modeling_mistral_advanced.py

# coding=utf-8
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" PyTorch Mistral model."""
from termcolor import colored
from tqdm import tqdm
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import inspect
import math
import copy
import time
import warnings
from typing import List, Optional, Tuple, Union
import gc
import os
import tempfile
import random
import numpy as np
import warnings
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from matplotlib.colors import LinearSegmentedColormap, LogNorm
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from configuration_mistral_advanced import VisionEncoderDecoderConfig
from configuration_mistral_advanced import EncoderDecoderConfig
from transformers.auto.configuration_auto import AutoConfig
from transformers.auto.modeling_auto import AutoModel, AutoModelForCausalLM
from collections import defaultdict
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)



from configuration_mistral_advanced import MistralConfig


if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MistralConfig"


# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )



# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

def _make_sliding_window_causal_mask(
    input_ids_shape: torch.Size,
    dtype: torch.dtype,
    device: torch.device,
    past_key_values_length: int = 0,
    sliding_window: int = 4096,
):
    """
    Make causal mask used for sliding window attention
    """
    bsz, tgt_len = input_ids_shape

    tensor = torch.full(
        (tgt_len, tgt_len),
        fill_value=1,
        device=device,
    )
    mask = torch.tril(tensor, diagonal=0)
    # make the mask banded to account for sliding window
    mask = torch.triu(mask, diagonal=-sliding_window)
    mask = torch.log(mask).to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)

# Inverse dim formula to find dim based on number of rotations
def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))

# Find dim range bounds based on rotations
def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
    low = math.floor(_yarn_find_correction_dim(
        low_rot, dim, base, max_position_embeddings))
    high = math.ceil(_yarn_find_correction_dim(
        high_rot, dim, base, max_position_embeddings))
    return max(low, 0), min(high, dim-1)  # Clamp values just in case

def _yarn_linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func

def _yarn_get_mscale(scale=1):
    if scale <= 1:
        return 1.0
    return 0.07 * math.log(scale) + 1.0


# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
class MistralRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        MistralRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral
# TODO @Arthur no longer copied from LLama after static cache
class MistralRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:seq_len].to(dtype=x.dtype),
            self.sin_cached[:seq_len].to(dtype=x.dtype),
        )

### YARN ADDITIONS

class MistralDynamicNTKScalingRotaryEmbedding(MistralRotaryEmbedding):
    """MistralRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        if seq_len > self.max_position_embeddings:
            base = self.base * (
                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
            ) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

class MistralLinearScalingRotaryEmbedding(MistralRotaryEmbedding):
    """MistralRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
        t = t / self.scaling_factor

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.cos().to(dtype), persistent=False)

class MistralYaRNScaledRotaryEmbedding(torch.nn.Module):
    """MistralRotaryEmbedding extended with YaRN. See: https://arxiv.org/abs/2309.00071"""
    def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048,
                 extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.scale = scale
        self.original_max_position_embeddings = original_max_position_embeddings
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow

        self.yarn(device)

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings
        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        dtype = torch.get_default_dtype()

        self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False)
        self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            self.max_seq_len_cached = seq_len

            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)

            self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False)
            self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False)
        return (
            self.cos_cached[:seq_len].to(dtype=x.dtype),
            self.sin_cached[:seq_len].to(dtype=x.dtype),
        )

    def yarn(self, device):
        pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (self.scale * pos_freqs)

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation

class MistralDynamicYaRNScaledRotaryEmbedding(torch.nn.Module):
    """MistralRotaryEmbedding extended with Dynamic YaRN. See: https://arxiv.org/abs/2309.00071"""
    def __init__(self, dim, max_position_embeddings=2048, base=10000, original_max_position_embeddings=2048,
                 extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.original_max_position_embeddings = original_max_position_embeddings
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow

        if finetuned:
            self.yarn(self.max_position_embeddings / self.original_max_position_embeddings, device)
        else:
            inv_freq = 1.0 / \
                (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
            self.register_buffer("inv_freq", inv_freq, persistent=False)
            self.mscale = 1

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings
        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        dtype = torch.get_default_dtype()

        self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False)
        self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            self.max_seq_len_cached = seq_len

            self.yarn(seq_len / self.max_position_embeddings, x.device)

            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)

            self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False)
            self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False)
        return (
            self.cos_cached[:seq_len].to(dtype=x.dtype),
            self.sin_cached[:seq_len].to(dtype=x.dtype),
        )

    def yarn(self, scale, device):
        pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (scale * pos_freqs)

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation
###################




# Copied from transformers.models.llama.modeling_llama.rotate_half
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)


# copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
# TODO @Arthur no longer copied from LLama after static cache
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        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[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class MistralMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        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_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


# Copied from transformers.models.llama.modeling_llama.repeat_kv
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)


class MistralAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
    and "Generating Long Sequences with Sparse Transformers".
    """

    def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.attention_dropout = config.attention_dropout

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        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_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        self.rotary_emb = MistralRotaryEmbedding(
            self.head_dim,
            max_position_embeddings=self.max_position_embeddings,
            base=self.rope_theta,
        )

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            if self.layer_idx is None:
                raise ValueError(
                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )

            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class MistralFlashAttention2(MistralAttention):
    """
    Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ):
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

            # overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            if self.layer_idx is None:
                raise ValueError(
                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        # Because the input can be padded, the absolute sequence length depends on the max position id.
        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)

        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        use_sliding_windows = (
            _flash_supports_window_size
            and getattr(self.config, "sliding_window", None) is not None
            and kv_seq_len > self.config.sliding_window
        )

        if not _flash_supports_window_size:
            logger.warning_once(
                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
                " make sure to upgrade flash-attn library."
            )

        if past_key_value is not None:
            # Activate slicing cache only if the config has a value `sliding_windows` attribute
            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
            if (
                getattr(self.config, "sliding_window", None) is not None
                and kv_seq_len > self.config.sliding_window
                and cache_has_contents
            ):
                slicing_tokens = 1 - self.config.sliding_window

                past_key = past_key_value[self.layer_idx][0]
                past_value = past_key_value[self.layer_idx][1]

                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
                past_value = past_value[:, :, slicing_tokens:, :].contiguous()

                if past_key.shape[-2] != self.config.sliding_window - 1:
                    raise ValueError(
                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
                        f" {past_key.shape}"
                    )

                if attention_mask is not None:
                    attention_mask = attention_mask[:, slicing_tokens:]
                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)

            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        dropout_rate = 0.0 if not self.training else self.attention_dropout

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in float16 just to be sure everything works as expected.
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        # Reashape to the expected shape for Flash Attention
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        attn_output = self._flash_attention_forward(
            query_states,
            key_states,
            value_states,
            attention_mask,
            q_len,
            dropout=dropout_rate,
            use_sliding_windows=use_sliding_windows,
        )

        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    def _flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        query_length,
        dropout=0.0,
        softmax_scale=None,
        use_sliding_windows=False,
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`float`):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
            use_sliding_windows (`bool`, *optional*):
                Whether to activate sliding window attention.
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            if not use_sliding_windows:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                )
            else:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            if not use_sliding_windows:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                )
            else:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape

        # On the first iteration we need to properly re-create the padding mask
        # by slicing it on the proper place
        if kv_seq_len != attention_mask.shape[-1]:
            attention_mask_num_tokens = attention_mask.shape[-1]
            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]

        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)

        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)

        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
# TODO @Arthur no longer copied from LLama after static cache
class MistralSdpaAttention(MistralAttention):
    """
    Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from MistralAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and attention_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=attention_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
            is_causal=self.is_causal and attention_mask is None and q_len > 1,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value


MISTRAL_ATTENTION_CLASSES = {
    "eager": MistralAttention,
    "flash_attention_2": MistralFlashAttention2,
    "sdpa": MistralSdpaAttention,
}


class MistralDecoderLayer(nn.Module):
    def __init__(self, config: MistralConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)

        self.mlp = MistralMLP(config)
        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, sequence_length)` where padding elements are indicated by 0.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


MISTRAL_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MistralConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
    MISTRAL_START_DOCSTRING,
)
class MistralPreTrainedModel(PreTrainedModel):
    config_class = MistralConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MistralDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            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_()


MISTRAL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance;
            - 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)`). This is also known as the legacy
            cache format.

