from typing import Callable, List, Optional, Set, Union

import torch
import torch.nn as nn

from colossalai.cluster import ProcessGroupMesh
from colossalai.pipeline.schedule.generate import GenerateSchedule
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer import ShardConfig, ShardFormer
from colossalai.shardformer.policies.base_policy import Policy

from .microbatch_manager import MicroBatchManager


class PPInferEngine:
    '''
    PPInferEngine is a class that handles the pipeline parallel inference.

    Args:
        pp_size (int): the number of pipeline stages.
        pp_model (`nn.Module`): the model already in pipeline parallelism style.
        model (`nn.Module`): the model not in pipeline style, and will be modified with `ShardFormer`.
        model_policy (`colossalai.shardformer.policies.base_policy.Policy`): the policy to shardformer model.
        micro_batch_size (int): the micro batch size.
        micro_batch_buffer_size (int): the buffer size for micro batch. Normally, it should be the same as the number of pipeline stages.
        new_length (int): the new length of the input sequence.
        early_stopping (bool): whether to stop early.

    Example:

    ```python
    from colossalai.ppinference import PPInferEngine
    from transformers import GPT2LMHeadModel, GPT2Tokenizer

    model = transformers.GPT2LMHeadModel.from_pretrained('gpt2')
    # assume the model is infered with 4 pipeline stages
    inferengine = PPInferEngine(pp_size=4, model=model, model_policy={Your own policy for pipeline sharding})

    input = ["Hello, my dog is cute, and I like"]
    tokenized_input = tokenizer(input, return_tensors='pt')
    output = engine.inference([tokenized_input])
    ```

    '''

    def __init__(
        self,
        pp_size: int,
        dtype: str = 'fp16',
        pp_model: nn.Module = None,
        model: nn.Module = None,
        model_policy: Policy = None,
        new_length: int = 32,
        micro_batch_size: int = 1,
        micro_batch_buffer_size: int = None,
        verbose: bool = False,
    # TODO: implement early_stopping, and various gerneration options
        early_stopping: bool = False,
        do_sample: bool = False,
        num_beams: int = 1,
    ) -> None:
        assert pp_model or (model and model_policy), "Either pp_model or model with model_policy should be provided."
        self.pp_size = pp_size
        self.pg_mesh = ProcessGroupMesh(pp_size)
        self.stage_manager = PipelineStageManager(self.pg_mesh, 0, True)
        self.mb_manager = MicroBatchManager(self.stage_manager.stage, new_length, micro_batch_size,
                                            micro_batch_buffer_size or pp_size)
        self.verbose = verbose
        self.schedule = GenerateSchedule(self.stage_manager, self.mb_manager, verbose)

        assert dtype in ['fp16', 'fp32', 'bf16'], "dtype should be one of 'fp16', 'fp32', 'bf16'"
        if dtype == 'fp16':
            model.half()
        elif dtype == 'bf16':
            model.to(torch.bfloat16)
        self.model = pp_model or self._shardformer(model, model_policy)

    def inference(self, input_list):
        out, timestamp = self.schedule.generate_step(self.model, iter(input_list))
        if self.verbose:
            return out, timestamp
        else:
            return out

    def _shardformer(self, model, model_policy):
        shardconfig = ShardConfig(
            tensor_parallel_process_group=None,
            pipeline_stage_manager=self.stage_manager,
            enable_tensor_parallelism=False,
            enable_fused_normalization=False,
            enable_all_optimization=False,
            enable_flash_attention=False,
            enable_jit_fused=False,
            enable_sequence_parallelism=False,
        )
        shardformer = ShardFormer(shard_config=shardconfig)
        shard_model, _ = shardformer.optimize(model, model_policy)
        return shard_model.cuda()
