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

import torch.nn as nn
from torch import Tensor
from transformers.modeling_outputs import BaseModelOutputWithPast

import colossalai.shardformer.layer as col_nn
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.modeling.chatglm2 import ChatGLMPipelineForwards
from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
    ChatGLMForConditionalGeneration,
    ChatGLMModel,
    GLMBlock,
)

from ..modeling.chatglm2 import (
    get_chatglm_sequence_parallel_forward_fn,
    get_flash_core_attention_forward,
    get_jit_fused_glm_block_forward,
)
from ..modeling.jit import get_jit_fused_dropout_add_func
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = ['ChatGLMPolicy', 'ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']


class ChatGLMPolicy(Policy):

    def config_sanity_check(self):
        pass

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

        if self.pipeline_stage_manager is not None:
            # the batch_size_dim is bounded to Model
            bsz_dim = 1
            setattr(self.model, 'batch_size_dim', bsz_dim)

        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:

        from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, CoreAttention, GLMBlock

        policy = {}

        use_sequence_parallel = self.shard_config.enable_sequence_parallelism
        overlap = self.shard_config.enable_sequence_overlap
        if self.shard_config.enable_tensor_parallelism:
            policy[ChatGLMModel] = ModulePolicyDescription(attribute_replacement={},
                                                           sub_module_replacement=[
                                                               SubModuleReplacementDescription(
                                                                   suffix="embedding.word_embeddings",
                                                                   target_module=col_nn.VocabParallelEmbedding1D,
                                                               )
                                                           ])

            policy[GLMBlock] = ModulePolicyDescription(
                attribute_replacement={
                    "self_attention.num_attention_heads_per_partition":
                        self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                    "self_attention.projection_size":
                        (self.model.config.kv_channels * self.model.config.num_attention_heads) //
                        self.shard_config.tensor_parallel_size,
                    "self_attention.qkv_hidden_size":
                        (self.model.config.kv_channels * self.model.config.num_attention_heads * 3) //
                        self.shard_config.tensor_parallel_size,
                    "self_attention.core_attention.num_attention_heads_per_partition":
                        self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                    "self_attention.core_attention.hidden_size_per_partition":
                        self.model.config.kv_channels * self.model.config.num_attention_heads //
                        self.shard_config.tensor_parallel_size,
                },
                param_replacement=[],
                sub_module_replacement=[
                    SubModuleReplacementDescription(suffix="self_attention.query_key_value",
                                                    target_module=col_nn.Linear1D_Col,
                                                    kwargs={
                                                        'seq_parallel': use_sequence_parallel,
                                                        'seq_parallel_dim': 0,
                                                        'overlap': overlap
                                                    }),
                    SubModuleReplacementDescription(suffix="self_attention.dense",
                                                    target_module=col_nn.Linear1D_Row,
                                                    kwargs={
                                                        'seq_parallel': use_sequence_parallel,
                                                        'seq_parallel_dim': 0
                                                    }),
                    SubModuleReplacementDescription(
                        suffix="self_attention.core_attention.attention_dropout",
                        target_module=col_nn.DropoutForParallelInput,
                    ),
                ])

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            if not self.model.config.rmsnorm:

                self.append_or_create_submodule_replacement(description=[
                    SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedLayerNorm),
                    SubModuleReplacementDescription(suffix="post_attention_layernorm",
                                                    target_module=col_nn.FusedLayerNorm)
                ],
                                                            policy=policy,
                                                            target_key=GLMBlock)

                if self.model.config.post_layer_norm:
                    self.append_or_create_submodule_replacement(description=[
                        SubModuleReplacementDescription(suffix="encoder.final_layernorm",
                                                        target_module=col_nn.FusedLayerNorm)
                    ],
                                                                policy=policy,
                                                                target_key=ChatGLMModel)

            else:
                self.append_or_create_submodule_replacement(description=[
                    SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedRMSNorm),
                    SubModuleReplacementDescription(suffix="post_attention_layernorm",
                                                    target_module=col_nn.FusedRMSNorm)
                ],
                                                            policy=policy,
                                                            target_key=GLMBlock)

                if self.model.config.post_layer_norm:
                    self.append_or_create_submodule_replacement(description=[
                        SubModuleReplacementDescription(suffix="encoder.final_layernorm",
                                                        target_module=col_nn.FusedRMSNorm)
                    ],
                                                                policy=policy,
                                                                target_key=ChatGLMModel)

        # use flash attention
        if self.shard_config.enable_flash_attention:
            self.append_or_create_method_replacement(description={
                'forward': get_flash_core_attention_forward(),
            },
                                                     policy=policy,
                                                     target_key=CoreAttention)

        # use sequence parallel
        if use_sequence_parallel:
            self.append_or_create_method_replacement(
                description={'forward': get_chatglm_sequence_parallel_forward_fn(self.shard_config)},
                policy=policy,
                target_key=ChatGLMModel)

        # use jit fused operator
        if self.shard_config.enable_jit_fused:
            self.append_or_create_method_replacement(description={
                'forward': get_jit_fused_glm_block_forward(),
                'dropout_add': get_jit_fused_dropout_add_func(),
            },
                                                     policy=policy,
                                                     target_key=GLMBlock)

        return policy

    def postprocess(self):
        return self.model

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

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

        held_layers = []
        layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages)
        if stage_manager.is_first_stage():
            held_layers.append(module.embedding)
        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
        held_layers.extend(module.encoder.layers[start_idx:end_idx])
        if stage_manager.is_last_stage():
            if module.encoder.post_layer_norm:
                held_layers.append(module.encoder.final_layernorm)

        # rotary_pos_emb is needed for all stages
        held_layers.append(module.rotary_pos_emb)

        return held_layers

    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 not self.pipeline_stage_manager:
            raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
        stage_manager = self.pipeline_stage_manager
        if self.model.__class__.__name__ == 'ChatGLMModel':
            module = self.model
        else:
            module = self.model.transformer

        layers_per_stage = Policy.distribute_layers(module.num_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,
                        shard_config=self.shard_config)
        }
        self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)


class ChatGLMModelPolicy(ChatGLMPolicy):

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

    def module_policy(self):
        from transformers.models.gpt2.modeling_gpt2 import GPT2Model

        policy = super().module_policy()

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(model_cls=ChatGLMModel,
                                      new_forward=ChatGLMPipelineForwards.chatglm_model_forward,
                                      policy=policy)
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        return super().get_held_layers()

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


class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy):

    def module_policy(self):
        policy = super().module_policy()

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(model_cls=ChatGLMForConditionalGeneration,
                                      new_forward=ChatGLMPipelineForwards.chatglm_for_conditional_generation_forward,
                                      policy=policy)
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        if self.pipeline_stage_manager.is_last_stage():
            held_layers.append(self.model.transformer.output_layer)
        return held_layers

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