            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

""" Classes to support Vision-Encoder-Text-Decoder architectures"""

VISION_ENCODER_DECODER_START_DOCSTRING = r"""
    This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
    as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
    [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
    function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
    generative task, like image captioning.

    The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
    tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
    Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
    Zhou, Wei Li, Peter J. Liu.

    Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
    Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
    character recognition (OCR) yields a significant performance improvement.

    After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
    other models (see the examples for more information).

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using an image processor (e.g. if you use ViT as the encoder,
            you should use [`AutoImageProcessor`]). See [`ViTImageProcessor.__call__`] for details.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the
            right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`.
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
            This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor
            of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
            decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
            into associated vectors than the model's internal embedding lookup matrix.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
            ..., config.vocab_size]` (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]`
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
        kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:

            - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
            - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function.
"""

DEPRECATION_WARNING = (
    "Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the"
    " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if"
    " fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the"
    " labels, no need to pass them yourself anymore."
)

ENCODER_DECODER_START_DOCSTRING = r"""
    This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
    encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
    [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
    function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
    generative task, like summarization.

    The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
    tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
    Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
    Zhou, Wei Li, Peter J. Liu.

    After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models
    (see the examples for more information).

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`EncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

ENCODER_DECODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the
            right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`.
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
            This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor
            of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
            decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
            into associated vectors than the model's internal embedding lookup matrix.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
            ..., config.vocab_size]` (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]`
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
        kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:

            - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
            - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function.
"""


@add_start_docstrings(
    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
    MISTRAL_START_DOCSTRING,
)
class MistralModel(MistralPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]

    Args:
        config: MistralConfig
    """

    def __init__(self, config: MistralConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = 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

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
                    "You are attempting to perform batched generation with padding_side='right'"
                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                )

        if self._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._attn_implementation == "sdpa" and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class MistralForCausalLM(MistralPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = MistralModel(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

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = 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,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            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]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, MistralForCausalLM

        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> 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]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""

        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
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = 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,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Ensure tensors are on the same device
            shift_labels = shift_labels.to(shift_logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # Omit tokens covered by past_key_values
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past
@add_start_docstrings(
    """
    The Mistral Model transformer with a sequence classification head on top (linear layer).

    [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    MISTRAL_START_DOCSTRING,
)
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL
class MistralForSequenceClassification(MistralPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = MistralModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, 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

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = 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,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.model(
            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,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
                    logits.device
                )
            else:
                sequence_lengths = -1

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING)
class VisionEncoderDecoderModel(PreTrainedModel):
    r"""
    [`VisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with
    one of the base vision model classes of the library as encoder and another one as decoder when created with the
    :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and
    :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder.
    """

    config_class = VisionEncoderDecoderConfig
    base_model_prefix = "vision_encoder_decoder"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def __init__(
        self,
        config: Optional[PretrainedConfig] = None,
        encoder: Optional[PreTrainedModel] = None,
        decoder: Optional[PreTrainedModel] = None,
    ):
        if config is None and (encoder is None or decoder is None):
            raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
        if config is None:
            config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
        else:
            if not isinstance(config, self.config_class):
                raise ValueError(f"Config: {config} has to be of type {self.config_class}")

        if config.decoder.cross_attention_hidden_size is not None:
            if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
                raise ValueError(
                    "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
                    f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
                    f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
                    " `config.encoder.hidden_size`."
                )

        # initialize with config
        # make sure input & output embeddings is not tied
        config.tie_word_embeddings = False
        super().__init__(config)

        if encoder is None:
            encoder = AutoModel.from_config(config.encoder)

        if decoder is None:
            decoder = AutoModelForCausalLM.from_config(config.decoder)

        self.encoder = encoder
        self.decoder = decoder

        if self.encoder.config.to_dict() != self.config.encoder.to_dict():
            logger.warning(
                f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
                f" {self.config.encoder}"
            )
        if self.decoder.config.to_dict() != self.config.decoder.to_dict():
            logger.warning(
                f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
                f" {self.config.decoder}"
            )

        # make sure that the individual model's config refers to the shared config
        # so that the updates to the config will be synced
        self.encoder.config = self.config.encoder
        self.decoder.config = self.config.decoder

        # encoder outputs might need to be projected to different dimension for decoder
        if (
            self.encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)

        if self.encoder.get_output_embeddings() is not None:
            raise ValueError(
                f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
            )

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def get_output_embeddings(self):
        return self.decoder.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        return self.decoder.set_output_embeddings(new_embeddings)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Example:

        ```python
        >>> from transformers import VisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer
        >>> from PIL import Image
        >>> import requests

        >>> image_processor = AutoImageProcessor.from_pretrained("ydshieh/vit-gpt2-coco-en")
        >>> decoder_tokenizer = AutoTokenizer.from_pretrained("ydshieh/vit-gpt2-coco-en")
        >>> model = VisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> img = Image.open(requests.get(url, stream=True).raw)
        >>> pixel_values = image_processor(images=img, return_tensors="pt").pixel_values  # Batch size 1

        >>> output_ids = model.generate(
        ...     pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True
        ... ).sequences

        >>> preds = decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True)
        >>> preds = [pred.strip() for pred in preds]

        >>> assert preds == ["a cat laying on top of a couch next to another cat"]
        ```"""

        from_tf = kwargs.pop("from_tf", False)
        if from_tf:
            from transformers import TFVisionEncoderDecoderModel

            # a workaround to load from tensorflow checkpoint
            # Using `_tf_model` won't work, because the weight names in the encoder/decoder of `_tf_model` get
            # extended before saving those components. For example, The name of `_tf_model.encoder.vit` is
            # `[top model name]/encoder/vit`, but the name of `tf_model.encoder.vit` is `[top model name]/vit`. The
            # [top model name] is handled (stripped) by the conversion method, and the former case gets extra `encoder`,
            # which should not occur when we want to save the components alone.
            # There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see
            #   https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245
            #   (the change in `src/transformers/modeling_tf_utils.py`)
            _tf_model = TFVisionEncoderDecoderModel.from_pretrained(
                pretrained_model_name_or_path, *model_args, **kwargs
            )
            config = _tf_model.config

            # Using `tf_model` instead
            encoder = _tf_model.encoder.__class__(_tf_model.config.encoder)
            decoder = _tf_model.decoder.__class__(_tf_model.config.decoder)
            # Make sure models are built
            encoder(encoder.dummy_inputs)
            decoder(decoder.dummy_inputs)

            # Get the variable correspondence between `_tf_model` and `encoder` and `decoder`
            encoder_variables = {}
            for v in encoder.trainable_variables + encoder.non_trainable_variables:
                encoder_variables["/".join(v.name.split("/")[1:])] = v
            decoder_variables = {}
            for v in decoder.trainable_variables + decoder.non_trainable_variables:
                decoder_variables["/".join(v.name.split("/")[1:])] = v

            _encoder_variables = {}
            for v in _tf_model.encoder.trainable_variables + _tf_model.encoder.non_trainable_variables:
                _encoder_variables["/".join(v.name.split("/")[2:])] = v
            _decoder_variables = {}
            for v in _tf_model.decoder.trainable_variables + _tf_model.decoder.non_trainable_variables:
                _decoder_variables["/".join(v.name.split("/")[2:])] = v

            # assign weight values to `encoder` and `decoder` from `_tf_model`
            for name, v in encoder_variables.items():
                v.assign(_encoder_variables[name])
            for name, v in decoder_variables.items():
                v.assign(_decoder_variables[name])

            tf_model = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder)

