-1, 1), **model_kwargs
 )
selected_outputs = self(
 **next_model_input,
 return_dict=True,
 output_hidden_states=False,
 output_attentions=False,
 )
 next_past_key_values = selected_outputs["past_key_values"]
else:
 next_past_key_values = None
 for possible_cache_name in ALL_CACHE_NAMES:
 next_past_key_values = next_past_key_values or getattr(outputs, possible_cache_name, None)
 # Do it in-place layer per layer to save memory
 if isinstance(next_past_key_values, DynamicCache) or (
 isinstance(next_past_key_values, EncoderDecoderCache)
 and isinstance(next_past_key_values.self_attention_cache, DynamicCache)
 ):
 next_past_key_values.batch_select_indices(augmented_idx)
 else:
 new_key_values = []
 for layer in next_past_key_values:
 items = []
 # item is either the key or the value matrix
 for item in layer:
 items.append(item[augmented_idx, ...])
 new_key_values.append(tuple(items))
next_past_key_values = tuple(new_key_values)
logit_for_next_step = torch.stack(torch.split(logits, top_k))[range(batch_size), selected_idx, :]
 logit_for_next_step = logit_for_next_step.to(input_ids.device)
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
 if self.config.is_encoder_decoder:
 next_step_cross_attentions = ()
 next_step_decoder_attentions = ()
 if output_attentions:
 for layer in outputs.cross_attentions:
 layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
 next_step_cross_attentions += (layer,)
 for layer in outputs.decoder_attentions:
 layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
 next_step_decoder_attentions += (layer,)
 outputs = Seq2SeqLMOutput(
 past_key_values=next_past_key_values,
 decoder_hidden_states=next_decoder_hidden_states,
 decoder_attentions=next_step_decoder_attentions or None,
 cross_attentions=next_step_cross_attentions or None,
 )
 else:
 next_step_attentions = ()
 if output_attentions:
 for layer in outputs.attentions:
 layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
 next_step_attentions += (layer,)
 outputs = CausalLMOutputWithPast(
 past_key_values=next_past_key_values,
 hidden_states=next_decoder_hidden_states,
 attentions=next_step_attentions or None,
 )
 # contrastive_search main logic end
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 continue
# finished sentences should have their next token be a padding token
 if has_eos_stopping_criteria:
 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
 input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
 if streamer is not None:
 streamer.put(next_tokens.cpu())
# stop when each sentence is finished
 unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
 this_peer_finished = unfinished_sequences.max() == 0
if streamer is not None:
 streamer.end()
if return_dict_in_generate:
 # Contrastive search works by forward looking at the next token, so we need to exclude it from
 # `past_key_values` to be consistent with the other decoding methods
 if model_kwargs.get("past_key_values") is not None:
 if isinstance(model_kwargs["past_key_values"], DynamicCache) or (
 isinstance(model_kwargs["past_key_values"], EncoderDecoderCache)
 and isinstance(model_kwargs["past_key_values"].self_attention_cache, DynamicCache)
 ):
 model_kwargs["past_key_values"].crop(-1)
 else:
 past_key_values = []
 for layer in model_kwargs["past_key_values"]:
 layer_past_key_values = []
 for item in layer:
 layer_past_key_values.append(item[..., :-1, :])
 past_key_values.append(tuple(layer_past_key_values))
 model_kwargs["past_key_values"] = tuple(past_key_values)
if self.config.is_encoder_decoder:
 return GenerateEncoderDecoderOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateDecoderOnlyOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return input_ids
def _sample(
 self,
 input_ids: torch.LongTensor,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 streamer: Optional["BaseStreamer"],
 **model_kwargs,
 ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
 can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
 The sequence used as a prompt for the generation.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 streamer (`BaseStreamer`, *optional*):
 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
 model_kwargs:
 Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
 an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or `torch.LongTensor`:
 A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
 # init values
 pad_token_id = generation_config._pad_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
 has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
 do_sample = generation_config.do_sample
# init attention / hidden states / scores tuples
 scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# keep track of which sequences are already finished
 batch_size, cur_len = input_ids.shape[:2]
 this_peer_finished = False
 unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
 model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
model_forward = self.__call__
 compile_forward = self._valid_auto_compile_criteria(model_kwargs, generation_config)
 if compile_forward:
 os.environ["TOKENIZERS_PARALLELISM"] = "0"
 # If we use FA2 and a static cache, we cannot compile with fullgraph
 if self.config._attn_implementation == "flash_attention_2" and getattr(
 model_kwargs.get("past_key_values"), "is_compileable", False
 ):
 if generation_config.compile_config is None:
 generation_config.compile_config = CompileConfig(fullgraph=False)
 # only raise warning if the user passed an explicit compile-config (otherwise, simply change the default without confusing the user)
 elif generation_config.compile_config.fullgraph:
 logger.warning_once(
 "When using Flash Attention 2 and a static cache, you cannot use the option `CompileConfig(fullgraph=True)` as "
 "FA2 introduces graph breaks. We overrode the option with `fullgraph=False`."
 )
 generation_config.compile_config.fullgraph = False
 model_forward = self.get_compiled_call(generation_config.compile_config)
if generation_config.prefill_chunk_size is not None:
 model_kwargs = self._prefill_chunking(input_ids, generation_config, **model_kwargs)
 is_prefill = False
 else:
 is_prefill = True
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 # prepare model inputs
 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# prepare variable output controls (note: some models won't accept all output controls)
 model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
 model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
if is_prefill:
 outputs = self(**model_inputs, return_dict=True)
 is_prefill = False
 else:
 outputs = model_forward(**model_inputs, return_dict=True)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 continue
# Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration
 # (the clone itself is always small)
 next_token_logits = outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=input_ids.device)
# pre-process distribution
 next_token_scores = logits_processor(input_ids, next_token_logits)
# Store scores, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_scores:
 scores += (next_token_scores,)
 if output_logits:
 raw_logits += (next_token_logits,)
 if output_attentions:
 decoder_attentions += (
 (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (outputs.hidden_states,)
 )
# token selection
 if do_sample:
 probs = nn.functional.softmax(next_token_scores, dim=-1)
 # TODO (joao): this OP throws "skipping cudagraphs due to ['incompatible ops']", find solution
 next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
 else:
 next_tokens = torch.argmax(next_token_scores, dim=-1)
# finished sentences should have their next token be a padding token
 if has_eos_stopping_criteria:
 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
 input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
 if streamer is not None:
 streamer.put(next_tokens.cpu())
unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
 this_peer_finished = unfinished_sequences.max() == 0
 cur_len += 1
# This is needed to properly delete outputs.logits which may be very large for first iteration
 # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
 del outputs
if streamer is not None:
 streamer.end()
if return_dict_in_generate:
 if self.config.is_encoder_decoder:
 return GenerateEncoderDecoderOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateDecoderOnlyOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return input_ids
# Auxiliary functions for beam search
 def _temporary_reorder_cache(self, past_key_values, beam_idx):
 """
 Temporary function to handle the different types of cache reordering processes while we roll out `Cache`.
TODO: standardize cache formats and make all models compatible with `Cache`. It would remove the need
 for this function, with `Cache.reorder_cache` being the sole remaining code path
 """
 model_class = self.__class__.__name__.lower()
 # Exception 1: code path for models using the legacy cache format
 if isinstance(past_key_values, (tuple, list)):
 past_key_values = self._reorder_cache(past_key_values, beam_idx)
 # Exception 2: models with different cache formats. These are limited to `DynamicCache` until their
 # cache format is standardized, to avoid adding complexity to the codebase.
 elif "gptbigcode" in model_class:
 if not isinstance(past_key_values, (DynamicCache, EncoderDecoderCache)):
 raise ValueError(
 f"Using an unsupported cache format with {model_class}. Currently, it only supports the "
 "legacy tuple format or `DynamicCache`"
 )
 past_key_values = self._reorder_cache(past_key_values, beam_idx)
 past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 # Standard code path: use the `Cache.reorder_cache`
 else:
 past_key_values.reorder_cache(beam_idx)
 return past_key_values
@staticmethod
 def _flatten_beam_dim(tensor: torch.Tensor) -> torch.Tensor:
 """[batch_size, num_beams, ...] -> [batch_size * num_beams, ...]"""
 shape = list(tensor.shape)
 return torch.reshape(tensor, [shape[0] * shape[1]] + shape[2:])
@staticmethod
 def _unflatten_beam_dim(tensor: torch.Tensor, batch_size: int, num_beams: int) -> torch.Tensor:
 """[batch_size * num_beams, ...] -> [batch_size, num_beams, ...]"""
 shape = list(tensor.shape)
 return torch.reshape(tensor, [batch_size, num_beams] + shape[1:])
@staticmethod
 def _gather_beams(tensor: torch.Tensor, beam_indices: torch.Tensor) -> torch.Tensor:
 """
 Gathers the beam slices indexed by beam_indices into new beam array.
Args:
 tensor (`torch.Tensor`): A tensor containing data to be gathered. The tensor is a 2D or a 3D tensor
 with the two first dimensions depicting the batch and the beam dimensions.
 beam_indices (`torch.Tensor` of shape `(batch_size, num_beams_to_select)`): The indices of the beams to
 select .
