from typing import Dict, Union

import torch.nn as nn

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

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 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)

        return policy

    def postprocess(self):
        return self.model


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)
        return policy


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)
        return policy