            # Deal with `enc_to_dec_proj`
            if hasattr(_tf_model, "enc_to_dec_proj"):
                tf_model(tf_model.dummy_inputs)
                tf_model.enc_to_dec_proj.kernel.assign(_tf_model.enc_to_dec_proj.kernel)
                tf_model.enc_to_dec_proj.bias.assign(_tf_model.enc_to_dec_proj.bias)

            with tempfile.TemporaryDirectory() as tmpdirname:
                encoder_dir = os.path.join(tmpdirname, "encoder")
                decoder_dir = os.path.join(tmpdirname, "decoder")
                tf_model.encoder.save_pretrained(encoder_dir)
                tf_model.decoder.save_pretrained(decoder_dir)

                if hasattr(tf_model, "enc_to_dec_proj"):
                    enc_to_dec_proj_weight = torch.transpose(
                        torch.from_numpy(tf_model.enc_to_dec_proj.kernel.numpy()), 1, 0
                    )
                    enc_to_dec_proj_bias = torch.from_numpy(tf_model.enc_to_dec_proj.bias.numpy())

                del _tf_model
                del tf_model
                gc.collect()

                model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
                    encoder_dir, decoder_dir, encoder_from_tf=True, decoder_from_tf=True
                )
                # This is only for copying some specific attributes of this particular model.
                model.config = config

                if hasattr(model, "enc_to_dec_proj"):
                    model.enc_to_dec_proj.weight.data = enc_to_dec_proj_weight.contiguous()
                    model.enc_to_dec_proj.bias.data = enc_to_dec_proj_bias.contiguous()

                return model

        # At the moment fast initialization is not supported for composite models
        if kwargs.get("_fast_init", False):
            logger.warning(
                "Fast initialization is currently not supported for VisionEncoderDecoderModel. "
                "Falling back to slow initialization..."
            )
        kwargs["_fast_init"] = False

        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

    @classmethod
    def from_encoder_decoder_pretrained(
        cls,
        encoder_pretrained_model_name_or_path: str = None,
        decoder_pretrained_model_name_or_path: str = None,
        *model_args,
        **kwargs,
    ) -> PreTrainedModel:
        r"""
        Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
        checkpoints.


        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you need to first set it back in training mode with `model.train()`.

        Params:
            encoder_pretrained_model_name_or_path (`str`, *optional*):
                Information necessary to initiate the image encoder. Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
                      example is `google/vit-base-patch16-224-in21k`.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
                Information necessary to initiate the text decoder. 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/`.
                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            model_args (remaining positional arguments, *optional*):
                All remaning positional arguments will be passed to the underlying model's `__init__` method.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`).

                - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
                - To update the parent model configuration, do not use a prefix for each configuration parameter.

                Behaves differently depending on whether a `config` is provided or automatically loaded.

        Example:

        ```python
        >>> from transformers import VisionEncoderDecoderModel

        >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
        >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
        ...     "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
        ... )
        >>> # saving model after fine-tuning
        >>> model.save_pretrained("./vit-bert")
        >>> # load fine-tuned model
        >>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")
        ```"""

        kwargs_encoder = {
            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
        }

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        # remove encoder, decoder kwargs from kwargs
        for key in kwargs_encoder.keys():
            del kwargs["encoder_" + key]
        for key in kwargs_decoder.keys():
            del kwargs["decoder_" + key]

        # Load and initialize the encoder and decoder
        # The distinction between encoder and decoder at the model level is made
        # by the value of the flag `is_decoder` that we need to set correctly.
        encoder = kwargs_encoder.pop("model", None)
        if encoder is None:
            if encoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_encoder:
                encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
                    encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
                )

                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                    logger.info(
                        f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
                        "from a decoder model. Cross-attention and casual mask are disabled."
                    )
                    encoder_config.is_decoder = False
                    encoder_config.add_cross_attention = False

                kwargs_encoder["config"] = encoder_config

            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)

        decoder = kwargs_decoder.pop("model", None)
        if decoder is None:
            if decoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_decoder:
                decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
                    decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
                )

                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                    logger.info(
                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
                        f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
                        f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
                    )
                    decoder_config.is_decoder = True
                    decoder_config.add_cross_attention = True

                kwargs_decoder["config"] = decoder_config

            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
                logger.warning(
                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
                    f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
                    "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
                    "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
                    "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
                )

            decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)

        # instantiate config with corresponding kwargs
        config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)

        # make sure input & output embeddings is not tied
        config.tie_word_embeddings = False
        return cls(encoder=encoder, decoder=decoder, config=config)

    @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        decoder_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,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, VisionEncoderDecoderModel
        >>> import requests
        >>> from PIL import Image
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
        >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

        >>> # load image from the IAM dataset
        >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

        >>> # training
        >>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id
        >>> model.config.pad_token_id = processor.tokenizer.pad_token_id
        >>> model.config.vocab_size = model.config.decoder.vocab_size

        >>> pixel_values = processor(image, return_tensors="pt").pixel_values
        >>> text = "hello world"
        >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
        >>> outputs = model(pixel_values=pixel_values, labels=labels)
        >>> loss = outputs.loss

        >>> # inference (generation)
        >>> generated_ids = model.generate(pixel_values)
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            if pixel_values is None:
                raise ValueError("You have to specify pixel_values")

            encoder_outputs = self.encoder(
                pixel_values=pixel_values,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_encoder,
            )
        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
            self.encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

        # else:
        encoder_attention_mask = None

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # Compute loss independent from decoder (as some shift the logits inside them)
        loss = None
        if labels is not None:
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
    ):
        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
        input_dict = {
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "decoder_input_ids": decoder_inputs["input_ids"],
            "encoder_outputs": encoder_outputs,
            "past_key_values": decoder_inputs["past_key_values"],
            "use_cache": use_cache,
        }
        return input_dict

    def resize_token_embeddings(self, *args, **kwargs):
        raise NotImplementedError(
            "Resizing the embedding layers via the VisionEncoderDecoderModel directly is not supported.Please use the"
            " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))"
        )

    def _reorder_cache(self, past_key_values, beam_idx):
        # apply decoder cache reordering here
        return self.decoder._reorder_cache(past_key_values, beam_idx)

@add_start_docstrings(ENCODER_DECODER_START_DOCSTRING)
class EncoderDecoderModel(PreTrainedModel):
    r"""
    [`EncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with one
    of the base model classes of the library as encoder and another one as decoder when created with the
    :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and
    :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder.
    """

    config_class = EncoderDecoderConfig
    base_model_prefix = "encoder_decoder"
    main_input_name = "input_ids"
    supports_gradient_checkpointing = True

    def __init__(
        self,
        config: Optional[PretrainedConfig] = None,
        encoder: Optional[PreTrainedModel] = None,
        decoder: Optional[PreTrainedModel] = None,
    ):
        if config is None and (encoder is None or decoder is None):
            raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
        if config is None:
            config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
        else:
            if not isinstance(config, self.config_class):
                raise ValueError(f"Config: {config} has to be of type {self.config_class}")

        if config.decoder.cross_attention_hidden_size is not None:
            if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
                raise ValueError(
                    "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
                    f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
                    f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
                    " `config.encoder.hidden_size`."
                )

        # initialize with config
        super().__init__(config)

        if encoder is None:
            from ..auto.modeling_auto import AutoModel

            encoder = AutoModel.from_config(config.encoder)

        if decoder is None:
            from ..auto.modeling_auto import AutoModelForCausalLM

            decoder = AutoModelForCausalLM.from_config(config.decoder)

        self.encoder = encoder
        self.decoder = decoder

        if self.encoder.config.to_dict() != self.config.encoder.to_dict():
            logger.warning(
                f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
                f" {self.config.encoder}"
            )
        if self.decoder.config.to_dict() != self.config.decoder.to_dict():
            logger.warning(
                f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
                f" {self.config.decoder}"
            )

        # make sure that the individual model's config refers to the shared config
        # so that the updates to the config will be synced
        self.encoder.config = self.config.encoder
        self.decoder.config = self.config.decoder

        # encoder outputs might need to be projected to different dimension for decoder
        if (
            self.encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)

        if self.encoder.get_output_embeddings() is not None:
            raise ValueError(
                f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
            )

        decoder_signature = set(inspect.signature(self.decoder.forward).parameters.keys())
        if "encoder_hidden_states" not in decoder_signature:
            raise ValueError(
                "The selected decoder is not prepared for the encoder hidden states to be passed. Please see the "
                "following discussion on GitHub: https://github.com/huggingface/transformers/issues/23350"
            )

        # tie encoder, decoder weights if config set accordingly
        self.tie_weights()

    def tie_weights(self):
        # tie encoder & decoder if needed
        if self.config.tie_encoder_decoder:
            # tie encoder and decoder base model
            decoder_base_model_prefix = self.decoder.base_model_prefix
            tied_weights = self._tie_encoder_decoder_weights(
                self.encoder,
                self.decoder._modules[decoder_base_model_prefix],
                self.decoder.base_model_prefix,
                "encoder",
            )
            # Setting a dynamic variable instead of `_tied_weights_keys` because it's a class
            # attributed not an instance member, therefore modifying it will modify the entire class
            # Leading to issues on subsequent calls by different tests or subsequent calls.
            self._dynamic_tied_weights_keys = tied_weights