Returns:
 A tensor with the selected beams
 """
 # `take_along_dim` requires its indices arg to have the same number of dims as `input`
 while len(beam_indices.shape) < len(tensor.shape):
 beam_indices = beam_indices.unsqueeze(-1)
 gathered_tensor = torch.take_along_dim(input=tensor, indices=beam_indices, dim=1)
 return gathered_tensor
@staticmethod
 def _beam_search_has_unfinished_sequences(
 running_beam_scores: torch.Tensor,
 beam_scores: torch.Tensor,
 is_sent_finished: torch.Tensor,
 next_token_hits_stopping_criteria: torch.Tensor,
 cur_len: int,
 max_length: int,
 decoder_prompt_len: int,
 early_stopping: Union[bool, str],
 length_penalty: float,
 ):
 """
 Beam Search stopping condition -- halts the generation loop if any of these conditions becomes False
 """
 # a. Can the open beams improve the top completed scores?
 # early_stopping == False -> apply heuristic = always get the best score from
 # `cur_len - decoder_prompt_len`. See the discussion below for more details.
 # https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
 # early_stopping == "never" -> compute the best score from `max_length` or `cur_len`, depending on the
 # sign of `length_penalty`. Positive `length_penalty` favors longer sequences, thus we use
 # `max_length` there.
 if early_stopping == "never" and length_penalty > 0.0:
 best_hypothetical_length = max_length - decoder_prompt_len
 else:
 best_hypothetical_length = cur_len - decoder_prompt_len
 best_possible_running_score = running_beam_scores[:, :1] / (best_hypothetical_length**length_penalty)
 worst_finished_score = torch.where(is_sent_finished, torch.min(beam_scores, dim=1, keepdim=True)[0], -1.0e9)
 improvement_possible = torch.any(best_possible_running_score > worst_finished_score)
# b. Is there still a beam without fully completed sequences? This is only relevant if early_stopping is
 # enabled, where we want to finish as soon as all beams have a completed sequence.
 exists_open_beam = ~(torch.all(is_sent_finished) & (early_stopping is True))
# c. Have we hit a stopping criteria with all running sequences and have no way to continue? e.g. we have
 # reached `max_length``
 valid_continuations = ~torch.all(next_token_hits_stopping_criteria)
return improvement_possible & exists_open_beam & valid_continuations
def _get_top_k_continuations(
 self,
 accumulated_log_probs: torch.Tensor,
 running_sequences: torch.Tensor,
 running_beam_indices: torch.Tensor,
 cur_len: int,
 decoder_prompt_len: int,
 do_sample: bool,
 beams_to_keep: int,
 num_beams: int,
 vocab_size: int,
 batch_size: int,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
 """
 Get top-K continuations given the accumulated log probs on the next token.
A few notes to understand what's going on:
 1. Each item in batch has `num_beams` * `vocab_size` candidate continuations. For each item, get the
 top K [K = (number of EOS tokens + 1) * `num_beams`] candidates with the highest accumulated
 log-probabilities, or sample them without replacement using the accumulated scores
 2. We gather the top K (as opposed to `num_beams`, or any number lower than K) here so that we have at
 least `num_beams` sequences remaining to continue the live beam search.
 3. Note that other stopping criteria might result in impossible to continue beams, i.e. all continuations
 selected in this step hit the stopping criteria.
 """
 # TODO (joao): This function should take an optional beam scorer function, to manipulate the scores after
 # token selection. The function should be an argument exposed, so that custom scoring functions can be
 # defined.
# Gather the top K scores from _all_ beams.
 if do_sample:
 topk_indices = torch.multinomial(
 nn.functional.softmax(accumulated_log_probs, dim=-1), num_samples=beams_to_keep
 )
 topk_log_probs = torch.gather(input=accumulated_log_probs, dim=1, index=topk_indices)
 else:
 topk_log_probs, topk_indices = torch.topk(accumulated_log_probs, k=beams_to_keep)
# Gather K top beams, recover the beam index by floor division and token id by modulo division
 topk_current_beam_indices = topk_indices // vocab_size
 topk_running_beam_indices = self._gather_beams(running_beam_indices, topk_current_beam_indices)
 topk_running_sequences = self._gather_beams(running_sequences, topk_current_beam_indices)
 topk_ids = topk_indices % vocab_size
# Update sequences for the K top-k new sequences.
 topk_running_sequences[:, :, cur_len] = topk_ids
# we want to store the beam indices with batch information -> real beam index = beam index % num beams
 batch_offset = torch.arange(batch_size, device=topk_ids.device).view(-1, 1) * num_beams
 batch_modified_indices = topk_current_beam_indices + batch_offset
 topk_running_beam_indices[:, :, cur_len - decoder_prompt_len] = batch_modified_indices
return topk_log_probs, topk_running_sequences, topk_running_beam_indices
def _get_running_beams_for_next_iteration(
 self,
 topk_log_probs: torch.Tensor,
 topk_running_sequences: torch.Tensor,
 topk_running_beam_indices: torch.Tensor,
 next_token_hits_stopping_criteria: torch.Tensor,
 num_beams: int,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
 """
 Given the top-K continuations, their scores, and whether they hit a stopping criteria, select the
 best non-finished beams to continue beam search in the next iteration.
 """
 # To prevent these just finished sequences from being used in subsequent iterations, set their log probs
 # to a very large negative value
 topk_running_log_probs = topk_log_probs + next_token_hits_stopping_criteria.to(torch.float32) * -1.0e9
next_topk_indices = torch.topk(topk_running_log_probs, k=num_beams)[1]
 running_sequences = self._gather_beams(topk_running_sequences, next_topk_indices)
 running_beam_scores = self._gather_beams(topk_running_log_probs, next_topk_indices)
 running_beam_indices = self._gather_beams(topk_running_beam_indices, next_topk_indices)
 return running_sequences, running_beam_scores, running_beam_indices
def _update_finished_beams(
 self,
 sequences: torch.Tensor,
 topk_running_sequences: torch.Tensor,
 beam_scores: torch.Tensor,
 topk_log_probs: torch.Tensor,
 beam_indices: torch.Tensor,
 topk_running_beam_indices: torch.Tensor,
 is_sent_finished: torch.Tensor,
 next_token_hits_stopping_criteria: torch.Tensor,
 top_num_beam_mask: torch.Tensor,
 num_beams: int,
 cur_len: int,
 decoder_prompt_len: int,
 length_penalty: float,
 early_stopping: Union[bool, str],
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
 """
 Updates the finished beams if (and only if) there are new completed sequences that have a higher score than
 the current finished sequences.
 """
 # Only the top `num_beam` sequences can be considered for the final returned sequences. Remember: the
 # remaining sequences only exist as a backup to ensure that we have at least `num_beams` sequences to
 # continue.
 did_top_num_beams_just_finished = next_token_hits_stopping_criteria & top_num_beam_mask[None, :]
# Further process topk logits for the finished beams
 # - add length penalty
 topk_log_probs = topk_log_probs / ((cur_len + 1 - decoder_prompt_len) ** length_penalty)
 # - make sure no scores can be added anymore if beam is full and early stopping is on
 beams_in_batch_are_full = torch.all(is_sent_finished, axis=-1, keepdims=True) & (early_stopping is True)
 topk_log_probs += beams_in_batch_are_full.to(torch.float32) * -1.0e9
 # - make sure still running sequences cannot be chosen as finalized beam
 topk_log_probs += (~did_top_num_beams_just_finished) * -1.0e9
# Get finalized `num_beam` sequences for the next generation step -- combine the previous finalized
 # data with the new finalized sequences (if any, non-finalized sequences have a very large negative score
 # in this step), and keep the best `num_beams` sequences.
 merged_sequences = torch.cat((sequences, topk_running_sequences), dim=1)
 merged_scores = torch.cat((beam_scores, topk_log_probs), dim=1)
 merged_beam_indices = torch.cat((beam_indices, topk_running_beam_indices), dim=1)
 merged_is_sent_finished = torch.cat((is_sent_finished, did_top_num_beams_just_finished), dim=1)
 topk_merged_indices = torch.topk(merged_scores, k=num_beams)[1]
 sequences = self._gather_beams(merged_sequences, topk_merged_indices)
 beam_scores = self._gather_beams(merged_scores, topk_merged_indices)
 beam_indices = self._gather_beams(merged_beam_indices, topk_merged_indices)
 is_sent_finished = self._gather_beams(merged_is_sent_finished, topk_merged_indices)
 return sequences, beam_scores, beam_indices, is_sent_finished
# end of auxiliary functions for beam search
def _beam_search(
 self,
 input_ids: torch.LongTensor,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 **model_kwargs,
 ) -> Union[GenerateBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **beam search decoding** and
 can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
If it's the first time you're diving into Beam Search, we recommend you read the following blog post:
 https://huggingface.co/blog/how-to-generate (especially the beam search section).
