# flake8: noqa
# yapf: disable
from typing import Dict, List, Optional, Union

from opencompass.models.base import BaseModel, LMTemplateParser
from opencompass.models.base_api import APITemplateParser
from opencompass.registry import MODELS
from opencompass.utils.logging import get_logger
from opencompass.utils.prompt import PromptList

PromptType = Union[PromptList, str]


def _get_stopping_criteria(stop_words, tokenizer, batch_size):
    from transformers import (PreTrainedTokenizer, StoppingCriteria,
                              StoppingCriteriaList)

    class MultiTokenEOSCriteria(StoppingCriteria):
        """Criteria to stop on the specified multi-token sequence."""

        def __init__(self, sequence: str, tokenizer: PreTrainedTokenizer, batch_size: int):
            self.done_tracker = [False] * batch_size
            self.sequence = sequence
            self.sequence_ids = tokenizer.encode(sequence, add_special_tokens=False)
            self.sequence_id_len = len(self.sequence_ids)
            self.tokenizer = tokenizer

        def __call__(self, input_ids, scores, **kwargs) -> bool:
            # compare the last len(stop) tokens
            lookback_ids_batch = input_ids[:, -self.sequence_id_len:]
            lookback_tokens_batch = self.tokenizer.batch_decode(lookback_ids_batch)
            for i, done in enumerate(self.done_tracker):
                if done:
                    continue
                self.done_tracker[i] = self.sequence in lookback_tokens_batch[i]
            return False not in self.done_tracker

    criteria = []
    for stop_word in stop_words:
        c = MultiTokenEOSCriteria(stop_word, tokenizer, batch_size)
        criteria.append(c)
    criteria = StoppingCriteriaList(criteria)
    return criteria

def _get_possible_max_seq_len(max_seq_len, path):
    if max_seq_len is not None:
        return max_seq_len

    from transformers import AutoConfig
    config = AutoConfig.from_pretrained(path, trust_remote_code=True)
    possible_keys = [
        'max_position_embeddings',
        'seq_length',
        'model_max_length',
    ]
    for k in possible_keys:
        if hasattr(config, k):
            return getattr(config, k)
    raise ValueError('max_seq_len is not provided and cannot be inferred from the model config.')


def _convert_chat_messages(inputs):
    outputs = []
    for _input in inputs:
        messages = []
        if isinstance(_input, str):
            messages.append({'role': 'HUMAN', 'prompt': _input})
        else:
            for item in _input:
                role = {
                    'HUMAN': 'user',
                    'BOT': 'assistant',
                    'SYSTEM': 'system',
                }[item['role']]
                messages.append({'role': role, 'content': item['prompt']})
        outputs.append(messages)
    return outputs


def _format_with_fast_chat_template(inputs: List[str], name: str='vicuna'):
    try:
        from fastchat.model import get_conversation_template
    except ImportError:
        raise ModuleNotFoundError('fastchat not found. Please install with\npip install "fschat[model_worker,webui]"')

    outputs = []
    for _input in inputs:
        template = get_conversation_template(name)
        for item in _input:
            if item['role'] == 'user':
                template.append_message(template.roles[0], item['content'])
            elif item['role'] == 'assistant':
                template.append_message(template.roles[1], item['content'])
            elif item['role'] == 'system':
                continue
            else:
                raise ValueError(f'Unknown role {item["role"]}')
        template.append_message(template.roles[1], None)
        outputs.append(template.get_prompt())
    return outputs


def _get_meta_template(meta_template):
    default_meta_template = dict(
        round=[
            dict(role='HUMAN', api_role='HUMAN'),
            dict(role='BOT', api_role='BOT', generate=True),
        ]
    )
    return APITemplateParser(meta_template or default_meta_template)


def _set_model_kwargs_torch_dtype(model_kwargs):
    import torch
    if 'torch_dtype' not in model_kwargs:
        torch_dtype = torch.float16
    else:
        torch_dtype = {
            'torch.float16': torch.float16,
            'torch.bfloat16': torch.bfloat16,
            'torch.float': torch.float,
            'auto': 'auto',
            'None': None,
        }.get(model_kwargs['torch_dtype'])
    if torch_dtype is not None:
        model_kwargs['torch_dtype'] = torch_dtype
    return model_kwargs