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def get_output_embeddings(self):
        return self.decoder.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        return self.decoder.set_output_embeddings(new_embeddings)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Example:

        ```python
        >>> from transformers import EncoderDecoderModel

        >>> model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
        ```"""

        from_tf = kwargs.pop("from_tf", False)
        if from_tf:
            from transformers import TFEncoderDecoderModel

            # a workaround to load from tensorflow checkpoint
            # Using `_tf_model` won't work, because the weight names in the encoder/decoder of `_tf_model` get
            # extended before saving those components. For example, The name of `_tf_model.encoder.vit` is
            # `[top model name]/encoder/vit`, but the name of `tf_model.encoder.vit` is `[top model name]/vit`. The
            # [top model name] is handled (stripped) by the conversion method, and the former case gets extra `encoder`,
            # which should not occur when we want to save the components alone.
            # There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see
            #   https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245
            #   (the change in `src/transformers/modeling_tf_utils.py`)
            _tf_model = TFEncoderDecoderModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            config = _tf_model.config

            # Using `tf_model` instead
            encoder = _tf_model.encoder.__class__(_tf_model.config.encoder)
            decoder = _tf_model.decoder.__class__(_tf_model.config.decoder)
            # Make sure models are built
            encoder(encoder.dummy_inputs)
            decoder(decoder.dummy_inputs)

            # Get the variable correspondence between `_tf_model` and `encoder` and `decoder`
            encoder_variables = {}
            for v in encoder.trainable_variables + encoder.non_trainable_variables:
                encoder_variables["/".join(v.name.split("/")[1:])] = v
            decoder_variables = {}
            for v in decoder.trainable_variables + decoder.non_trainable_variables:
                decoder_variables["/".join(v.name.split("/")[1:])] = v

            _encoder_variables = {}
            for v in _tf_model.encoder.trainable_variables + _tf_model.encoder.non_trainable_variables:
                _encoder_variables["/".join(v.name.split("/")[2:])] = v
            _decoder_variables = {}
            for v in _tf_model.decoder.trainable_variables + _tf_model.decoder.non_trainable_variables:
                _decoder_variables["/".join(v.name.split("/")[2:])] = v

            # assign weight values to `encoder` and `decoder` from `_tf_model`
            for name, v in encoder_variables.items():
                v.assign(_encoder_variables[name])
            for name, v in decoder_variables.items():
                v.assign(_decoder_variables[name])

            tf_model = TFEncoderDecoderModel(encoder=encoder, decoder=decoder)

            # Deal with `enc_to_dec_proj`
            if hasattr(_tf_model, "enc_to_dec_proj"):
                tf_model(tf_model.dummy_inputs)
                tf_model.enc_to_dec_proj.kernel.assign(_tf_model.enc_to_dec_proj.kernel)
                tf_model.enc_to_dec_proj.bias.assign(_tf_model.enc_to_dec_proj.bias)

            with tempfile.TemporaryDirectory() as tmpdirname:
                encoder_dir = os.path.join(tmpdirname, "encoder")
                decoder_dir = os.path.join(tmpdirname, "decoder")
                tf_model.encoder.save_pretrained(encoder_dir)
                tf_model.decoder.save_pretrained(decoder_dir)

                if hasattr(tf_model, "enc_to_dec_proj"):
                    enc_to_dec_proj_weight = torch.transpose(
                        torch.from_numpy(tf_model.enc_to_dec_proj.kernel.numpy()), 1, 0
                    )
                    enc_to_dec_proj_bias = torch.from_numpy(tf_model.enc_to_dec_proj.bias.numpy())

                del _tf_model
                del tf_model
                gc.collect()

                model = EncoderDecoderModel.from_encoder_decoder_pretrained(
                    encoder_dir, decoder_dir, encoder_from_tf=True, decoder_from_tf=True
                )
                # This is only for copying some specific attributes of this particular model.
                model.config = config

                if hasattr(model, "enc_to_dec_proj"):
                    model.enc_to_dec_proj.weight.data = enc_to_dec_proj_weight.contiguous()
                    model.enc_to_dec_proj.bias.data = enc_to_dec_proj_bias.contiguous()

                return model

        # At the moment fast initialization is not supported for composite models
        if kwargs.get("_fast_init", False):
            logger.warning(
                "Fast initialization is currently not supported for EncoderDecoderModel. "
                "Falling back to slow initialization..."
            )
        kwargs["_fast_init"] = False

        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

    @classmethod
    def from_encoder_decoder_pretrained(
        cls,
        encoder_pretrained_model_name_or_path: str = None,
        decoder_pretrained_model_name_or_path: str = None,
        *model_args,
        **kwargs,
    ) -> PreTrainedModel:
        r"""
        Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
        checkpoints.


        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you need to first set it back in training mode with `model.train()`.

        Params:
            encoder_pretrained_model_name_or_path (`str`, *optional*):
                Information necessary to initiate the encoder. 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/`.
                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
                Information necessary to initiate the decoder. 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/`.
                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            model_args (remaining positional arguments, *optional*):
                All remaining positional arguments will be passed to the underlying model's `__init__` method.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`).

                - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
                - To update the parent model configuration, do not use a prefix for each configuration parameter.

                Behaves differently depending on whether a `config` is provided or automatically loaded.

        Example:

        ```python
        >>> from transformers import EncoderDecoderModel

        >>> # initialize a bert2bert from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased")
        >>> # saving model after fine-tuning
        >>> model.save_pretrained("./bert2bert")
        >>> # load fine-tuned model
        >>> model = EncoderDecoderModel.from_pretrained("./bert2bert")
        ```"""

        kwargs_encoder = {
            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
        }

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        # remove encoder, decoder kwargs from kwargs
        for key in kwargs_encoder.keys():
            del kwargs["encoder_" + key]
        for key in kwargs_decoder.keys():
            del kwargs["decoder_" + key]

        # Load and initialize the encoder and decoder
        # The distinction between encoder and decoder at the model level is made
        # by the value of the flag `is_decoder` that we need to set correctly.
        encoder = kwargs_encoder.pop("model", None)
        if encoder is None:
            if encoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_encoder:
                encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
                    encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
                )

                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                    logger.info(
                        f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
                        "from a decoder model. Cross-attention and casual mask are disabled."
                    )
                    encoder_config.is_decoder = False
                    encoder_config.add_cross_attention = False

                kwargs_encoder["config"] = encoder_config

            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)

        decoder = kwargs_decoder.pop("model", None)
        if decoder is None:
            if decoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_decoder:
                decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
                    decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
                )

                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                    logger.info(
                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
                        f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
                        f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
                    )
                    decoder_config.is_decoder = True
                    decoder_config.add_cross_attention = True

                kwargs_decoder["config"] = decoder_config

            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
                logger.warning(
                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
                    f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
                    "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
                    "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
                    "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
                )

            decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)

        # instantiate config with corresponding kwargs
        config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
        return cls(encoder=encoder, decoder=decoder, config=config)

    @add_start_docstrings_to_model_forward(ENCODER_DECODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
        past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_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,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, Seq2SeqLMOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import EncoderDecoderModel, BertTokenizer
        >>> import torch

        >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained(
        ...     "google-bert/bert-base-uncased", "google-bert/bert-base-uncased"
        ... )  # initialize Bert2Bert from pre-trained checkpoints

        >>> # training
        >>> model.config.decoder_start_token_id = tokenizer.cls_token_id
        >>> model.config.pad_token_id = tokenizer.pad_token_id
        >>> model.config.vocab_size = model.config.decoder.vocab_size

        >>> input_ids = tokenizer("This is a really long text", return_tensors="pt").input_ids
        >>> labels = tokenizer("This is the corresponding summary", return_tensors="pt").input_ids
        >>> outputs = model(input_ids=input_ids, labels=labels)
        >>> loss, logits = outputs.loss, outputs.logits

        >>> # save and load from pretrained
        >>> model.save_pretrained("bert2bert")
        >>> model = EncoderDecoderModel.from_pretrained("bert2bert")