You can recompute the sequence scores from the individual scores using the `compute_transition_scores` function
 (https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationMixin.compute_transition_scores)
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
 The sequence used as a prompt for the generation.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`:
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 model_kwargs:
 Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
 an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or
 `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
# 1. init beam_search values
 pad_token_id = generation_config._pad_token_tensor
 eos_token_id = generation_config._eos_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
 do_sample = generation_config.do_sample
 early_stopping = generation_config.early_stopping
 length_penalty = generation_config.length_penalty
 max_length = generation_config.max_length
 num_beams = generation_config.num_beams
 num_return_sequences = generation_config.num_return_sequences
batch_size_unflattened, cur_len = input_ids.shape[:2]
 batch_size = batch_size_unflattened // num_beams
 # TODO (joao): standardize special cases
 if self.__class__.__name__ == "MoshiDepthDecoder":
 vocab_size = self.config.audio_vocab_size
 elif self.__class__.__name__ == "ImageGPTForCausalImageModeling":
 vocab_size = self.get_output_embeddings().out_features
 else:
 vocab_size = self.config.get_text_config().vocab_size
 decoder_prompt_len = cur_len
 this_peer_finished = False
# At each beam search step, we want to keep top K [K = (number of EOS tokens + 1) * `num_beams`] candidates
 # with the highest log-probabilities, or sample K continuations without replacement. We gather the top K
 # (as opposed to `num_beams`, or any number lower than K) so that we have at least `num_beams` sequences
 # non-finished to continue the live beam search, in case the top `num_beams` all select an EOS token.
 n_eos_tokens = eos_token_id.shape[0] if eos_token_id is not None else 0
 beams_to_keep = max(2, 1 + n_eos_tokens) * num_beams
 top_num_beam_mask = torch.cat(
 (torch.ones((num_beams), dtype=torch.bool), torch.zeros((beams_to_keep - num_beams), dtype=torch.bool)),
 dim=0,
 ).to(input_ids.device)
model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
# (joao) feature lost in the refactor. Probably won't implement, hurts readability with minimal gains (there
 # are newer low-memory alternatives like the offloaded cache)
 sequential = generation_config.low_memory
 if sequential:
 raise ValueError(
 "`low_memory=True` is not supported after the beam search refactor. Please check the discussion in "
 "#35802 *after the PR got merged*, and add a comment there if your questions are not yet answered."
 )
# 2. init output tuples
 all_scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 beam_indices = () if (return_dict_in_generate and output_logits) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# 3. init running tensors and static-shaped placeholders
# per batch, beam-item holding current token in loop and completed sequences
 output_fill_value = pad_token_id or eos_token_id[0] if eos_token_id is not None else -1
 running_sequences = torch.full(
 (batch_size, num_beams, max_length),
 fill_value=output_fill_value,
 dtype=torch.int64,
 device=input_ids.device,
 )
 running_sequences[:, :, :cur_len] = self._unflatten_beam_dim(input_ids, batch_size, num_beams)
 sequences = running_sequences.detach().clone()
# per batch, beam-item score, logprobs
 # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
 # of the first beam are considered to avoid sampling the exact same tokens across all beams.
 running_beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
 running_beam_scores[:, 1:] = -1e9
 beam_scores = torch.full((batch_size, num_beams), fill_value=-1e9, dtype=torch.float, device=input_ids.device)
# per batch, beam-item state bit indicating if sentence has finished.
 is_sent_finished = torch.zeros((batch_size, num_beams), dtype=torch.bool, device=input_ids.device)
# per batch, beam-item state bit indicating if there are valid continuations.
 next_token_hits_stopping_criteria = torch.zeros(
 (batch_size, num_beams), dtype=torch.bool, device=input_ids.device
 )
# per batch selected beam indices
 running_beam_indices = torch.full(
 (batch_size, num_beams, max_length - cur_len), fill_value=-1, dtype=torch.int32, device=input_ids.device
 )
 beam_indices = running_beam_indices.detach().clone()
# 4. run the generation loop
 while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 # a. Forward current tokens, obtain the logits
 flat_running_sequences = self._flatten_beam_dim(running_sequences[:, :, :cur_len])
 model_inputs = self.prepare_inputs_for_generation(flat_running_sequences, **model_kwargs)
# prepare variable output controls (note: some models won't accept all output controls)
 model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
 model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
model_outputs = self(**model_inputs, return_dict=True)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 model_outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 continue
# Copy is needed to avoid keeping a hanging ref
 logits = model_outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=input_ids.device)
# b. Compute log probs -- get log probabilities from logits, process logits with processors (*e.g.*
 # `temperature`, ...), and add new logprobs to existing running logprobs scores.
 log_probs = nn.functional.log_softmax(logits, dim=-1)
 log_probs = logits_processor(flat_running_sequences, log_probs)
# Store logits, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_logits:
 raw_logits += (logits.clone(),)
 if return_dict_in_generate and output_scores:
 all_scores += (log_probs.clone(),)
if output_attentions:
 decoder_attentions += (
 (model_outputs.decoder_attentions,)
 if self.config.is_encoder_decoder
 else (model_outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (model_outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (model_outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (model_outputs.hidden_states,)
 )
# This is needed to properly delete logits which may be very large for first iteration
 # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
 del model_outputs
log_probs = self._unflatten_beam_dim(log_probs, batch_size, num_beams)
 log_probs = log_probs + running_beam_scores[:, :, None]
 log_probs = torch.reshape(log_probs, (batch_size, num_beams * vocab_size))
# c. Retrieve top-K continuations, i.e. select the next token (greedy or sampling) and then keep the best
 # continuations among all beams based on the accumulated scores.
 topk_log_probs, topk_running_sequences, topk_running_beam_indices = self._get_top_k_continuations(
 accumulated_log_probs=log_probs,
 running_sequences=running_sequences,
 running_beam_indices=running_beam_indices,
 cur_len=cur_len,
 decoder_prompt_len=decoder_prompt_len,
 do_sample=do_sample,
 beams_to_keep=beams_to_keep,
 num_beams=num_beams,
 vocab_size=vocab_size,
 batch_size=batch_size,
 )
# d. Check which running sequences have finished
 next_token_hits_stopping_criteria = stopping_criteria(
 self._flatten_beam_dim(topk_running_sequences[:, :, : cur_len + 1]), # remove unfilled token indexes
 all_scores,
 )
 next_token_hits_stopping_criteria = self._unflatten_beam_dim(
 next_token_hits_stopping_criteria, batch_size, beams_to_keep
 )
# e. Get the non-finished running `num_beams` sequences for the next generation step
 running_sequences, running_beam_scores, running_beam_indices = self._get_running_beams_for_next_iteration(
 topk_log_probs=topk_log_probs,
 topk_running_sequences=topk_running_sequences,
 topk_running_beam_indices=topk_running_beam_indices,
 next_token_hits_stopping_criteria=next_token_hits_stopping_criteria,
 num_beams=num_beams,
 )
# f. Update the completed beams if a new high score in a finished sequence is found
 sequences, beam_scores, beam_indices, is_sent_finished = self._update_finished_beams(
 sequences=sequences,
 topk_running_sequences=topk_running_sequences,
 beam_scores=beam_scores,
 topk_log_probs=topk_log_probs,
 beam_indices=beam_indices,
 topk_running_beam_indices=topk_running_beam_indices,
 is_sent_finished=is_sent_finished,
 next_token_hits_stopping_criteria=next_token_hits_stopping_criteria,
 top_num_beam_mask=top_num_beam_mask,
 num_beams=num_beams,
 cur_len=cur_len,
 decoder_prompt_len=decoder_prompt_len,
 length_penalty=length_penalty,
 early_stopping=early_stopping,
 )
# g. Prepare remaining data for the next iteration, including computing the stopping condition for
 # beam search as a whole (as opposed to individual beams, i.e. `stopping_criteria`)
# pluck the cache from the beam indices that will be used in the next iteration
 if model_kwargs.get("past_key_values", None) is not None:
 model_kwargs["past_key_values"] = self._temporary_reorder_cache(
 past_key_values=model_kwargs["past_key_values"],
 beam_idx=self._flatten_beam_dim(running_beam_indices[..., cur_len - decoder_prompt_len]),
 )
cur_len = cur_len + 1
 this_peer_finished = not self._beam_search_has_unfinished_sequences(
 running_beam_scores,
 beam_scores,
 is_sent_finished,
 next_token_hits_stopping_criteria,
 cur_len,
 max_length,
 decoder_prompt_len,
 early_stopping,
 length_penalty,
 )
# 5. prepare outputs
 # Take best beams for each batch (the score is sorted in descending order)
 sequences = self._flatten_beam_dim(sequences[:, :num_return_sequences, :])
 beam_scores = self._flatten_beam_dim(beam_scores[:, :num_return_sequences])
 beam_indices = self._flatten_beam_dim(beam_indices[:, :num_return_sequences, :])
# Crop the static-shaped tensors to the actual size.
 # `beam_indices` is initialized with -1s, and is updated with the beam index of the generated token at each
 # step. We can use it to detect the generated length, which may be != `cur_len` (e.g. selected beam is from a
 # previous decoding iteration)
 max_generated_length = ((beam_indices + 1).bool()).sum(dim=1).max()
 output_length = decoder_prompt_len + max_generated_length
 sequences = sequences[:, :output_length]
 beam_indices = beam_indices[:, :max_generated_length]
if return_dict_in_generate:
 if not output_scores:
 beam_scores = None
if self.config.is_encoder_decoder:
 return GenerateBeamEncoderDecoderOutput(
 sequences=sequences,
 sequences_scores=beam_scores,
 scores=all_scores,
 logits=raw_logits,
 beam_indices=beam_indices,
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateBeamDecoderOnlyOutput(
 sequences=sequences,
 sequences_scores=beam_scores,
 scores=all_scores,
 logits=raw_logits,
 beam_indices=beam_indices,
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return sequences
def _group_beam_search(
 self,
 input_ids: torch.LongTensor,
 beam_scorer: BeamScorer,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 **model_kwargs,
 ):
 r"""
 Generates sequences of token ids for models with a language modeling head using **diverse beam search
 decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
 The sequence used as a prompt for the generation.
