import random
from typing import List, Optional, Tuple, Union

import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
)
from transformers.models.opt.modeling_opt import (
    OPTForCausalLM,
    OPTForQuestionAnswering,
    OPTForSequenceClassification,
    OPTModel,
)
from transformers.utils import logging

from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.layer import ColoAttention
from colossalai.shardformer.shard import ShardConfig

logger = logging.get_logger(__name__)


def _get_attention_mask(
    self: OPTModel,
    shard_config: ShardConfig,
    hidden_states: torch.Tensor,
    past_key_values_length: int,
    attention_mask: Optional[torch.FloatTensor],
):
    batch_size, seq_length = hidden_states.shape[:2]
    mask_seq_length = past_key_values_length + seq_length
    if shard_config.enable_flash_attention:
        attention_mask = ColoAttention.prepare_attn_kwargs(
            (batch_size, 1, seq_length, mask_seq_length),
            hidden_states.dtype,
            hidden_states.device,
            attention_mask,
            is_causal=True,
        )
    else:
        attention_mask = self.decoder._prepare_decoder_attention_mask(
            attention_mask,
            (batch_size, seq_length),
            hidden_states,
            past_key_values_length,
        )
    return attention_mask


class OPTPipelineForwards:
    """
    This class serves as a micro library for forward function substitution of OPT models
    under pipeline setting.
    """

    @staticmethod
    def opt_model_forward(
        self: OPTModel,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = 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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: Optional[ShardConfig] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """
        This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward
        """

        from transformers.modeling_outputs import BaseModelOutputWithPast
        from transformers.utils import logging

        logger = logging.get_logger(__name__)

        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

        decoder = self.decoder
        if stage_manager.is_first_stage():
            # 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:
                input_shape = input_ids.size()
                input_ids = input_ids.view(-1, input_shape[-1])
            elif inputs_embeds is not None:
                input_shape = inputs_embeds.size()[:-1]
            else:
                raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

            batch_size, seq_length = input_shape

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

            if decoder.project_in is not None:
                inputs_embeds = decoder.project_in(inputs_embeds)
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            inputs_embeds.dtype

        else:
            if hidden_states is None:
                raise ValueError("hidden_states shouldn't be None for intermediate stages.")
            input_shape = hidden_states.size()[:-1]
            batch_size, seq_length = input_shape[0], input_shape[1]
            device = hidden_states.device
            hidden_states.dtype

        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        # required mask seq length can be calculated via length of past
        mask_seq_length = past_key_values_length + seq_length
        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
        elif attention_mask.shape[1] != mask_seq_length:
            raise ValueError(
                f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
                f"{mask_seq_length} (sum of the lengths of current and past inputs)"
            )

        if stage_manager.is_first_stage():
            causal_attention_mask = _get_attention_mask(
                self,
                shard_config,
                inputs_embeds,
                past_key_values_length,
                attention_mask,
            )
            pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length)
            hidden_states = inputs_embeds + pos_embeds
        else:
            causal_attention_mask = _get_attention_mask(
                self,
                shard_config,
                hidden_states,
                past_key_values_length,
                attention_mask,
            )

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

        # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future.
        if past_key_values:
            logger.warning_once("Non-empty past_key_values is not supported for pipeline models at the moment.")
            past_key_values = None
        if output_attentions:
            logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
            output_attentions = False
        if output_hidden_states:
            logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
            output_hidden_states = False
        if use_cache:
            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
            use_cache = False

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

        # check if head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != (len(decoder.layers)):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for"
                        f" {head_mask.size()[0]}."
                    )

        start_idx, end_idx = stage_index[0], stage_index[1]

        torch.cuda.set_device(device)

        for idx in range(start_idx, end_idx):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            decoder_layer = decoder.layers[idx]

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            dropout_probability = random.uniform(0, 1)
            if decoder.training and (dropout_probability < decoder.layerdrop):
                continue

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if decoder.gradient_checkpointing and decoder.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    causal_attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    past_key_value=past_key_value,
                    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],)

        if stage_manager.is_last_stage():
            if decoder.final_layer_norm is not None:
                hidden_states = decoder.final_layer_norm(hidden_states)
            if decoder.project_out is not None:
                hidden_states = decoder.project_out(hidden_states)

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

        next_cache = next_decoder_cache if use_cache else None

        if stage_manager.is_last_stage():
            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,
            )
        else:
            return {"hidden_states": hidden_states}

    @staticmethod
    def opt_for_causal_lm_forward(
        self: OPTForCausalLM,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = 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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: Optional[ShardConfig] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForCausalLM.forward.
        Please refer to original code of transformers for more details.
        """

        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 = OPTPipelineForwards.opt_model_forward(
            self.model,
            input_ids=input_ids,
            attention_mask=attention_mask,
            head_mask=head_mask,
            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,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
            shard_config=shard_config,
        )
        if stage_manager.is_last_stage():
            logits = self.lm_head(outputs[0]).contiguous()
            loss = None
            if labels is not None:
                # move labels to correct device to enable model parallelism
                labels = labels.to(logits.device)
                # Shift so that tokens < n predict n
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
                # Flatten the tokens
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
            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,
            )
        else:
            hidden_states = outputs.get("hidden_states")
            return {"hidden_states": hidden_states}