        >>> # generation
        >>> generated = model.generate(input_ids)
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_encoder,
            )
        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
            self.encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )
            if decoder_attention_mask is None:
                decoder_attention_mask = decoder_input_ids.new_tensor(decoder_input_ids != self.config.pad_token_id)

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # Compute loss independent from decoder (as some shift the logits inside them)
        loss = None
        if labels is not None:
            warnings.warn(DEPRECATION_WARNING, FutureWarning)
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
    ):
        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
        input_dict = {
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "decoder_input_ids": decoder_inputs["input_ids"],
            "encoder_outputs": encoder_outputs,
            "past_key_values": decoder_inputs["past_key_values"],
            "use_cache": use_cache,
        }
        return input_dict

    def resize_token_embeddings(self, *args, **kwargs):
        raise NotImplementedError(
            "Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the"
            " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
            " model.decoder.resize_token_embeddings(...))"
        )

    def _reorder_cache(self, past_key_values, beam_idx):
        # apply decoder cache reordering here
        return self.decoder._reorder_cache(past_key_values, beam_idx)


class MistralForCausalThoughtLM(MistralPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

def save_tokens_with_rewards_to_pdf(input_ids, token_rewards, tokenizer, output_file="text.pdf", eps=0.2, eps2=0.5):
    c = canvas.Canvas(output_file, pagesize=letter)
    c.setFont("Courier", 8)
    x, y = 50, 750
    previous_text = ""
    current_text = ""
    for token_idx, reward in enumerate(token_rewards):
        current_text = tokenizer.decode(input_ids[: token_idx + 1])
        if current_text != previous_text:
            diff_text = current_text[len(previous_text) :]
            if "\n" in diff_text:
                lines = diff_text.split("\n")
                for line_idx, line in enumerate(lines):
                    if line_idx > 0:
                        x = 50
                        y -= 12
                    if abs(reward) < eps:
                        opacity = 0
                    elif abs(reward) > eps2:
                        opacity = 0.8
                    else:
                        opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
                    text_width = c.stringWidth(line)
                    if reward > 0:
                        highlight_color = HexColor("#4CCD99")
                    else:
                        highlight_color = HexColor("#FFC700")
                    highlight_color.alpha = opacity
                    c.setFillColor(highlight_color)
                    c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
                    c.setFillColor(HexColor("#000000"))
                    c.drawString(x, y, line)
                    x += text_width
            else:
                if abs(reward) < eps:
                    opacity = 0
                elif abs(reward) > eps2:
                    opacity = 0.8
                else:
                    opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
                text_width = c.stringWidth(diff_text)
                if reward > 0:
                    highlight_color = HexColor("#4CCD99")
                else:
                    highlight_color = HexColor("#FFC700")
                highlight_color.alpha = opacity
                c.setFillColor(highlight_color)
                c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
                c.setFillColor(HexColor("#000000"))
                c.drawString(x, y, diff_text)
                x += text_width
            if x > 550:
                x = 50
                y -= 12
            if y < 50:
                c.showPage()
                y = 750
                x = 50
            previous_text = current_text
    c.showPage()
    c.save()
    def __init__(self, config):
        super().__init__(config)
        self.model = MistralModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.max_thoughts = config.max_thoughts
        self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
        self.use_concat_talk_head = config.use_concat_talk_head
        self.use_shallow_talk = config.use_shallow_talk
        self.use_complex_talk_head = config.use_complex_talk_head
        self.use_weighted_talk_head = config.use_weighted_talk_head
        # the weighted head will output a single value, so it can't be passed to the lm head
        assert not (self.use_weighted_talk_head and self.use_shallow_talk)

        self.n_ahead = 1
        self.n_ahead_talk = 1
        self.n_passes = 1
        self.n_tokens_print = 1
        self.gradient_accumulation_steps = 1
        self.training_steps = 0
        self.tokenizer = None
        self.start_token_id = None
        self.end_token_id = None
        self.rm_initialized = False
        self.residual_talk_head = True
        self.thought_init_std_scale = 1e-2

        self.final_only_mode = False
        self.first_and_last_mode = True
        self.first_only = False
        self.original_loss_weight = 0.5

        self.cumulative_residual = False
        self.clever_residual = False
        self.skip_residual = False
        self.no_residual = True

        self.optimize_lm_head_only_at_start = False
        self.optimize_model_only_at_start = False

        if self.optimize_model_only_at_start:
            raise NotImplementedError
        self.train_only_thinking_embedding = False
        self.weighted_embeddings = False
        self.use_start_thought_token = True
        self.use_end_thought_token = True
        self.initialize_thought_embedding_to_normal = False
        self.initial_start_token = "---"
        self.initial_end_token = "---"
        self.output_logits_at_the_end = True
        self.gumbel_temperature = 0.001

        self.use_policy_loss = True
        self.include_policy_loss = True
        self.trice_mode = True
        self.remove_negative_rewards = True
        self.use_policy_loss_for_end_thought = True
        
        self.base_original_mode = False
        self.original_mode = False

        self.thought_prefix = "(Let's think step by step"
        self.tokenized_thought_prefix = None
        self.log_dict = defaultdict(int)
        self.eval_log_dict = defaultdict(int)
        self.print_final_only = True
        self.loss_mean = loss_mean
        self.all_rewards = []
        self.all_unreduced_losses = []
        self.kill_after = 100

        self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
        self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))

        self.policy_loss_beta = 1e6
        self.embedding_scale = 1e2
        self.reinforce_temperature = 3
        self.base_loss_beta = 1

        # Not used in the paper:
        self.use_thought_prefix = False
        self.use_reparam_for_thought_embeddings = False
        self.use_upper_triangular = False
        self.subtract_mean_reward = False
        self.comparison_mode = False
        self.gumbel_detach = True
    
        # For visualization
        self.eval_mode = False

        num_talk = 1
        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
        if self.use_weighted_talk_head:
            talk_output_dim = 1
        else:
            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size

        if not self.merged_lm_and_talk_heads:
            if self.use_complex_talk_head:
                self.talk_head = nn.ModuleList([nn.Sequential(
                    nn.Linear(talk_input_dim, config.hidden_size),
                    nn.ReLU(),
                    nn.Linear(config.hidden_size, config.hidden_size),
                    nn.ReLU(),
                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
                )])
            else:
                self.talk_head = nn.ModuleList([nn.Sequential(
                    nn.Linear(talk_input_dim, talk_output_dim, 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

    @torch.no_grad()
    def infer(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        batch_size, seq_len = input_ids.shape

        # Save the original input_ids and attention_mask for later use
        original_input_ids = input_ids.clone()
        original_attention_mask = attention_mask.clone() if attention_mask is not None else None

        # Append the start thought token to the input sequence
        start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
        input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
        seq_len += 1

        # Update the attention mask
        if attention_mask is not None:
            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

        # Generate the continuation
        continuation_length = self.n_ahead - 2
        new_key_values = past_key_values
        
        start_time = time.time()
        for continuation_idx in range(continuation_length):
            outputs = self.model(
                input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_values=new_key_values,
                inputs_embeds=inputs_embeds,
                use_cache=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
            new_key_values = outputs.past_key_values

            hidden_states = outputs[0]

            logits = self.lm_head(hidden_states)
            logits = logits[:, -1, :]  # Only consider the last token

            # Apply Gumbel-Softmax to the logits
            next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
            next_token_id = torch.argmax(next_token_logits, dim=-1)

            # Append the generated token to the input sequence
            input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
            seq_len += 1

            # Update the attention mask
            if attention_mask is not None:
                attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

        # Append the end thought token to the input sequence
        end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
        input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
        seq_len += 1

        # Update the attention mask
        if attention_mask is not None:
            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

        # Get the hidden states before and after the thought
        outputs_before = self.model(
            input_ids=original_input_ids,
            attention_mask=original_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=return_dict,
        )
        hidden_states_before = outputs_before[0][:, -1:, :]

        # two new tokens: last continuation token and end thought token
        outputs_after = self.model(
            input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=new_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states_after = outputs_after[0][:, -1:, :]

        # Apply the talk head to get the mixing weight
        mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))

        # Apply the mixing weight to the hidden states
        mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after

        # Apply the language model head to get the final logits
        logits = self.lm_head(mixed_hidden_states)
        return logits

