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# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Sample Generate LLAMA"""
import os
import sys
import time
import logging
from typing import Union

from torch import distributed as dist
from transformers import AutoTokenizer
from mindspeed_llm import megatron_adaptor
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, \
    get_gpt_layer_local_spec
from megatron.core.transformer.spec_utils import import_module
from megatron.training.initialize import initialize_megatron
from megatron.training import get_args, print_rank_0
from megatron.legacy.model import GPTModel
from megatron.training.arguments import core_transformer_config_from_args
from megatron.training.yaml_arguments import core_transformer_config_from_yaml

from mindspeed_llm.tasks.inference.module import GPTModelInfer, MegatronModuleForCausalLM
from mindspeed_llm.tasks.evaluation.utils import add_text_generate_args
from mindspeed_llm.tasks.evaluation.eval_api.chat import Chat
from mindspeed_llm.tasks.evaluation.eval_impl.boolq_eval import BoolqEval
from mindspeed_llm.tasks.evaluation.eval_impl.gsm8k_eval import Gsm8kEval
from mindspeed_llm.tasks.evaluation.eval_impl.mmlu_eval import MmluEval
from mindspeed_llm.tasks.evaluation.eval_impl.ceval_exam import CEvalExam
from mindspeed_llm.tasks.evaluation.eval_impl.bbh_eval import BBHEval
from mindspeed_llm.tasks.evaluation.eval_impl.agi_eval import AGIEvalExam
from mindspeed_llm.tasks.evaluation.eval_impl.human_eval import HumanEval

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
logging.getLogger().setLevel(logging.INFO)
logger = logging.getLogger(__name__)


def model_provider(pre_process=True, post_process=True) -> Union[GPTModelInfer, GPTModel]:
    """Builds the model.

    If you set the use_mcore_models to True, it will return the mcore GPT model and if not the legacy GPT model.

    Args:
        pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
        post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.


    Returns:
        Union[GPTModelInfer, GPTModel]: The returned model
    """
    args = get_args()
    use_te = args.transformer_impl == "transformer_engine"

    print_rank_0('building GPT model ...')
    # Experimental loading arguments from yaml
    if args.yaml_cfg is not None:
        config = core_transformer_config_from_yaml(args, "language_model")
    else:
        config = core_transformer_config_from_args(args)

    if args.use_mcore_models:
        if args.spec is not None:
            transformer_layer_spec = import_module(args.spec)
        else:
            if use_te:
                transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm)
            else:
                transformer_layer_spec = get_gpt_layer_local_spec(args.num_experts, args.moe_grouped_gemm)

        model = GPTModelInfer(
            config=config,
            transformer_layer_spec=transformer_layer_spec,
            vocab_size=args.padded_vocab_size,
            max_sequence_length=args.max_position_embeddings,
            pre_process=pre_process,
            post_process=post_process,
            fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
            parallel_output=False,
            share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
            position_embedding_type=args.position_embedding_type,
            rotary_percent=args.rotary_percent,
            seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor
        )
    else:
        if not args.context_parallel_size == 1:
            raise ValueError("Context parallelism is only supported with Megatron Core!")

        model = GPTModel(
            config,
            parallel_output=False,
            pre_process=pre_process,
            post_process=post_process
        )

    return model


def get_result(result, tokenizer):
    if result:
        final_results = []
        if isinstance(result[0], list):
            for idx, res in enumerate(result[0]):
                final_result = [res]
                if result[1][idx][0][tokenizer.encode("Yes")[-1]] >= result[1][idx][0][tokenizer.encode("No")[-1]]:
                    final_result.append('T')
                else:
                    final_result.append('F')
                final_results.append(final_result)
        else:
            final_result = [result[0]]
            if result[1][0][tokenizer.encode("Yes")[-1]] >= result[1][0][tokenizer.encode("No")[-1]]:
                final_result.append('T')
            else:
                final_result.append('F')
            final_results.append(final_result)
    else:
        final_results = None
    return final_results


class LLMChat(Chat):
    def __init__(self, llm_args, model, tokenizer):
        self.args = llm_args
        self.model = model
        self.tokenizer = tokenizer
        self.template = "{instruction}"

    def chat(self, instruction, history):
        instruction_temp = None
        if self.args.prompt_type is None:
            instruction_temp = [self.template.format(instruction=ins) if (self.tokenizer.chat_template is None or self.args.no_chat_template) else self.tokenizer.apply_chat_template([{"role": "user", "content": ins}]) for ins in instruction]
        else:
            instruction_temp = instruction