 beam_scorer (`BeamScorer`):
 An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
 sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 model_kwargs:
 Additional model specific kwargs that will be forwarded to the `forward` function of the model. If
 model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or
 `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
 # init values
 pad_token_id = generation_config._pad_token_tensor
 eos_token_id = generation_config._eos_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
num_beams = beam_scorer.num_beams
 num_beam_groups = beam_scorer.num_beam_groups
 num_sub_beams = num_beams // num_beam_groups
 batch_size = len(beam_scorer._beam_hyps) // num_beam_groups
 device = input_ids.device
batch_beam_size, cur_len = input_ids.shape
 model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
if return_dict_in_generate and output_scores:
 beam_indices = [tuple(() for _ in range(num_sub_beams * batch_size)) for _ in range(num_beam_groups)]
 else:
 beam_indices = None
if num_beams * batch_size != batch_beam_size:
 raise ValueError(
 f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
 )
# init attention / hidden states / scores tuples
 scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# initialise score of first beam of each group with 0 and the rest with -1e9. This ensures that the beams in
 # the same group don't produce same tokens every time.
 beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
 beam_scores[:, ::num_sub_beams] = 0
 beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False
decoder_prompt_len = input_ids.shape[1] # record the prompt length of decoder
 while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 # predicted tokens in cur_len step
 current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
# indices which will form the beams in the next time step
 reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
# do one decoder step on all beams of all sentences in batch
 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# prepare variable output controls (note: some models won't accept all output controls)
 model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
 model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
outputs = self(**model_inputs, return_dict=True)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 cur_len = cur_len + 1
 continue
if output_scores:
 processed_score = torch.zeros_like(outputs.logits[:, -1, :])
 if output_logits:
 # Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration
 # (the clone itself is always small)
 raw_logit_score = outputs.logits[:, -1, :].to(copy=True, device=input_ids.device)
for beam_group_idx in range(num_beam_groups):
 group_start_idx = beam_group_idx * num_sub_beams
 group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
 group_size = group_end_idx - group_start_idx
# indices of beams of current group among all sentences in batch
 batch_group_indices = []
for batch_idx in range(batch_size):
 batch_group_indices.extend(
 [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
 )
 group_input_ids = input_ids[batch_group_indices]
# select outputs of beams of current group only
 # No need to clone() the logits here as they will not retain outputs.logits at the end of the loop
 # .float() is needed to retain precision for later logits manipulations
 next_token_logits = outputs.logits[batch_group_indices, -1, :].to(
 dtype=torch.float32, device=input_ids.device
 )
next_token_scores = nn.functional.log_softmax(
 next_token_logits, dim=-1
 ) # (batch_size * group_size, vocab_size)
 vocab_size = next_token_scores.shape[-1]
next_token_scores_processed = logits_processor(
 group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
 )
 next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
 next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
if output_scores:
 processed_score[batch_group_indices] = next_token_scores_processed
# reshape for beam search
 next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
# Sample 1 + len(eos_token_id) next tokens for each beam so we have at least 1 non eos token per beam.
 n_eos_tokens = eos_token_id.shape[0] if eos_token_id is not None else 0
 next_token_scores, next_tokens = torch.topk(
 next_token_scores, max(2, 1 + n_eos_tokens) * group_size, dim=1, largest=True, sorted=True
 )
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
 next_tokens = next_tokens % vocab_size
# stateless
 process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
 beam_outputs = beam_scorer.process(
 group_input_ids,
 next_token_scores,
 next_tokens,
 next_indices,
 pad_token_id=pad_token_id,
 eos_token_id=eos_token_id,
 beam_indices=process_beam_indices,
 group_index=beam_group_idx,
 decoder_prompt_len=decoder_prompt_len,
 )
 beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
 beam_next_tokens = beam_outputs["next_beam_tokens"]
 beam_idx = beam_outputs["next_beam_indices"]
if return_dict_in_generate and output_scores:
 beam_indices[beam_group_idx] = tuple(
 beam_indices[beam_group_idx][beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices[0]))
 )
input_ids[batch_group_indices] = group_input_ids[beam_idx]
 group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
 current_tokens[batch_group_indices] = group_input_ids[:, -1]
# (beam_idx // group_size) -> batch_idx
 # (beam_idx % group_size) -> offset of idx inside the group
 reordering_indices[batch_group_indices] = (
 num_beams * torch.div(beam_idx, group_size, rounding_mode="floor")
 + group_start_idx
 + (beam_idx % group_size)
 )
# Store scores, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_scores:
 scores += (processed_score,)
 if output_logits:
 raw_logits += (raw_logit_score,)
 if output_attentions:
 decoder_attentions += (
 (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (outputs.hidden_states,)
 )
input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
# This is needed to properly delete outputs.logits which may be very large for first iteration
 # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
 # IMPORTANT: Note that this should appear BEFORE the call to _reorder_cache() to save the maximum memory
 # (that way the memory peak does not include outputs.logits)
 del outputs
if model_kwargs.get("past_key_values", None) is not None:
 model_kwargs["past_key_values"] = self._temporary_reorder_cache(
 model_kwargs["past_key_values"], reordering_indices
 )
# increase cur_len
 cur_len = cur_len + 1
if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
 this_peer_finished = True
final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
 sequence_outputs = beam_scorer.finalize(
 input_ids,
 beam_scores,
 next_tokens,
 next_indices,
 pad_token_id=pad_token_id,
 eos_token_id=eos_token_id,
 max_length=stopping_criteria.max_length,
 beam_indices=final_beam_indices,
 decoder_prompt_len=decoder_prompt_len,
 )
if return_dict_in_generate:
 if not output_scores:
 sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
 return GenerateBeamEncoderDecoderOutput(
 sequences=sequence_outputs["sequences"],
 sequences_scores=sequence_outputs["sequence_scores"],
 scores=scores,
 logits=raw_logits,
 beam_indices=sequence_outputs["beam_indices"],
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateBeamDecoderOnlyOutput(
 sequences=sequence_outputs["sequences"],
 sequences_scores=sequence_outputs["sequence_scores"],
 scores=scores,
 logits=raw_logits,
 beam_indices=sequence_outputs["beam_indices"],
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return sequence_outputs["sequences"]
def _constrained_beam_search(
 self,
 input_ids: torch.LongTensor,
 constrained_beam_scorer: ConstrainedBeamSearchScorer,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 **model_kwargs,
 ) -> Union[GenerateBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **constrained beam search
 decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
 The sequence used as a prompt for the generation.
 constrained_beam_scorer (`ConstrainedBeamSearchScorer`):
 A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
 sorted during generation, while satisfying a list of positive constraints. For more information, the
 documentation of [`ConstrainedBeamSearchScorer`] should be read.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 model_kwargs:
 Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
 an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or
 `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
 # init values
 pad_token_id = generation_config._pad_token_tensor
 eos_token_id = generation_config._eos_token_tensor
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
batch_size = len(constrained_beam_scorer._beam_hyps)
 num_beams = constrained_beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape[:2]
 model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
if num_beams * batch_size != batch_beam_size:
 raise ValueError(
 f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
 )
# init attention / hidden states / scores tuples
 scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 beam_indices = (
 tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
 )
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
 # of the first beam are considered to avoid sampling the exact same tokens across all beams.
 beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
 beam_scores[:, 1:] = -1e9
 beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False
decoder_prompt_len = input_ids.shape[1] # record the prompt length of decoder
 while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# prepare variable output controls (note: some models won't accept all output controls)
 model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
 model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
outputs = self(**model_inputs, return_dict=True)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 )
 if synced_gpus and this_peer_finished:
 cur_len = cur_len + 1
 continue
# Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration
 # (the clone itself is always small)
 # .float() is needed to retain precision for later logits manipulations
 next_token_logits = outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=input_ids.device)
 next_token_scores = nn.functional.log_softmax(
 next_token_logits, dim=-1
 ) # (batch_size * num_beams, vocab_size)
next_token_scores_processed = logits_processor(input_ids, next_token_scores)
next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(
 next_token_scores_processed
 )
scores_for_all_vocab = next_token_scores.clone()
# Store scores, attentions and hidden_states when required
 if return_dict_in_generate:
 if output_scores:
 scores += (next_token_scores,)
 if output_logits:
 raw_logits += (next_token_logits,)
 if output_attentions:
 decoder_attentions += (
 (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
 )
 if self.config.is_encoder_decoder:
 cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
 decoder_hidden_states += (
 (outputs.decoder_hidden_states,)
 if self.config.is_encoder_decoder
 else (outputs.hidden_states,)
 )
# reshape for beam search
 vocab_size = next_token_scores.shape[-1]
