from transformers.models.qwen3.modeling_qwen3 import (
    create_causal_mask,
    create_sliding_window_causal_mask,
    Qwen3ForCausalLM,
    Qwen3PreTrainedModel,
    Qwen3Model,
    GenerationMixin,
    Unpack,
    TransformersKwargs,
)



class Qwen3ForCut(Qwen3PreTrainedModel, GenerationMixin):
    # _tied_weights_keys = ["lm_head.weight"]
    # _tp_plan = {"lm_head": "colwise_rep"}
    # _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config, ):
        super().__init__(config)
        self.model = Qwen3Model(config)
        self.vocab_size = config.vocab_size
        self.cut_head = nn.ModuleList(
            [nn.Linear(config.hidden_size, 512, bias=False), nn.ReLU(inplace=False), nn.Linear(512, 2, bias=False)]
        )

        # Initialize weights and apply final processing
        self.post_init()


    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cls_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None, # [bsz, q_len]
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutputWithPast:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, Qwen3ForCausalLM

        >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

        >>> 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
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: BaseModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        bsz, q_len, h_size = hidden_states.shape
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        # logits = self.lm_head(hidden_states[:, slice_indices, :])

        
        loss = None
        logits = None
        if self.training and (labels is not None):
            # hidden_states = torch.concat(tensors=[hidden_states, hidden_states[cls_mask].reshape(bsz, 1, h_size).expand(-1, q_len, -1)], dim=-1)
            cut_point_mask = (labels!=-100)
            r_shift_mask = get_shift_mask(cut_point_mask=cut_point_mask, side="right")
            l_shift_mask = get_shift_mask(cut_point_mask=cut_point_mask, side="left")
            shift_hidden_states = hidden_states[r_shift_mask].contiguous()
            shift_labels = labels[l_shift_mask].contiguous()             

            loss_fct = LigerCrossEntropyLoss()

            logits = shift_hidden_states
            for mlp in self.cut_head:
                logits = mlp(logits)

            loss = loss_fct(logits, shift_labels)
        else:
            logits = hidden_states
            # logits = torch.concat(tensors=[hidden_states, hidden_states[cls_mask].reshape(bsz, 1, h_size).expand(-1, q_len, -1)], dim=-1)
            for mlp in self.cut_head:
                logits = mlp(logits)
            
            if labels is not None:
                loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)

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