    @staticmethod
    def opt_for_sequence_classification_forward(
        self: OPTForSequenceClassification,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: Optional[ShardConfig] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward.
        Please refer to original code of transformers for more details.
        """

        logger = logging.get_logger(__name__)

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

        transformer_outputs = OPTPipelineForwards.opt_model_forward(
            self.model,
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
            shard_config=shard_config,
        )

        if stage_manager.is_last_stage():
            hidden_states = transformer_outputs[0]
            logits = self.score(hidden_states)

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

            if self.config.pad_token_id is None:
                sequence_lengths = -1
            else:
                if input_ids is not None:
                    sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
                else:
                    sequence_lengths = -1
                    logger.warning(
                        f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                        "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                    )

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

            loss = None
            if labels is not None:
                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,
            )
        else:
            hidden_states = transformer_outputs.get("hidden_states")
            return {"hidden_states": hidden_states}

    @staticmethod
    def opt_for_question_answering_forward(
        self: OPTForQuestionAnswering,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: 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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: Optional[ShardConfig] = None,
    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
        r"""
        This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForQuestionAnswering.forward.
        Please refer to original code of transformers for more details.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = OPTPipelineForwards.opt_model_forward(
            self.model,
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
            shard_config=shard_config,
        )
        if stage_manager.is_last_stage():
            hidden_states = transformer_outputs[0]

            logits = self.qa_outputs(hidden_states)
            start_logits, end_logits = logits.split(1, dim=-1)
            start_logits = start_logits.squeeze(-1).contiguous()
            end_logits = end_logits.squeeze(-1).contiguous()

            total_loss = None
            if start_positions is not None and end_positions is not None:
                # If we are on multi-GPU, split add a dimension
                if len(start_positions.size()) > 1:
                    start_positions = start_positions.squeeze(-1)
                if len(end_positions.size()) > 1:
                    end_positions = end_positions.squeeze(-1)
                # sometimes the start/end positions are outside our model inputs, we ignore these terms
                ignored_index = start_logits.size(1)
                start_positions = start_positions.clamp(0, ignored_index)
                end_positions = end_positions.clamp(0, ignored_index)

                loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
                start_loss = loss_fct(start_logits, start_positions)
                end_loss = loss_fct(end_logits, end_positions)
                total_loss = (start_loss + end_loss) / 2

            if not return_dict:
                output = (start_logits, end_logits) + transformer_outputs[2:]
                return ((total_loss,) + output) if total_loss is not None else output

            return QuestionAnsweringModelOutput(
                loss=total_loss,
                start_logits=start_logits,
                end_logits=end_logits,
                hidden_states=transformer_outputs.hidden_states,
                attentions=transformer_outputs.attentions,
            )
        else:
            hidden_states = transformer_outputs.get("hidden_states")
            return {"hidden_states": hidden_states}


def get_opt_flash_attention_forward(shard_config: ShardConfig):
    from transformers.models.opt.modeling_opt import OPTAttention

    def forward(
        self: OPTAttention,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[dict] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""
        assert layer_head_mask is None, "layer_head_mask is not supported for FlashAttention"
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states)
        # get key, value proj
        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        query_states = self._shape(query_states, tgt_len, bsz)

        dropout_p = self.dropout if self.training else 0.0
        attn_output = ColoAttention.attention(
            query_states,
            key_states,
            value_states,
            **attention_mask,
            dropout_p=dropout_p,
            scale=self.scaling,
        )

        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned aross GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, None, past_key_value

    return forward


def get_opt_decoder_forward_for_flash_attention(shard_config: ShardConfig):
    from transformers.models.opt.modeling_opt import OPTDecoder

    def forward(
        self: OPTDecoder,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = 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:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

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

        batch_size, seq_length = input_shape
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        # required mask seq length can be calculated via length of past
        mask_seq_length = past_key_values_length + seq_length

        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
        elif attention_mask.shape[1] != mask_seq_length:
            raise ValueError(
                f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
                f"{mask_seq_length} (sum of the lengths of current and past inputs)"
            )
        causal_attention_mask = _get_attention_mask(
            self, shard_config, inputs_embeds, past_key_values_length, attention_mask
        )
        pos_embeds = self.embed_positions(attention_mask, past_key_values_length)

        if self.project_in is not None:
            inputs_embeds = self.project_in(inputs_embeds)

        hidden_states = inputs_embeds + pos_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

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

        # check if head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != (len(self.layers)):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
                        f" {head_mask.size()[0]}."
                    )

        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:
                    continue

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    causal_attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    past_key_value=past_key_value,
                    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],)

        if self.final_layer_norm is not None:
            hidden_states = self.final_layer_norm(hidden_states)

        if self.project_out is not None:
            hidden_states = self.project_out(hidden_states)

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

        next_cache = next_decoder_cache if use_cache else None
        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,
        )

    return forward


def get_jit_fused_opt_decoder_layer_forward():
    from transformers.models.opt.modeling_opt import OPTDecoderLayer

    def forward(
        self: OPTDecoderLayer,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            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

        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )

        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training)

        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Fully Connected
        hidden_states_shape = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
        residual = hidden_states

        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)

        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)

        hidden_states = self.fc2(hidden_states)

        hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training).view(hidden_states_shape)

        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs

    return forward