 next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
# Sample 1 + len(eos_token_id) next tokens for each beam so we have at least 1 non eos token per beam.
 n_eos_tokens = eos_token_id.shape[0] if eos_token_id is not None else 0
 next_token_scores, next_tokens = torch.topk(
 next_token_scores, max(2, 1 + n_eos_tokens) * num_beams, dim=1, largest=True, sorted=True
 )
next_indices = (next_tokens / vocab_size).long()
 next_tokens = next_tokens % vocab_size
# stateless
 beam_outputs = constrained_beam_scorer.process(
 input_ids,
 next_token_scores,
 next_tokens,
 next_indices,
 scores_for_all_vocab,
 pad_token_id=pad_token_id,
 eos_token_id=eos_token_id,
 beam_indices=beam_indices,
 decoder_prompt_len=decoder_prompt_len,
 )
 beam_scores = beam_outputs["next_beam_scores"]
 beam_next_tokens = beam_outputs["next_beam_tokens"]
 beam_idx = beam_outputs["next_beam_indices"]
input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
# This is needed to properly delete outputs.logits which may be very large for first iteration
 # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
 # IMPORTANT: Note that this should appear BEFORE the call to _reorder_cache() to save the maximum memory
 # (that way the memory peak does not include outputs.logits)
 del outputs
if model_kwargs.get("past_key_values", None) is not None:
 model_kwargs["past_key_values"] = self._temporary_reorder_cache(
 model_kwargs["past_key_values"], beam_idx
 )
if return_dict_in_generate and output_scores:
 beam_indices = tuple(beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices)))
# increase cur_len
 cur_len = cur_len + 1
if constrained_beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
 this_peer_finished = True
sequence_outputs = constrained_beam_scorer.finalize(
 input_ids,
 beam_scores,
 next_tokens,
 next_indices,
 pad_token_id=pad_token_id,
 eos_token_id=eos_token_id,
 max_length=stopping_criteria.max_length,
 beam_indices=beam_indices,
 decoder_prompt_len=decoder_prompt_len,
 )
if return_dict_in_generate:
 if not output_scores:
 sequence_outputs["sequence_scores"] = None
 if self.config.is_encoder_decoder:
 return GenerateBeamEncoderDecoderOutput(
 sequences=sequence_outputs["sequences"],
 sequences_scores=sequence_outputs["sequence_scores"],
 scores=scores,
 logits=raw_logits,
 beam_indices=sequence_outputs["beam_indices"],
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateBeamDecoderOnlyOutput(
 sequences=sequence_outputs["sequences"],
 sequences_scores=sequence_outputs["sequence_scores"],
 scores=scores,
 logits=raw_logits,
 beam_indices=sequence_outputs["beam_indices"],
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return sequence_outputs["sequences"]
def _assisted_decoding(
 self,
 input_ids: torch.LongTensor,
 candidate_generator: CandidateGenerator,
 logits_processor: LogitsProcessorList,
 stopping_criteria: StoppingCriteriaList,
 generation_config: GenerationConfig,
 synced_gpus: bool,
 streamer: Optional["BaseStreamer"],
 **model_kwargs,
 ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
 r"""
 Generates sequences of token ids for models with a language modeling head using **greedy decoding** or
 **sample** (depending on `do_sample`), assisted by candidate sequences. Assisted generation is an example of a
 candidate decoding strategy. Can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text
 models.
Parameters:
 input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
 The sequence used as a prompt for the generation.
 candidate_generator (`CandidateGenerator`):
 A derived instance of [`CandidateGenerator`] that defines how candidate sequences are generated. For
 more information, the documentation of [`CandidateGenerator`] should be read.
 logits_processor (`LogitsProcessorList`):
 An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
 used to modify the prediction scores of the language modeling head applied at each generation step.
 stopping_criteria (`StoppingCriteriaList`):
 An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
 used to tell if the generation loop should stop.
 generation_config ([`~generation.GenerationConfig`]):
 The generation configuration to be used as parametrization of the decoding method.
 synced_gpus (`bool`):
 Whether to continue running the while loop until max_length (needed to avoid deadlocking with
 `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
 streamer (`BaseStreamer`, *optional*):
 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
 model_kwargs:
 Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
 If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
 [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or
 `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
 [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
 `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
 `model.config.is_encoder_decoder=True`.
 """
 # init values
 do_sample = generation_config.do_sample
 output_attentions = generation_config.output_attentions
 output_hidden_states = generation_config.output_hidden_states
 output_scores = generation_config.output_scores
 output_logits = generation_config.output_logits
 return_dict_in_generate = generation_config.return_dict_in_generate
# init attention / hidden states / scores tuples
 scores = () if (return_dict_in_generate and output_scores) else None
 raw_logits = () if (return_dict_in_generate and output_logits) else None
 decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
 cross_attentions = () if (return_dict_in_generate and output_attentions) else None
 decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
 if return_dict_in_generate and self.config.is_encoder_decoder:
 encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
 encoder_hidden_states = (
 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 )
# keep track of which sequences are already finished
 batch_size, cur_len = input_ids.shape[:2]
 unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
 model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
this_peer_finished = False
 is_first_iteration = True # to preserve the same API in the output as other generation methods
 while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
 cur_len = input_ids.shape[1]
# 1. Fetch candidate sequences from a `CandidateGenerator` and move to the correct device
 candidate_input_ids, candidate_logits = candidate_generator.get_candidates(input_ids)
 candidate_input_ids = candidate_input_ids.to(self.device)
 if candidate_logits is not None:
 candidate_logits = candidate_logits.to(self.device)
candidate_length = candidate_input_ids.shape[1] - input_ids.shape[1]
 is_done_candidate = stopping_criteria(candidate_input_ids, None)
# 2. Use the original model to obtain the next token logits given the candidate sequence. We obtain
 # `candidate_length + 1` relevant logits from this process: in the event that all candidates are correct,
 # we use this forward pass to also pick the subsequent logits in the original model.
# 2.1. Prepare the model inputs
 candidate_kwargs = copy.copy(model_kwargs)
 candidate_kwargs = _prepare_attention_mask(
 candidate_kwargs, candidate_input_ids.shape[1], self.config.is_encoder_decoder
 )
 candidate_kwargs = _prepare_token_type_ids(candidate_kwargs, candidate_input_ids.shape[1])
 if "cache_position" in candidate_kwargs:
 candidate_kwargs["cache_position"] = torch.cat(
 (
 candidate_kwargs["cache_position"],
 torch.arange(cur_len, cur_len + candidate_length, device=input_ids.device, dtype=torch.long),
 ),
 dim=0,
 )
model_inputs = self.prepare_inputs_for_generation(candidate_input_ids, **candidate_kwargs)
 if "logits_to_keep" in model_inputs:
 model_inputs["logits_to_keep"] = candidate_length + 1
# 2.2. Run a forward pass on the candidate sequence
 # prepare variable output controls (note: some models won't accept all output controls)
 model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
 model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
outputs = self(**model_inputs)
# 2.3. Process the new logits
 # .float() is needed to retain precision for later logits manipulations
 new_logits = outputs.logits[:, -candidate_length - 1 :].to(
 dtype=torch.float32, device=input_ids.device
 ) # excludes the input prompt if present
 next_token_logits = new_logits.clone()
 if len(logits_processor) > 0:
 for i in range(candidate_length + 1):
 new_logits[:, i, :] = logits_processor(candidate_input_ids[:, : cur_len + i], new_logits[:, i, :])
# 3. Select the accepted tokens. There are two possible cases:
 # Case 1: `do_sample=True` and we have logits for the candidates (originally from speculative decoding)
 # πŸ‘‰ Apply algorithm 1 from the speculative decoding paper (https://huggingface.co/papers/2211.17192).
 if do_sample and candidate_logits is not None:
 valid_tokens, n_matches = _speculative_sampling(
 candidate_input_ids,
 candidate_logits,
 candidate_length,
 new_logits,
 is_done_candidate,
 )
# Case 2: all other cases (originally from assisted generation) πŸ‘‰ Compare the tokens selected from the
 # original model logits with the candidate tokens. We can keep the candidate tokens until the first
 # mismatch, or until the max length is reached.
 else:
 if do_sample:
 probs = new_logits.softmax(dim=-1)
 selected_tokens = torch.multinomial(probs[0, :, :], num_samples=1).squeeze(1)[None, :]
 else:
 selected_tokens = new_logits.argmax(dim=-1)
candidate_new_tokens = candidate_input_ids[:, cur_len:]
 n_matches = ((~(candidate_new_tokens == selected_tokens[:, :-1])).cumsum(dim=-1) < 1).sum()
# Ensure we don't generate beyond max_len or an EOS token
 if is_done_candidate and n_matches == candidate_length:
 n_matches -= 1
 valid_tokens = selected_tokens[:, : n_matches + 1]
