from functools import partial
from typing import Callable, Dict, List, Union

import torch.nn as nn
from torch import Tensor
from torch.nn import Module

from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D

from ..modeling.llama import LlamaPipelineForwards, get_llama_flash_attention_forward
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy']


class LlamaPolicy(Policy):

    def config_sanity_check(self):
        pass

    def preprocess(self):
        if self.shard_config.enable_tensor_parallelism:
            # Resize embedding
            vocab_size = self.model.config.vocab_size
            world_size = self.shard_config.tensor_parallel_size

            if vocab_size % world_size != 0:
                new_vocab_size = vocab_size + world_size - vocab_size % world_size
                self.model.resize_token_embeddings(new_vocab_size)

        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
        from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel

        policy = {}

        if self.shard_config.enable_tensor_parallelism:
            policy[LlamaDecoderLayer] = ModulePolicyDescription(
                attribute_replacement={
                    "self_attn.hidden_size":
                        self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
                    "self_attn.num_heads":
                        self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                },
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="self_attn.q_proj",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.k_proj",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.v_proj",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.o_proj",
                        target_module=Linear1D_Row,
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.gate_proj",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.up_proj",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.down_proj",
                        target_module=Linear1D_Row,
                    )
                ],
            )

            self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
                suffix="embed_tokens",
                target_module=VocabParallelEmbedding1D,
            ),
                                                        policy=policy,
                                                        target_key=LlamaModel)

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            self.append_or_create_submodule_replacement(description=[
                SubModuleReplacementDescription(
                    suffix="input_layernorm",
                    target_module=FusedRMSNorm,
                ),
                SubModuleReplacementDescription(
                    suffix="post_attention_layernorm",
                    target_module=FusedRMSNorm,
                )
            ],
                                                        policy=policy,
                                                        target_key=LlamaDecoderLayer)

            self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
                suffix="norm",
                target_module=FusedRMSNorm,
            ),
                                                        policy=policy,
                                                        target_key=LlamaModel)

        if self.shard_config.enable_flash_attention:
            policy[LlamaAttention] = ModulePolicyDescription(method_replacement={
                'forward': get_llama_flash_attention_forward(),
            })

        return policy

    def postprocess(self):
        return self.model

    def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
        """If under pipeline parallel setting, replacing the original forward method of huggingface
           to customized forward method, and add this changing to policy."""
        if self.pipeline_stage_manager:
            stage_manager = self.pipeline_stage_manager
            if self.model.__class__.__name__ == "LlamaModel":
                module = self.model
            else:
                module = self.model.model

            layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
            method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
            self.append_or_create_method_replacement(description=method_replacement,
                                                     policy=policy,
                                                     target_key=model_cls)

        return

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        assert self.pipeline_stage_manager is not None

        if self.model.__class__.__name__ == 'LlamaModel':
            module = self.model
        else:
            module = self.model.model
        stage_manager = self.pipeline_stage_manager

        held_layers = []
        layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
        if stage_manager.is_first_stage():
            held_layers.append(module.embed_tokens)
        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
        held_layers.extend(module.layers[start_idx:end_idx])
        if stage_manager.is_last_stage():
            held_layers.append(module.norm)

        return held_layers


class LlamaModelPolicy(LlamaPolicy):

    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        policy = super().module_policy()
        from transformers.models.llama.modeling_llama import LlamaModel
        if self.pipeline_stage_manager:
            # set None as default
            self.set_pipeline_forward(model_cls=LlamaModel,
                                      new_forward=LlamaPipelineForwards.llama_model_forward,
                                      policy=policy)
        return policy

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        held_layers = super().get_held_layers()
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        """No shared params in llama model"""
        return []


class LlamaForCausalLMPolicy(LlamaPolicy):

    def module_policy(self):
        from transformers import LlamaForCausalLM

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            # add a new item for casual lm
            new_item = {
                LlamaForCausalLM:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True))
                    ])
            }
            policy.update(new_item)

        if self.pipeline_stage_manager:
            # set None as default
            self.set_pipeline_forward(model_cls=LlamaForCausalLM,
                                      new_forward=LlamaPipelineForwards.llama_for_causal_lm_forward,
                                      policy=policy)

        return policy

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        stage_manager = self.pipeline_stage_manager
        held_layers = super().get_held_layers()
        if stage_manager.is_last_stage():
            held_layers.append(self.model.lm_head)
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        llama_model = self.model.model
        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
            if id(llama_model.embed_tokens.weight) == id(
                    self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1:
                # tie weights
                return [{
                    0: llama_model.embed_tokens.weight,
                    self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight
                }]
        return []


class LlamaForSequenceClassificationPolicy(LlamaPolicy):

    def module_policy(self):
        from transformers import LlamaForSequenceClassification

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            # add a new item for sequence classification
            new_item = {
                LlamaForSequenceClassification:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True))
                    ])
            }
            policy.update(new_item)
        # to be confirmed
        if self.pipeline_stage_manager:
            # set None as default
            self.set_pipeline_forward(model_cls=LlamaForSequenceClassification,
                                      new_forward=LlamaPipelineForwards.llama_for_sequence_classification_forward,
                                      policy=policy)
        return policy

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        stage_manager = self.pipeline_stage_manager
        held_layers = super().get_held_layers()
        if stage_manager.is_last_stage():
            held_layers.append(self.model.score)
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        """No shared params in llama for sequence classification model"""
        return []