        result = self.model.generate(
            instruction_temp,
            do_sample=False,
            max_new_tokens=self.args.max_new_tokens,
            stream=False,
            return_output_log_probs=True
        )
        return get_result(result, self.tokenizer), dist.get_rank()

    def beam_search_chat(self, instruction, history):
        instruction_temp = None
        if self.args.prompt_type is None:
            instruction_temp = self.template.format(instruction=instruction) if (self.tokenizer.chat_template is None or self.args.no_chat_template) else self.tokenizer.apply_chat_template([{"role": "user", "content": instruction}])
        else:
            instruction_temp = instruction

        result = self.model.generate(
            instruction_temp,
            do_sample=False,
            max_new_tokens=self.args.max_new_tokens,
            stream=False,
            num_beams=4,
            top_k=50,
            top_p=0.95,
            length_penalty=0.7
        )
        return [result], dist.get_rank()


def mmlu(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None
    for path in eval_args.task_data_path:
        if 'mmlu' in path:
            data_path = path
    try:
        if data_path:
            mmlu_eval = MmluEval(test_dir=data_path, eval_args=eval_args)
            answer, score_df = mmlu_eval.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def gsm8k(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None
    for path in eval_args.task_data_path:
        if 'gsm8k' in path:
            data_path = path
    try:
        if data_path:
            gsm8k_eval = Gsm8kEval(test_dir=data_path, eval_args=eval_args)
            answer, score_df = gsm8k_eval.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def boolq(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None

    for path in eval_args.task_data_path:
        if 'boolq' in path:
            data_path = path
    try:
        if data_path:
            boolq_eval = BoolqEval(test_dir=data_path, eval_args=eval_args)
            answer, score_df = boolq_eval.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def ceval(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None

    for path in eval_args.task_data_path:
        if 'ceval' in path:
            data_path = path
    try:
        if data_path:
            ceval_exam = CEvalExam(test_dir=data_path, eval_args=eval_args)
            answer, score_df = ceval_exam.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def human_eval(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None

    for path in eval_args.task_data_path:
        if 'human_eval' in path:
            data_path = path
    try:
        if data_path:
            human_eval_exam = HumanEval(test_dir=data_path, eval_args=eval_args)
            answer, score_df = human_eval_exam.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def agi_eval(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None

    for path in eval_args.task_data_path:
        if 'agieval' in path:
            data_path = path
    try:
        if data_path:
            agieval_exam = AGIEvalExam(test_dir=data_path, eval_args=eval_args)
            answer, score_df = agieval_exam.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def bbh_eval(eval_args, agent):
    data_path = None
    answer = None 
    score_df = None

    for path in eval_args.task_data_path:
        if 'bbh' in path:
            data_path = path
    try:
        if data_path:
            bbh = BBHEval(test_dir=data_path, eval_args=eval_args)
            answer, score_df = bbh.eval(chat=agent)
            if dist.get_rank() == 0:
                logger.info('\n{}'.format(score_df))
    except Exception as e:
        logger.info(e)

    return answer, score_df


def main():
    initialize_megatron(extra_args_provider=add_text_generate_args,
                        args_defaults={'no_load_rng': True,
                                       'no_load_optim': True})
    args = get_args()
    model = MegatronModuleForCausalLM.from_pretrained(
        model_provider=model_provider,
        pretrained_model_name_or_path=args.load
    )
    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path, trust_remote_code=True, local_files_only=True)

    rank = dist.get_rank()
    if 'mmlu' in args.task:
        a = time.time()
        mmlu(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'MMLU Running Time:, {time.time() - a}')
    if 'gsm8k' in args.task:
        a = time.time()
        gsm8k(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'GSM8k Running Time: {time.time() - a}')
    if 'boolq' in args.task:
        a = time.time()
        boolq(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'Boolq Running Time: {time.time() - a}')
    if 'ceval' in args.task:
        a = time.time()
        ceval(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'Ceval Running Time: {time.time() - a}')
    if 'bbh' in args.task:
        a = time.time()
        bbh_eval(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'bbh Running Time: {time.time() - a}')
    if 'agieval' in args.task:
        a = time.time()
        agi_eval(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'agi_eval Running Time: {time.time() - a}')
    if 'human_eval' in args.task:
        a = time.time()
        human_eval(args, LLMChat(args, model, tokenizer))
        if rank == 0:
            logger.info(f'Human_eval Running Time: {time.time() - a}')


if __name__ == "__main__":
    main()