# 4. Update variables according to the number of matching assistant tokens. Remember: the token generated
 # by the model after the last candidate match is also valid, as it is generated from a correct sequence.
 # Because of this last token, assisted generation search reduces to a normal greedy search/sample if there
 # is no match.
# 4.1. Get the valid continuation, after the matching tokens
 input_ids = torch.cat((input_ids, valid_tokens), dim=-1)
 if streamer is not None:
 streamer.put(valid_tokens.cpu())
 new_cur_len = input_ids.shape[1]
# 4.2. Discard past key values relative to unused assistant tokens
 new_cache_size = new_cur_len - 1
 outputs.past_key_values = _crop_past_key_values(self, outputs.past_key_values, new_cache_size)
# 5. Update the candidate generation strategy if needed
 candidate_generator.update_candidate_strategy(input_ids, new_logits, n_matches)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
 model_kwargs = self._update_model_kwargs_for_generation(
 outputs,
 model_kwargs,
 is_encoder_decoder=self.config.is_encoder_decoder,
 num_new_tokens=n_matches + 1,
 )
 if synced_gpus and this_peer_finished:
 continue
# Store scores, attentions and hidden_states when required
 # Assistant: modified to append one tuple element per token, as in the other generation methods.
 if return_dict_in_generate:
 newly_added_length = n_matches + 1
 if output_scores:
 scores += tuple(new_logits[:, i, :] for i in range(newly_added_length))
 if output_logits:
 raw_logits += tuple(next_token_logits[:, i, :] for i in range(newly_added_length))
newly_added_length = new_cur_len if is_first_iteration else newly_added_length
 if output_attentions:
 if self.config.is_encoder_decoder:
 cross_attentions = _split_model_outputs(
 cross_attentions, outputs.cross_attentions, cur_len, newly_added_length
 )
 decoder_attentions = _split_model_outputs(
 decoder_attentions,
 outputs.decoder_attentions,
 cur_len,
 newly_added_length,
 is_decoder_attention=True,
 )
 # some (V)LLMs have hard requirement on SDPA and thus never return attn
 elif outputs.attentions[0] is not None:
 decoder_attentions = _split_model_outputs(
 decoder_attentions,
 outputs.attentions,
 cur_len,
 newly_added_length,
 is_decoder_attention=True,
 )
 if output_hidden_states:
 if self.config.is_encoder_decoder:
 decoder_hidden_states = _split_model_outputs(
 decoder_hidden_states, outputs.decoder_hidden_states, cur_len, newly_added_length
 )
 else:
 decoder_hidden_states = _split_model_outputs(
 decoder_hidden_states, outputs.hidden_states, cur_len, newly_added_length
 )
unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
 this_peer_finished = unfinished_sequences.max() == 0
 is_first_iteration = False
if streamer is not None:
 streamer.end()
if (
 hasattr(candidate_generator, "assistant_model")
 and candidate_generator.assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic"
 ):
 candidate_generator.assistant_model.generation_config.num_assistant_tokens = (
 candidate_generator.num_assistant_tokens
 )
 if return_dict_in_generate:
 if self.config.is_encoder_decoder:
 return GenerateEncoderDecoderOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 encoder_attentions=encoder_attentions,
 encoder_hidden_states=encoder_hidden_states,
 decoder_attentions=decoder_attentions,
 cross_attentions=cross_attentions,
 decoder_hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return GenerateDecoderOnlyOutput(
 sequences=input_ids,
 scores=scores,
 logits=raw_logits,
 attentions=decoder_attentions,
 hidden_states=decoder_hidden_states,
 past_key_values=model_kwargs.get("past_key_values"),
 )
 else:
 return input_ids
def _prefill_chunking(self, input_ids: torch.LongTensor, generation_config: GenerationConfig, **model_kwargs):
 # Even if we are not compiling the forward, flex is always compiled when used. With chunk prefill, we may
 # end up needing just a bit more graphs than the default (which is 8). Doing this avoids very cryptic warnings
 torch._dynamo.config.cache_size_limit = 64
chunk_size = generation_config.prefill_chunk_size
 # Only chunk up the token just before last, so that decoding is completely performed outside this function
 # (here we simply prefill the cache)
 input_chunks = torch.split(input_ids[:, :-1], chunk_size, dim=-1)
if "past_key_values" not in model_kwargs:
 raise ValueError("Cannot use prefill chunking without a cache")
model_forward = self.forward
compile_forward = self._valid_auto_compile_criteria(model_kwargs, generation_config)
 if compile_forward:
 model_forward = self.get_compiled_call(generation_config.compile_config)
attention_mask = model_kwargs.pop("attention_mask", None)
past_length = 0
 for input_chunk in input_chunks:
 current_length = past_length + input_chunk.shape[-1]
 # Prepare inputs
 if attention_mask is not None:
 model_kwargs["attention_mask"] = attention_mask[:, :current_length]
 model_kwargs["cache_position"] = torch.arange(
 past_length, current_length, dtype=torch.long, device=input_chunk.device
 )
 model_kwargs["position_ids"] = model_kwargs["cache_position"].unsqueeze(0)
 model_inputs = self.prepare_inputs_for_generation(input_chunk, **model_kwargs)
outputs = model_forward(**model_inputs, return_dict=True)
model_kwargs["past_key_values"] = outputs.past_key_values
 past_length = current_length
model_kwargs["attention_mask"] = attention_mask
 model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + 1
 _ = model_kwargs.pop("position_ids", None)
return model_kwargs
def _speculative_sampling(
 candidate_input_ids,
 candidate_logits,
 candidate_length,
 new_logits,
 is_done_candidate,
 ):
 """
 Applies sampling as in the speculative decoding paper (https://huggingface.co/papers/2211.17192, algorithm 1). Returns
 the selected tokens, as well as the number of candidate matches.
NOTE: Unless otherwise stated, the variable names match those in the paper.
 """
 new_candidate_input_ids = candidate_input_ids[:, -candidate_length:]
 # Gets the probabilities from the logits. q_i and p_i denote the assistant and model probabilities of the tokens
 # selected by the assistant, respectively.
 q = candidate_logits.softmax(dim=-1)
 q_i = q[:, torch.arange(candidate_length), new_candidate_input_ids].squeeze(0, 1)
 p = new_logits.softmax(dim=-1)
 p_i = p[:, torch.arange(candidate_length), new_candidate_input_ids].squeeze(0, 1)
 probability_ratio = p_i / q_i
# When probability_ratio > 1 (i.e. q_i(x) < p_i(x), or "assistant probability of the candidate token is smaller
 # than the model probability for the same token"), keep the token. Otherwise reject with p = 1 - probability_ratio
 # (= keep with p = probability_ratio). Keep all the tokens until the first rejection
 r_i = torch.rand_like(probability_ratio)
 is_accepted = r_i <= probability_ratio
 n_matches = ((~is_accepted).cumsum(dim=-1) < 1).sum() # this is `n` in algorithm 1
# Ensure we don't generate beyond max_len or an EOS token (not in algorithm 1, but needed for correct behavior)
 if is_done_candidate and n_matches == candidate_length:
 # Output length is assumed to be `n_matches + 1`. Since we won't generate another token with the target model
 # due to acceptance on EOS we fix `n_matches`
 n_matches -= 1
 valid_tokens = new_candidate_input_ids[:, : n_matches + 1]
 else:
 # Next token selection: if there is a rejection, adjust the distribution from the main model before sampling.
 gamma = candidate_logits.shape[1]
 p_n_plus_1 = p[:, n_matches, :]
 if n_matches < gamma:
 q_n_plus_1 = q[:, n_matches, :]
 p_prime = torch.clamp((p_n_plus_1 - q_n_plus_1), min=0)
 p_prime.div_(p_prime.sum())
 else:
 p_prime = p_n_plus_1
 t = torch.multinomial(p_prime, num_samples=1).squeeze(1)[None, :]
# The selected tokens include the matches (if any) plus the next sampled tokens
 if n_matches > 0:
 valid_tokens = torch.cat((new_candidate_input_ids[:, :n_matches], t), dim=-1)
 else:
 valid_tokens = t
return valid_tokens, n_matches
def _split_model_outputs(outputs, new_outputs, cur_len, added_len, is_decoder_attention=False):
 """
 Given the (decoder/cross attentions)/(decoder hidden states) for multiple generated tokens, splits it into a tuple
 where each member corresponds to a single generated token.
 """
 # Retrocompatibility: in our generation functions, the first iteration includes the attention/hidden states for the
 # prompt.
 if len(outputs) == 0:
 new_tuple = ()
 for layer in new_outputs:
 last_dim_size = cur_len if is_decoder_attention else layer.shape[-1]
 new_tuple += (layer[..., :cur_len, :last_dim_size],)
 outputs += (new_tuple,)
 # The first iteration contains the prompt + 1 generated token, let's update the length variables accordingly
 cur_len += 1
 added_len -= cur_len
for i in range(added_len):
 new_tuple = ()
 for layer in new_outputs:
 last_dim_size = cur_len + i if is_decoder_attention else layer.shape[-1]
 new_tuple += (layer[..., i : i + 1, :last_dim_size],)
 outputs += (new_tuple,)
 return outputs
def _ranking_fast(
 context_hidden: torch.FloatTensor,
 next_hidden: torch.FloatTensor,
 next_top_k_probs: torch.FloatTensor,
 cosine_matrix_mask: torch.LongTensor,
 alpha: float,
 beam_width: int,
 ) -> torch.FloatTensor:
 """
 Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
 in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
 row in the batch.