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = 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,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            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]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, MistralForCausalLM

        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> 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]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        log_dict = self.log_dict if self.training else self.eval_log_dict

        if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after:
            raise ValueError("Killed after")

        if not self.training:
            n_ahead_talk_to_restore = self.n_ahead_talk
            n_passes_to_restore = self.n_passes
            self.n_ahead_talk = 1
            self.n_passes = 1

        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
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
        assert not (self.skip_residual and self.use_policy_loss)

        if self.tokenized_thought_prefix is None and self.use_thought_prefix:
            self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]

        def apply_head(head, states, detach=False):
            if detach:
                head_weight = head.weight.detach()
            else:
                head_weight = head.weight
            head_weight = head_weight.to(states.device)
            return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()
    
        def idx_if_sequential(head, idx=0):
            if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
                return idx_if_sequential(head[idx], idx=idx)
            return head

        def none_repeat_interleave(x, n):
            if x is None:
                return x
            return x.repeat_interleave(n, dim=0)

        if self.n_passes > 1:
            input_ids = none_repeat_interleave(input_ids, self.n_passes)
            attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
            position_ids = none_repeat_interleave(position_ids, self.n_passes)
            inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
            labels = none_repeat_interleave(labels, self.n_passes)
            if past_key_values is not None:
                past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
        cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)

        self.tokenizer_has_start_thought_token = True
        self.tokenizer_has_end_thought_token = True
        if self.start_token_id is None:
            self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
            if self.start_token_id == 0:
                self.start_token_id = self.tokenizer.bos_token_id
                self.tokenizer_has_start_thought_token = False
            elif self.use_start_thought_token:
                # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
                base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
                if self.initialize_thought_embedding_to_normal:
                    self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
                else:
                    self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
                self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
        if self.end_token_id is None:
            self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
            if self.end_token_id == 0:
                self.end_token_id = self.tokenizer.eos_token_id
                self.tokenizer_has_end_thought_token = False
            elif self.use_end_thought_token:
                # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
                base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
                if self.initialize_thought_embedding_to_normal:
                    self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
                else:
                    self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
                self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)

        if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
            self.rm_initialized = True                        
            if not self.use_shallow_talk:
                head = self.talk_head[0]
                cur_head = head[-1] if isinstance(head, nn.Sequential) else head
                talk_input_dim = cur_head.weight.data.shape[1]
                talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
                cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
            else:
                # convert to identity transform
                def lambda_transform(cur_head):
                    if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
                        return torch.cat([
                        torch.eye(
                            cur_head.weight.data.shape[0],
                            device=cur_head.weight.device,
                            dtype=cur_head.weight.dtype
                        ),
                        torch.zeros(
                            cur_head.weight.data.shape[0],
                            cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
                            device=cur_head.weight.device,
                            dtype=cur_head.weight.dtype
                        )], dim=1)
                    return torch.eye(
                        cur_head.weight.data.shape[0],
                        device=cur_head.weight.device,
                        dtype=cur_head.weight.dtype
                    )
                if isinstance(self.talk_head[0], nn.Sequential):
                    for cur_head in self.talk_head[0]:
                        # if it has weights
                        if hasattr(cur_head, "weight"):
                            cur_head.weight.data = lambda_transform(cur_head)
                else:
                    self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])

        loss = None
        prev_rm_tokens = None
        cur_rm_tokens = None
        prev_rm_logits = None
        prev_sample_probs = None
        did_skip_sampling = None
        skip_sampling = None
        sample_probs = None
        hidden_states = None
        logits = None
        talk_kl_penalty = None
        rm_logits = None
        residual_logits = None
        probabilities_2d = None
        prev_probabilities_2d = None
        policy_reward = None
        logits_to_output = None
        batch_size, seq_len = input_ids.shape
        base_input_ids = input_ids.clone()
        loss_list = []
        dqn_loss_list = []
        sampled_token_history = []
        sample_probs_history = []
        action_loglikelihoods_list = []

        if self.use_end_thought_token or self.use_start_thought_token:
            if not self.use_reparam_for_thought_embeddings:
                start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale
                end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale
            else:
                start_embedding = self.start_embedding * self.embedding_scale
                end_embedding = self.end_embedding * self.embedding_scale
            base_embeddings = self.model.embed_tokens.weight
            if self.train_only_thinking_embedding:
                base_embeddings = base_embeddings.detach()
        # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1
        for ahead_idx in range(fwd_iters):
            past_key_values_length = 0
            if past_key_values is not None:
                use_legacy_cache = not isinstance(past_key_values, Cache)
                if use_legacy_cache:
                    past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                past_key_values_length = past_key_values.get_usable_length(seq_len)

            if position_ids is None:
                device = input_ids.device if input_ids is not None else inputs_embeds.device
                position_ids = torch.arange(
                    past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device
                )
                position_ids = position_ids.unsqueeze(0).view(-1, seq_len)
            else:
                position_ids = position_ids.view(-1, seq_len).long()

            if inputs_embeds is None:
                contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any()
                contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any()
                contains_thought = contains_start or contains_end
                if contains_thought:
                    thought_id = self.start_token_id if contains_start else self.end_token_id
                    cur_thought_embedding = start_embedding if contains_start else end_embedding
                    if self.use_reparam_for_thought_embeddings:
                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
                        inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
                        if contains_start:
                            sampled_start = inputs_embeds.clone().detach()
                        if contains_end:
                            sampled_end = inputs_embeds.clone().detach()
                    else:
                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
                else:
                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
                        inputs_embeds = self.model.embed_tokens(input_ids)
            
            if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode:
                if attention_mask is None:
                    base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device)
                    base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len)
                    base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1)
                    attention_mask = base_attention_mask
                    breakpoint()
                elif attention_mask.dim() == 2:
                    if seq_len + past_key_values_length != attention_mask.shape[-1]:
                        breakpoint()
                        attention_mask = torch.cat(
                            [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask],
                            dim=-1
                        )
                    # # if the attention mask 
                    attention_mask = _prepare_4d_causal_attention_mask(
                        attention_mask,
                        (batch_size, seq_len),
                        inputs_embeds,
                        past_key_values_length,
                        sliding_window=self.config.sliding_window,
                    )

            outputs = 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,
                return_dict=return_dict,
            )

            prev_hidden_states = hidden_states
            hidden_states = outputs[0]
            prev_rm_logits = rm_logits  # for policy gradient
            prev_rm_tokens = cur_rm_tokens  # for policy gradient

            if ahead_idx == 0:
                hidden_states_lm = hidden_states
                logits = self.lm_head(hidden_states_lm)
                base_hidden_states = hidden_states.clone()
                initial_loss_logits = logits.clone()
                if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start:
                    logits = logits.detach()
                    base_hidden_states = base_hidden_states.detach()
                if self.optimize_model_only_at_start:
                    hidden_states = hidden_states.detach()
                base_logits = logits.clone()
            else:
                talk_hidden_states = hidden_states
                if self.merged_lm_and_talk_heads:
                    assert self.no_residual
                    residual_logits = self.lm_head(hidden_states)
                    talk_hidden_states = hidden_states
                else:
                    if ahead_idx > self.n_ahead - 1:
                        cur_base_hidden = torch.cat([
                            base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :],
                            base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :]
                        ], dim=-2)
                    else:
                        cur_base_hidden = base_hidden_states

                    if self.use_concat_talk_head:
                        # concatenate the hidden states with the original hidden states
                        head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1)
                    else:
                        head_input_hidden_states = talk_hidden_states

                    residual_logits = self.talk_head[0](head_input_hidden_states)
                    if self.use_shallow_talk:
                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)                        
                    residual_logits = residual_logits.to(logits.device)
                    if self.use_weighted_talk_head:
                        # combine the cur_base_hidden with the talk_hidden_states according to the weighted head
                        residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits
                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)

                assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1
                if self.clever_residual:
                    if ahead_idx >= self.n_ahead - 1:
                        # get the logits shifted according to the current talk ahead
                        cur_base_logits = torch.cat([
                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
                        ], dim=-2)
                        if self.optimize_lm_head_only_at_start:
                            cur_base_logits = cur_base_logits.detach()
                        logits = cur_base_logits + residual_logits
                    else:
                        logits += residual_logits / self.n_ahead
                elif self.cumulative_residual:
                    if self.residual_talk_head:
                        if ahead_idx < self.n_ahead:
                            logits += residual_logits
                        else:
                            # get the logits shifted according to the current talk ahead
                            cur_base_logits = torch.cat([
                                base_logits[..., ahead_idx - self.n_ahead + 1:, :],
                                base_logits[..., :ahead_idx - self.n_ahead + 1, :]
                            ], dim=-2)
                            if self.optimize_lm_head_only_at_start:
                                cur_base_logits = cur_base_logits.detach()
                            logits = cur_base_logits + residual_logits
                    else:
                        if ahead_idx < self.n_ahead:
                            logits += residual_logits
                        else:
                            logits = residual_logits
                elif self.skip_residual:
                    if ahead_idx >= self.n_ahead:
                        # get the logits shifted according to the current talk ahead
                        cur_base_logits = torch.cat([
                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
                        ], dim=-2)
                        if self.optimize_lm_head_only_at_start:
                            cur_base_logits = cur_base_logits.detach()
                        logits = cur_base_logits
                elif self.no_residual:
                    logits = residual_logits
                else:
                    logits = base_logits + residual_logits

            attempted = False
            talk_loss_list = []
            if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0):
                loss = None
                attempted = True

                if labels is not None:
                    for shift_amount in range(self.n_ahead_talk):
                        # Shift so that tokens < n predict n
                        #  ab[cde]f
                        # abc[def]
                        if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
                            loss_logits = initial_loss_logits
                        else:
                            loss_logits = logits
                        shift_logits = loss_logits[..., shift_amount:-1, :].contiguous()
                        shift_labels = labels[..., 1 + shift_amount:].contiguous()
                        # Flatten the tokens
                        loss_fct = CrossEntropyLoss(reduction="none")
                        shift_logits = shift_logits.view(-1, self.config.vocab_size)
                        shift_labels = shift_labels.view(-1).clone()
                        # Enable model parallelism
                        shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100
                        shift_labels = shift_labels.to(shift_logits.device)
                        loss = loss_fct(shift_logits, shift_labels)
                        if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode:
                            loss_list.append(loss)
                        talk_loss_list.append(nonzero_mean(loss).detach())
            
            if not attempted or self.comparison_mode:
                rm_hidden_states = hidden_states
                # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm())
                rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start)
                    
                # don't allow it to predict the thinking token
                if self.tokenizer_has_start_thought_token:                    
                    rm_logits[..., self.start_token_id] = -1e10
                if self.tokenizer_has_end_thought_token:
                    rm_logits[..., self.end_token_id] = -1e10
                probabilities = rm_logits
                if probabilities_2d is not None:
                    prev_probabilities_2d = probabilities_2d.clone()
                probabilities_2d = probabilities.view(-1, probabilities.size(-1))

                did_skip_sampling = skip_sampling
                skip_sampling = False
                if ahead_idx == 0 and self.use_start_thought_token:
                    override_token = self.start_token_id
                elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]:
                    override_token = self.tokenized_thought_prefix[..., ahead_idx]
                elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token:
                    override_token = self.end_token_id
                else:
                    override_token = None
                if override_token is not None and self.n_ahead > 1:
                    # always start with the start token
                    probabilities_2d = torch.zeros_like(probabilities_2d)
                    probabilities_2d[:, override_token] = 1.0
                    skip_sampling = True
                elif ahead_idx >= self.n_ahead - 1:
                    if labels is not None:  # we're in the talk phase
                        cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1
                        # print("Setting rm to labels", cur_talk_n, "during", ahead_idx)
                        shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device)
                        padding = torch.full_like(
                            labels[..., :cur_talk_n],
                            self.tokenizer.pad_token_id,
                            dtype=torch.long,
                            device=shift_labels.device
                        )
                        new_rm_tokens = torch.cat(
                            [shift_labels, padding],
                            dim=-1
                        )
                        # convert rm tokens to one-hot
                        probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype)
                        skip_sampling = True
                    else:
                        continue
                temperature = self.gumbel_temperature if self.training else 0.001
                prev_sample_probs = sample_probs
                sample_probs = probabilities_2d
                if ahead_idx < self.n_ahead - 1 and not skip_sampling:
                    probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1)
                    if self.gumbel_detach:
                        probabilities_2d = probabilities_2d.detach()
                sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu())
                # convert rm logits directly to embeddings
                contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0)
                contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0)
                contains_thought = contains_start or contains_end

                if not contains_thought:
                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
                        inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype))
                else:
                    thought_id = self.start_token_id if contains_start else self.end_token_id
                    cur_thought_embedding = start_embedding if contains_start else end_embedding
                    if self.use_reparam_for_thought_embeddings:
                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
                        inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
                        if contains_start:
                            sampled_start = inputs_embeds.clone().detach()
                        else:
                            sampled_end = inputs_embeds.clone().detach()
                    else:
                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
                        inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
                inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)

                if len(attention_mask.shape) == 2:
                    breakpoint()
                else:
                    original_attention = attention_mask[..., :attention_mask.shape[-2]]
                    if self.use_upper_triangular:
                        new_attention = original_attention
                    else:
                        original_attention = original_attention == attention_mask.max()
                        # because eye isn't implemented for BF16, we need to handle the case
                        if not attention_mask.dtype == torch.bfloat16:
                            new_attention = torch.eye(
                                seq_len, dtype=attention_mask.dtype, device=attention_mask.device
                            )
                        else:
                            new_attention = torch.eye(
                                seq_len, dtype=torch.float32, device=attention_mask.device
                            ).to(attention_mask.dtype)

                        new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1)
                        new_attention = new_attention * original_attention
                        new_attention[new_attention == 0] = attention_mask.min()
                        new_attention[new_attention == 1] = attention_mask.max()
                    attention_mask = torch.cat([attention_mask, new_attention], dim=-1)
                past_key_values = outputs.past_key_values
                position_ids = position_ids + 1

                if labels is not None and (self.n_ahead > 1 or not self.base_original_mode):
                    # Shift so that tokens < n predict n
                    # logits: abcdef -> bcdef? -> cdef??
                    # labels: abcdef -> ?bcdef -> ??cdef
                    if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
                        loss_logits = initial_loss_logits
                    else:
                        loss_logits = logits
                    shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1))
                    shift_logits = loss_logits[..., :-shift_idx, :].contiguous()
                    shift_labels = labels[..., shift_idx:].contiguous()
                    # Flatten the tokens
                    loss_fct = CrossEntropyLoss(reduction="none")
                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
                    shift_labels = shift_labels.view(-1)
                    # Enable model parallelism
                    shift_labels = shift_labels.to(shift_logits.device)
                    # if shift_labels.min() == self.tokenizer.pad_token_id:
                    shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels)
                    unreduced_loss = loss_fct(shift_logits, shift_labels)
                    if torch.any(unreduced_loss != unreduced_loss):
                        raise ValueError("NaN loss")
                    unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1)
                    loss_list.append(unreduced_loss)


                    if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token):
                        # we treat the change in loss as the reward
                        previous_loss = loss_list[-2]
                        # for example, suppose n_ahead = 3 and n_ahead_talk = 2
                        # note that we end at self.n_ahead + self.n_ahead_talk - 2
                        # in this case, 5 - 2 = 3, so we end at ahead_idx = 3
                        # we also predict the next token at ahead_idx = 2
                        # when we get to ahead_idx = 2, we predict ahead
                        # so we shift by 1
                        # note that this is ahead_idx = n_ahead - 1
                        # when we get to ahead_idx = 3, we predict ahead
                        # so we shift by 2
                        # note that this is ahead_idx = n_ahead
                        if ahead_idx < self.n_ahead - 1:
                            shift_amount = 0
                            original_dqn_reward = (previous_loss - unreduced_loss).detach()
                            if self.first_and_last_mode:
                                original_dqn_reward = original_dqn_reward * 0.0
                        else:
                            # logits vs cur_policy_shift_logits
                            # let's look at rm_logits and prev_rm_logits
                            shift_amount = max(0, ahead_idx - (self.n_ahead - 1))
                            # let's say shift_amount = 2
                            # abcdefg -> bcdefg? -> cdefg??
                            # logits = [a b]c d e f[g]
                            # labels = [a b c]d e f g
                            cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach()
                            cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous()
                            # Flatten the tokens
                            cur_policy_loss_fct = CrossEntropyLoss(reduction="none")
                            cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size)
                            cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone()
                            # Enable model parallelism
                            cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100
                            cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device)
                            cur_policy_reward_base_loss = loss_fct(
                                cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device)
                            ).reshape(logits.shape[0], -1)
                            original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss
                                
