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

import torch
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel, GPT2Model
from transformers.utils import logging

from colossalai.pipeline.stage_manager import PipelineStageManager


class GPT2PipelineForwards:
    '''
    This class serves as a micro library for forward function substitution of GPT2 models
    under pipeline setting.
    '''

    @staticmethod
    def gpt2_model_forward(
            self: GPT2Model,
            input_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
            attention_mask: Optional[torch.FloatTensor] = None,
            token_type_ids: Optional[torch.LongTensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            head_mask: Optional[torch.FloatTensor] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            encoder_attention_mask: 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) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]:

        # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward.
        # Please refer to original code of transformers for more details.
        logger = logging.get_logger(__name__)

        # Preprocess passed in arguments
        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

        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

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)

        if stage_manager.is_first_stage():
            if input_ids is not None and inputs_embeds is not None:
                raise ValueError("You cannot specify both input_ids and 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])
                batch_size = input_ids.shape[0]
            elif inputs_embeds is not None:
                input_shape = inputs_embeds.size()[:-1]
                batch_size = inputs_embeds.shape[0]
            else:
                raise ValueError("You have to specify either input_ids or inputs_embeds")

            device = input_ids.device if input_ids is not None else inputs_embeds.device
            if token_type_ids is not None:
                token_type_ids = token_type_ids.view(-1, input_shape[-1])
        else:
            if hidden_states is None:
                raise ValueError("hidden_states shouldn't be None for stages other than the first stage.")
            input_shape = hidden_states.size()[:-1]
            batch_size, seq_length = input_shape[0], input_shape[1]
            device = hidden_states.device

        # GPT2Attention mask.
        if attention_mask is not None:
            if batch_size <= 0:
                raise ValueError("batch_size has to be defined and > 0")
            attention_mask = attention_mask.view(batch_size, -1)
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, None, None, :]

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and the dtype's smallest value for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.
            attention_mask = attention_mask.to(dtype=self.dtype)    # fp16 compatibility
            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.add_cross_attention and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # head_mask has shape n_layer x batch x n_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if stage_manager.is_first_stage():
            if position_ids is not None:
                position_ids = position_ids.view(-1, input_shape[-1])
            else:
                position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
                position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

            if inputs_embeds is None:
                inputs_embeds = self.wte(input_ids)
            position_embeds = self.wpe(position_ids)
            hidden_states = inputs_embeds + position_embeds
            if token_type_ids is not None:
                token_type_embeds = self.wte(token_type_ids)
                hidden_states = hidden_states + token_type_embeds
            hidden_states = self.drop(hidden_states)

        output_shape = input_shape + (hidden_states.size(-1),)

        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

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        all_hidden_states = () if output_hidden_states else None

        # Going through held blocks.
        start_idx, end_idx = stage_index[0], stage_index[1]
        for i, layer_past in zip(range(start_idx, end_idx), past_key_values):
            block = self.h[i]
            # Model parallel
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                # Ensure layer_past is on same device as hidden_states (might not be correct)
                if layer_past is not None:
                    layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
                # Ensure that attention_mask is always on the same device as hidden_states
                if attention_mask is not None:
                    attention_mask = attention_mask.to(hidden_states.device)
                if isinstance(head_mask, torch.Tensor):
                    head_mask = head_mask.to(hidden_states.device)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):

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

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    None,
                    attention_mask,
                    head_mask[i],
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=attention_mask,
                    head_mask=head_mask[i],
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

            # Model Parallel: If it's the last layer for that device, put things on the next device
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
                        hidden_states = hidden_states.to("cuda:" + str(k + 1))

        if stage_manager.is_last_stage():
            hidden_states = self.ln_f(hidden_states)

        hidden_states = hidden_states.view(output_shape)

        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        return {'hidden_states': hidden_states, 'past_key_values': presents}

    @staticmethod
    def gpt2_lmhead_model_forward(
            self: GPT2LMHeadModel,
            input_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
            attention_mask: Optional[torch.FloatTensor] = None,
            token_type_ids: Optional[torch.LongTensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            head_mask: Optional[torch.FloatTensor] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            encoder_attention_mask: 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) -> Union[Dict, Tuple, CausalLMOutputWithCrossAttentions]:
        r"""
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
                `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
                are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`

            This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.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

        # If is first stage and after warmup, go throught lm_head first
        if stage_manager.is_first_stage() and hidden_states is not None:
            lm_logits = self.lm_head(hidden_states)
            return {'logits': lm_logits}

        # Not first stage or before warmup, go through gpt2 model
        outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
                                                          input_ids,
                                                          past_key_values=past_key_values,
                                                          attention_mask=attention_mask,
                                                          token_type_ids=token_type_ids,
                                                          position_ids=position_ids,
                                                          head_mask=head_mask,
                                                          inputs_embeds=inputs_embeds,
                                                          encoder_hidden_states=encoder_hidden_states,
                                                          encoder_attention_mask=encoder_attention_mask,
                                                          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)

        return outputs