 """
 norm_context_hidden = context_hidden / context_hidden.norm(dim=2, keepdim=True)
 norm_next_hidden = next_hidden / next_hidden.norm(dim=2, keepdim=True)
 cosine_matrix = torch.matmul(norm_context_hidden, norm_next_hidden.transpose(1, 2)).squeeze(-1) # [B*K, S]
# Penalize cosine_matrix based on the cosine_matrix_mask (ignore padding positions)
 # Using a large negative value for masked positions
 cosine_matrix_mask = cosine_matrix_mask.to(dtype=cosine_matrix.dtype)
 cosine_matrix_mask = (1 - cosine_matrix_mask) * torch.finfo(cosine_matrix.dtype).min
 cosine_matrix = cosine_matrix + cosine_matrix_mask
degeneration_penalty, _ = torch.max(cosine_matrix, dim=-1) # [B*K]
 next_top_k_probs = next_top_k_probs.view(-1) # [B*K]
 contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
 contrastive_score = torch.stack(torch.split(contrastive_score, beam_width)) # [B, K]
 _, selected_idx = contrastive_score.max(dim=-1) # [B]
 return selected_idx
def _split(data, full_batch_size: int, split_size: int):
 """
 Takes care of three cases:
 1. data is a tensor: e.g. last_hidden_state, pooler_output etc. split them on the batch_size dim
 2. data is a tuple: e.g. hidden_states, attentions etc. Keep the tuple as it is and split each tensor in it and
 return a list of tuples
 3. data is a tuple of tuples, e.g. past_key_values. Keep the tuple as it is and split each tuple in it and
 return a list of tuples of tuples
 (see documentation of ModelOutput)
 """
 if data is None:
 return [None] * (full_batch_size // split_size)
 if isinstance(data, torch.Tensor):
 return [data[i : i + split_size] for i in range(0, full_batch_size, split_size)]
 # New cache format
 elif isinstance(data, DynamicCache) or (
 isinstance(data, EncoderDecoderCache) and isinstance(data.self_attention_cache, DynamicCache)
 ):
 return data.batch_split(full_batch_size, split_size)
 elif isinstance(data, tuple):
 # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
 if isinstance(data[0], tuple):
 return [
 tuple(tuple(tensor[i : i + split_size] for tensor in inner_tuple) for inner_tuple in data)
 for i in range(0, full_batch_size, split_size)
 ]
else:
 return [
 tuple(sub_tensor[i : i + split_size] for sub_tensor in data)
 for i in range(0, full_batch_size, split_size)
 ]
 else:
 raise TypeError(f"Unexpected attribute type: {type(data)}")
def _split_model_inputs(
 model_input: Union[ModelOutput, dict], split_size: int, full_batch_size: int, config: PretrainedConfig
 ) -> list[Union[ModelOutput, dict]]:
 """
 Split a ModelOutput object (or its subclasses) or Dict into a list of same-class objects based on a specified split
 size. The input object is dict when it was prepared for forward pass and ModelOutput when it was returned from
 previous forward pass.
 """
 # Edge case: if model_input is None, return a list of Nones
 # this happens with Whisper where encoder_outputs is None
 if model_input is None:
 return [model_input] * (full_batch_size // split_size)
 # Infer the class from the object
 model_output_cls = type(model_input)
 if (full_batch_size % split_size) != 0:
 raise ValueError("`full_batch_size` must be divisible by `split_size`")
if split_size > full_batch_size:
 raise ValueError("`split_size` must be smaller or equal to `full_batch_size`")
# Helper function to split tensors or tuples of tensors
# Find all the dataclass fields (e.g., last_hidden_state, pooler_output etc.) and split them
 keys = (
 model_input.__dataclass_fields__.keys() if hasattr(model_input, "__dataclass_fields__") else model_input.keys()
 )
 # We only keep keys that are in the model_input
 keys = [k for k in keys if k in model_input]
 # Here we can have four types of values: tensors, tuples of tensors and booleans, and encoder_outputs which is a
 # ModelOutput object.
 # bool should not be split but replicated for each split
 bool_keys = [k for k in keys if isinstance(model_input[k], bool) or k == "cache_position"]
 keys_to_ignore = ["cache_position", "encoder_outputs", "logits_to_keep"]
 non_bool_keys = [k for k in keys if not isinstance(model_input[k], bool) and k not in keys_to_ignore]
# we split the tensors and tuples of tensors
 data_split_list = [
 {k: _split(model_input[k], full_batch_size, split_size)[i] for k in non_bool_keys}
 for i in range(full_batch_size // split_size)
 ]
 # bool values are the same and replicated for each split
 bool_data = {k: model_input[k] for k in bool_keys}
 # encoder_outputs is a ModelOutput object and should be split by its own
 if "encoder_outputs" in model_input:
 encoder_outputs_split = _split_model_inputs(
 model_input["encoder_outputs"], split_size, full_batch_size, config.get_text_config()
 )
 data_split_list = [
 {**data_split, "encoder_outputs": encoder_outputs_split[i]} for i, data_split in enumerate(data_split_list)
 ]
 # logits_to_keep should be replicated for each split, similar to bool values
 if "logits_to_keep" in model_input:
 data_split_list = [
 {**data_split, "logits_to_keep": model_input["logits_to_keep"]} for data_split in data_split_list
 ]
# Convert each dictionary in the list to an object of the inferred class
 split_model_inputs: list[Union[ModelOutput, dict]] = [
 model_output_cls(**data_split, **bool_data) for data_split in data_split_list
 ]
return split_model_inputs
def stack_model_outputs(model_outputs: list[ModelOutput], config: PretrainedConfig) -> ModelOutput:
 """
 Stack a list of ModelOutput objects (or its subclasses) along the batch_size dimension. The function infers the
 specific ModelOutput subclass from the list provided.
 """
 if not model_outputs:
 raise ValueError("Input list is empty.")
# Infer the class from the first object in the list
 model_output_cls = type(model_outputs[0])
# Ensure all objects are of the same type
 if not all(isinstance(obj, model_output_cls) for obj in model_outputs):
 raise ValueError("All elements in the list should be of the same type.")
# Helper function to concat tensors or tuples of tensors
 def _concat(data):
 """
 Reverse of `_split` function above.
 """
 if any(data is None for data in data):
 return None
 if isinstance(data[0], torch.Tensor):
 return torch.cat(data, dim=0)
 # New cache format
 elif isinstance(data[0], DynamicCache):
 return DynamicCache.from_batch_splits(data)
 elif isinstance(data[0], EncoderDecoderCache):
 return EncoderDecoderCache.from_batch_splits(data)
 elif isinstance(data[0], tuple):
 # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
 if isinstance(data[0][0], tuple):
 return tuple(
 tuple(torch.cat([attr[i][j] for attr in data], dim=0) for j in range(len(data[0][0])))
 for i in range(len(data[0]))
 )
 else:
 return tuple(torch.cat([attr[i] for attr in data], dim=0) for i in range(len(data[0])))
 elif isinstance(data[0], (int, float)):
 # If the elements are integers or floats, return a tensor
 return torch.tensor(data)
 else:
 raise TypeError(f"Unexpected attribute type: {type(data[0])}")
# Use a dictionary comprehension to gather attributes from all objects and concatenate them
 concatenated_data = {
 k: _concat([getattr(model_output, k) for model_output in model_outputs])
 for k in model_output_cls.__dataclass_fields__.keys()
 }
# Return a new object of the inferred class with the concatenated attributes
 return model_output_cls(**concatenated_data)
def _relative_top_filter(
 scores: torch.FloatTensor,
 baseline_scores: torch.FloatTensor,
 relative_top: float = 0.1,
 filter_value: float = -float("Inf"),
 base_filter_value=-1e-3,
 min_tokens_to_keep: int = 1,
 ) -> torch.FloatTensor:
 """
 Reference: https://github.com/XiangLi1999/ContrastiveDecoding/blob/170e9142e92159c1237d731e240f5eb14aabf428/transformers/src/transformers/generation_logits_process.py#L235
 Apply filtering to only keep tokens with a probability above a certain threshold. The threshold is defined as `relative_top` * max probability in the distribution.