                        if not did_skip_sampling:
                            nonzero_indices = prev_probabilities_2d.nonzero()
                            action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]]
                            action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount]
                            action_loglikelihoods_list.append(action_loglikelihoods_2d)
                        if policy_reward is None:
                            policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
                        else:
                            if self.n_ahead_talk > shift_amount:
                                added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
                            else:
                                added_reward = original_dqn_reward
                            policy_reward += added_reward
                    
                    if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2:
                        # only compute during the thinking phase
                        if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token):
                            # sampled_start, sampled_end
                            # calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution
                            # with mean start_embedding[0] and standard deviation start_embedding[1]
                            if self.use_start_thought_token:
                                exp_start_std = torch.exp(start_embedding[1])
                                start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) ** 2 / exp_start_std ** 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi)
                                start_loglikelihood = start_loglikelihood.mean(dim=-1)
                            if self.use_end_thought_token:
                                exp_end_std = torch.exp(end_embedding[1])
                                end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) ** 2 / exp_end_std ** 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi)
                                end_loglikelihood = end_loglikelihood.mean(dim=-1)
                            # we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings
                            if self.use_end_thought_token and self.use_policy_loss_for_end_thought:
                                action_loglikelihoods_list.append(end_loglikelihood)
                            if self.use_start_thought_token:
                                action_loglikelihoods_list.append(start_loglikelihood)                                

                        if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode:
                            with torch.no_grad():
                                # calculate the 0.75 quantile of the rewards
                                filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten()
                                filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id
                                filtered_tokens = filtered_tokens[filtered_tokens_mask]
                                filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()
                                filtered_rewards = filtered_rewards[filtered_tokens_mask]

                                abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten())
                                abs_reward_list = abs_reward_list[filtered_tokens_mask]
                                medium_quantile = np.quantile(abs_reward_list, 0.5)
                                upper_quantile = np.quantile(abs_reward_list, 0.95)

                                save_tokens_with_rewards_to_pdf(
                                    filtered_tokens,
                                    [0] + filtered_rewards.tolist(),
                                    self.tokenizer,
                                    output_file=f"texts/rewards_talk_{self.n_ahead_talk}_{self.training_steps}.pdf",
                                    eps=medium_quantile,
                                    eps2=upper_quantile,
                                )

                                def plot_kde(data, losses):
                                    sns.set(style="whitegrid")
                                    # Create the KDE plot
                                    sns.kdeplot(data, fill=True)
                                    # Set the plot title and labels
                                    plt.title("KDE Plot")
                                    plt.xlabel("Value")
                                    plt.ylabel("Density")
                                    # Save the plot
                                    plt.savefig(f"texts/kde_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
                                    # Close the plot
                                    plt.close()

                                    # Step 1: Create a base color palette
                                    base_colors = sns.color_palette("light:#5A9", n_colors=256)  # More colors for a smoother gradient
                                    base_cmap = LinearSegmentedColormap.from_list("log_light", base_colors)
                                    log_norm = LogNorm(vmin=1e-3, vmax=10)

                                    sns.kdeplot(x=data, y=losses, fill=True, levels=20, norm=log_norm, cut=0, linewidths=0)
                                    # limit y to 0 to 25 and x to -1 to 1
                                    plt.xlim(-1, 1)
                                    plt.ylim(0, 25)
                                    plt.savefig(f"texts/jointer_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
                                    plt.close()

                                self.all_rewards.extend(filtered_rewards)
                                self.all_unreduced_losses.extend(unreduced_loss[:, :-1].flatten()[filtered_tokens_mask].float().flatten().cpu().detach().numpy())
                                plot_kde(self.all_rewards, self.all_unreduced_losses)

                        for action_loglikelihoods_2d in action_loglikelihoods_list:
                            train_policy_reward = policy_reward

                            # discard rewards below the mean
                            if self.trice_mode and self.n_passes > 1:
                                batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1])
                                # average over the passes
                                train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True)
                                train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1])
                                
                            if self.subtract_mean_reward:
                                train_policy_reward = train_policy_reward - train_policy_reward.mean()
                            if self.remove_negative_rewards:
                                fixed_policy_reward = train_policy_reward.detach().clamp(min=0)
                            else:
                                fixed_policy_reward = train_policy_reward.detach()
                            actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device)
                            if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts:
                                # This will only happen when we force the next token to be the end of thought token
                                break
                            dqn_loss_list.append(actor_loss.mean())

        if loss_list:
            if self.first_and_last_mode:
                loss = sum(
                    self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk)
                ) * (1 - self.original_loss_weight) / self.n_ahead_talk
                loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight
                # Let's NaN out the others
                # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4
                for i in range(1, len(loss_list) - self.n_ahead_talk):
                    loss_list[i] = loss_list[i] * math.nan
            elif self.first_only:
                loss = self.loss_mean(loss_list[0])
            elif self.final_only_mode:
                loss = sum(
                    self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1)
                ) / self.n_ahead_talk   
            else:
                loss = None
                for i in range(len(loss_list)):
                    cur_loss = self.loss_mean(loss_list[i])
                    if loss is not None:
                        loss = loss + cur_loss.to(loss.device)
                    else:
                        loss = cur_loss
                loss = loss / len(loss_list)
            
            loss = loss * self.base_loss_beta

        if dqn_loss_list:
            dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list)
            if self.include_policy_loss:
                if loss is not None:
                    loss += dqn_loss * self.policy_loss_beta
                else:
                    loss = dqn_loss * self.policy_loss_beta

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
    
        base_log_dict = {
            f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list))
        }

        if loss is not None:
            base_log_dict["loss_train"] = loss.item()
        
        for loss_key, loss_val in base_log_dict.items():
            log_dict[loss_key] += loss_val / self.n_tokens_print
                
        if self.use_policy_loss and policy_reward is not None:
            log_dict["policy_loss"] += dqn_loss / self.n_tokens_print
            log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print

        if not loss_list:
            if loss is not None:
                log_dict["loss_0"] += loss / self.n_tokens_print
        else:
            log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print
            log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print

        # also log relative losses to loss_0
        if loss_list:
            for i in range(len(loss_list)):
                talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1)
                if not talk_loss_list:
                    cur_talk_loss = nonzero_mean(loss_list[0])
                else:
                    cur_talk_loss = talk_loss_list[talk_idx]
                log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print
        if self.training:
            self.training_steps += 1
        try:
            # if self.training_steps % (self.gradient_accumulation_steps * 256) == 0:
            if self.wandb_enabled:
                if self.training_steps % (self.n_tokens_print) == 0 or not self.training:# and "0" in str(loss.device):
                    if not self.training:
                        new_log_dict = {}
                        for key in list(log_dict.keys()):
                            new_log_dict["eval_" + key] = log_dict[key]
                        log_dict = new_log_dict
                    log_dict["training_steps"] = self.training_steps 
                    log_dict["batch_size"] = batch_size
                    log_dict["example_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
                    if self.n_ahead > 1:
                        log_dict["compute_steps"] = self.training_steps * batch_size * (self.n_ahead + self.n_ahead_talk - 1) * self.gradient_accumulation_steps
                    else: # There's no overhead for talk tokens if there's no thinking
                        log_dict["compute_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
                    # remove all nans
                    for key in list(log_dict.keys()):
                        if log_dict[key] != log_dict[key]:
                            del log_dict[key]
                    if self.training:
                        wandb.log(log_dict)
                    if self.training:
                        self.log_dict = defaultdict(int)
                    else:
                        self.eval_log_dict = defaultdict(int)
        except Exception as e:
            pass

        if not self.training:
            self.n_ahead_talk = n_ahead_talk_to_restore
            self.n_passes = n_passes_to_restore
        return CausalLMOutputWithPast(
            loss=loss if loss is not None else None,
            logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # Omit tokens covered by past_key_values
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past