 """
 scores_normalized = scores.log_softmax(dim=-1)
 baseline_scores_normalized = baseline_scores.log_softmax(dim=-1)
 sorted_logits, sorted_indices = torch.sort(scores_normalized, descending=True)
 min_thresh = sorted_logits[..., min_tokens_to_keep - 1]
 probs_max = torch.max(scores_normalized, dim=-1).values
 probs_thresh = probs_max + np.log(relative_top)
 probs_thresh = torch.min(min_thresh, probs_thresh)
 probs_thresh = probs_thresh.unsqueeze(-1)
 baseline_scores_normalized[scores_normalized < probs_thresh] = base_filter_value
 scores_normalized[scores_normalized < probs_thresh] = filter_value
 return scores_normalized, baseline_scores_normalized
def _dola_select_contrast(
 candidate_premature_layers: list[int],
 candidate_premature_logits: dict[int, torch.FloatTensor],
 final_logits: torch.FloatTensor,
 ) -> torch.FloatTensor:
 if len(candidate_premature_layers) == 1:
 base_logits = candidate_premature_logits[candidate_premature_layers[0]]
 final_logits, base_logits = _relative_top_filter(final_logits, base_logits)
 logits = final_logits - base_logits
 return logits
# 1. Stacking all premature_layers into a new dimension
 stacked_premature_layers = torch.stack([candidate_premature_logits[i] for i in candidate_premature_layers], dim=0)
# 2. Calculate the softmax values for mature_layer and all premature_layers
 # shape: (batch_size, vocab_size)
 softmax_mature_layer = F.softmax(final_logits, dim=-1)
 # shape: (num_premature_layers, batch_size, vocab_size)
 softmax_premature_layers = F.softmax(stacked_premature_layers, dim=-1)
# 3. Calculate the average distribution
 # shape: (num_premature_layers, batch_size, vocab_size)
 avg_dist = 0.5 * (softmax_mature_layer[None, :, :] + softmax_premature_layers)
# 4. Calculate log-softmax for the KL divergence
 # shape: (batch_size, vocab_size)
 log_softmax_mature_layer = F.log_softmax(final_logits, dim=-1)
 # shape: (num_premature_layers, batch_size, vocab_size)
 log_softmax_premature_layers = F.log_softmax(stacked_premature_layers, dim=-1)
# 5. Calculate the KL divergences and then the JS divergences
 # shape: (num_premature_layers, batch_size)
 kl1 = F.kl_div(log_softmax_mature_layer[None, :, :], avg_dist, reduction="none").mean(-1)
 # shape: (num_premature_layers, batch_size)
 kl2 = F.kl_div(log_softmax_premature_layers, avg_dist, reduction="none").mean(-1)
 js_divs = 0.5 * (kl1 + kl2) # shape: (num_premature_layers, batch_size)
# 6. Reduce the batchmean
 js_divs = js_divs.mean(-1) # shape: (num_premature_layers,)
 premature_layer = candidate_premature_layers[int(js_divs.argmax().item())]
base_logits = candidate_premature_logits[premature_layer]
 final_logits, base_logits = _relative_top_filter(final_logits, base_logits)
 logits = final_logits - base_logits
 return logits
</script>
<body class="bg-gray-900 text-gray-200 flex flex-col h-screen">
 <header class="bg-gray-800/50 backdrop-blur-sm border-b border-gray-700 p-4 shadow-lg">
 <h1 class="text-2xl font-bold text-center text-white">Bloatedness Visualizer</h1>
 <p class="text-center text-gray-400 mt-1">Paste your model in the 'Main' tab and add dependencies in other tabs.</p>
 </header>
<main class="flex-grow flex flex-col md:flex-row gap-4 p-4 overflow-hidden">
 <!-- Left Panel: Code Input & Controls -->
 <div class="md:w-1/3 flex flex-col h-full">
 <div class="flex-grow flex flex-col bg-gray-800 rounded-lg shadow-2xl border border-gray-700">
 <div class="p-4 border-b border-gray-700 flex justify-between items-center">
 <h2 class="text-lg font-semibold">Code Input</h2>
 <button id="visualize-btn" class="bg-blue-600 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded-md transition-colors duration-300">
 Visualize
 </button>
 </div>
 <div class="border-b border-gray-700 bg-gray-900/50 px-2 pt-2 flex items-center gap-2">
 <div id="tab-bar" class="flex gap-1">
 <!-- Tabs will be dynamically inserted here -->
 </div>
 <button id="add-tab-btn" class="ml-auto bg-gray-600 hover:bg-gray-500 text-white font-bold h-8 w-8 rounded-full transition-colors duration-200">+</button>
 </div>
 <div id="code-inputs-container" class="flex-grow relative">
 <!-- Textareas will be dynamically inserted here -->
 </div>
 </div>
 </div>
<!-- Right Panel: Visualization & Details -->
 <div class="md:w-2/3 flex flex-col gap-4 h-full">
 <div class="flex-grow bg-gray-800 rounded-lg shadow-2xl border border-gray-700 relative overflow-hidden">
 <div id="graph-container" class="w-full h-full"></div>
 <div id="loading-spinner" class="absolute inset-0 bg-gray-800/50 flex items-center justify-center hidden z-10">
 <svg class="animate-spin h-10 w-10 text-white" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24">
 <circle class="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" stroke-width="4"></circle>
 <path class="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path>
 </svg>
 </div>
 </div>
 <div id="details-panel" class="h-40 bg-gray-800 rounded-lg shadow-2xl border border-gray-700 p-4 flex flex-col">
 <h3 class="text-lg font-semibold border-b border-gray-700 pb-2 mb-2">Details</h3>
 <div id="details-content" class="text-gray-400 fira-code overflow-y-auto">
 <p>Click on a node in the graph to see its details. Scroll to zoom, drag to pan.</p>
 </div>
 </div>
 </div>
 </main>
<script>
 // --- DOM Element References ---
 const visualizeBtn = document.getElementById('visualize-btn');
 const graphContainer = document.getElementById('graph-container');
 const detailsContent = document.getElementById('details-content');
 const loadingSpinner = document.getElementById('loading-spinner');
 const tabBar = document.getElementById('tab-bar');
 const codeInputsContainer = document.getElementById('code-inputs-container');
 const addTabBtn = document.getElementById('add-tab-btn');
 // --- Example Code ---
 const exampleCodeMain = document.getElementById("main-code").textContent;
 const exampleCodeDeps = document.getElementById("dependencies").textContent;
 fetch("main_code.py")
 .then(res => res.text())
 .then(code => {
 const exampleCodeMain = code;
 // do something with the code
 });
 fetch("dependencies.py")
 .then(res => res.text())
 .then(code => {
 const exampleCodeDeps = code;
 // do something with the code
 });
 // --- Tab Management ---
 let tabCounter = 0;
 function addTab(name, content = '', isActive = false) {
 tabCounter++;
 const tabId = `tab-${tabCounter}`;
 const textareaId = `textarea-${tabCounter}`;
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 tabButton.id = tabId;
 tabButton.className = 'tab px-4 py-2 text-sm font-medium rounded-t-md transition-colors duration-200';
 tabButton.textContent = name;
 tabButton.dataset.textareaId = textareaId;
 tabBar.appendChild(tabButton);
 // Create Textarea
 const textarea = document.createElement('textarea');
 textarea.id = textareaId;
 textarea.className = 'fira-code w-full h-full p-4 bg-gray-900 text-gray-300 resize-none focus:outline-none absolute top-0 left-0';
 textarea.placeholder = `Paste dependency code here...`;
 textarea.value = content;
 codeInputsContainer.appendChild(textarea);
 tabButton.addEventListener('click', () => switchTab(tabId));
 if (isActive) {
 switchTab(tabId);
 } else {
 textarea.classList.add('hidden');
 }
 }
 function switchTab(tabId) {
 // Update tab buttons
 document.querySelectorAll('.tab').forEach(tab => {
 tab.classList.toggle('active', tab.id === tabId);
 });
 // Update textareas
 document.querySelectorAll('#code-inputs-container textarea').forEach(area => {
 area.classList.toggle('hidden', area.id !== document.getElementById(tabId).dataset.textareaId);
 });
 }
 addTabBtn.addEventListener('click', () => addTab(`Dep ${tabCounter}`));
 // --- Core Logic: Parser ---
 function parsePythonCode(code) {
 const nodes = [];
 const links = [];
 const nodeRegistry = new Set();
 let currentClassInfo = null;
 const lines = code.split('\n');
 lines.forEach(line => {
 const indentation = line.match(/^\s*/)[0].length;
 if (line.trim().length > 0 && indentation === 0) {
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 }
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 const className = classMatch[1];
 const parents = classMatch[2] ? classMatch[2].split(',').map(p => p.trim()) : [];
 if (!nodeRegistry.has(className)) {
 nodes.push({ id: className, type: 'class', parents: parents });
 nodeRegistry.add(className);
 } else {
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 const existingNode = nodes.find(n => n.id === className);
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 const signature = methodMatch[2];
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 links.push({ source: currentClassInfo.name, target: methodId, type: 'method' });
 }
 }
 });
 return { nodes, links };
 }
 // --- Core Logic: D3 Visualization ---
 let simulation;
 function renderGraph(data) {
 graphContainer.innerHTML = '';
 const width = graphContainer.clientWidth;
 const height = graphContainer.clientHeight;
 const svg = d3.select(graphContainer).append("svg")
 .attr("viewBox", [-width / 2, -height / 2, width, height]);
const container = svg.append("g");
 // Add zoom capabilities
 const zoom = d3.zoom()
 .scaleExtent([0.1, 4])
 .on("zoom", (event) => {
 container.attr("transform", event.transform);
 });
 svg.call(zoom);
 if (simulation) {
 simulation.stop();
 }
 simulation = d3.forceSimulation(data.nodes)
 .force("link", d3.forceLink(data.links).id(d => d.id).distance(d => d.type === 'inheritance' ? 150 : 60).strength(0.5))
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 .selectAll("line")
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 .join("line")
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 detailsContent.innerHTML = content;
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 function handleVisualize() {
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 document.querySelectorAll('#code-inputs-container textarea').forEach(area => {
 allCode += area.value + '\n';
 });
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 graphContainer.innerHTML = '<p class="p-4 text-center text-gray-400">Please paste some code to visualize.</p>';
 return;
 }
 const graphData = parsePythonCode(allCode);
 renderGraph(graphData);
 } catch (error) {
 console.error("Failed to visualize code:", error);
 graphContainer.innerHTML = `<p class="p-4 text-center text-red-400">An error occurred during parsing. Check the console for details.</p>`;
 } finally {
 loadingSpinner.classList.add('hidden');
 }
 }, 50);
 }
 // --- Event Listeners ---
 visualizeBtn.addEventListener('click', handleVisualize);
// --- Initial Load ---
 window.addEventListener('load', () => {
 addTab('Main', exampleCodeMain, true);
 addTab('Deps', exampleCodeDeps);
 handleVisualize();
 });
 window.addEventListener('resize', () => {
 let allCode = '';
 document.querySelectorAll('#code-inputs-container textarea').forEach(area => {
 allCode += area.value + '\n';
 });
 if (allCode.trim()) {
 handleVisualize();
 }
 });
 </script>
</body>
</html>