@MODELS.register_module()
class HuggingFacewithChatTemplate(BaseModel):

    def __init__(self,
                 path: str,
                 model_kwargs: dict = dict(),
                 tokenizer_path: Optional[str] = None,
                 tokenizer_kwargs: dict = dict(),
                 peft_path: Optional[str] = None,
                 peft_kwargs: dict = dict(),
                 tokenizer_only: bool = False,
                 generation_kwargs: dict = dict(),
                 max_seq_len: Optional[int] = None,
                 meta_template: Optional[Dict] = None,
                 pad_token_id: Optional[int] = None,
                 fastchat_template: Optional[str] = None,
                 stop_words: Optional[str] = [],
                 **other_kwargs):

        self.logger = get_logger()
        self.path = path
        self.tokenizer_only = tokenizer_only
        self.template_parser = _get_meta_template(meta_template)
        self.max_seq_len = _get_possible_max_seq_len(max_seq_len, path)
        self._load_tokenizer(tokenizer_path or path, tokenizer_kwargs, pad_token_id)
        if not tokenizer_only:
            self._load_model(path=path, kwargs=model_kwargs, peft_path=peft_path, peft_kwargs=peft_kwargs)
        self.generation_kwargs = generation_kwargs
        self.fastchat_template = fastchat_template
        self.stop_words = stop_words

        for k, v in other_kwargs.items():
            if v is not None:
                self.logger.warning(f'Unused argument {k}={v}')

    def _load_tokenizer(self, path: Optional[str], kwargs: dict, pad_token_id: Optional[int] = None):
        from transformers import AutoTokenizer, GenerationConfig

        DEFAULT_TOKENIZER_KWARGS = dict(padding_side='left', truncation_side='left', use_fast=False, trust_remote_code=True)
        tokenizer_kwargs = DEFAULT_TOKENIZER_KWARGS
        tokenizer_kwargs.update(kwargs)
        self.tokenizer = AutoTokenizer.from_pretrained(path, **tokenizer_kwargs)

        # A patch for some models without pad_token_id
        if pad_token_id is not None:
            if self.tokenizer.pad_token_id is None:
                self.logger.debug(f'Using {pad_token_id} as pad_token_id')
            elif self.tokenizer.pad_token_id != pad_token_id:
                self.logger.warning(f'pad_token_id is not consistent. Using {pad_token_id} as pad_token_id')
            self.tokenizer.pad_token_id = pad_token_id
            return
        if self.tokenizer.pad_token_id is not None:
            return
        self.logger.warning('pad_token_id is not set for the tokenizer.')
        generation_config = GenerationConfig.from_pretrained(path)
        if generation_config.pad_token_id is not None:
            self.logger.warning(f'Using {generation_config.pad_token_id} as pad_token_id.')
            self.tokenizer.pad_token_id = generation_config.pad_token_id
            return
        if self.tokenizer.eos_token_id is not None:
            self.logger.warning(f'Using eos_token_id {self.tokenizer.eos_token_id} as pad_token_id.')
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
            return
        raise ValueError('pad_token_id is not set for this tokenizer. Please set `pad_token_id={PAD_TOKEN_ID}` in model_cfg.')

    def _load_model(self, path: str, kwargs: dict, peft_path: Optional[str] = None, peft_kwargs: dict = dict()):
        from transformers import AutoModel, AutoModelForCausalLM

        DEFAULT_MODEL_KWARGS = dict(device_map='auto', trust_remote_code=True)
        model_kwargs = DEFAULT_MODEL_KWARGS
        model_kwargs.update(kwargs)
        model_kwargs = _set_model_kwargs_torch_dtype(model_kwargs)

        try:
            self.model = AutoModelForCausalLM.from_pretrained(path, **model_kwargs)
        except ValueError:
            self.model = AutoModel.from_pretrained(path, **model_kwargs)

        if peft_path is not None:
            from peft import PeftModel
            peft_kwargs['is_trainable'] = False
            self.model = PeftModel.from_pretrained(self.model, peft_path, **peft_kwargs)

        self.model.eval()
        self.model.generation_config.do_sample = False

    def generate(self,
                 inputs: List[str],
                 max_out_len: int,
                 min_out_len: Optional[int] = None,
                 stopping_criteria: List[str] = [],
                 **kwargs) -> List[str]:
        messages = _convert_chat_messages(inputs)
        batch_size = len(messages)

        tokenize_kwargs = dict(
            return_tensors='pt',
            padding=True,
            truncation=True,
            add_special_tokens=True,
            max_length=self.max_seq_len
        )
        if self.fastchat_template:
            messages = _format_with_fast_chat_template(messages, self.fastchat_template)
            tokens = self.tokenizer.batch_encode_plus(messages, **tokenize_kwargs)
        else:
            messages = [self.tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False) for m in messages]
            tokenize_kwargs['add_special_tokens'] = False
            tokens = self.tokenizer.batch_encode_plus(messages, **tokenize_kwargs)

        tokens = {k: v.to(self.model.device) for k, v in tokens.items()}

        generation_kwargs = self.generation_kwargs.copy()
        generation_kwargs.update(kwargs)
        stopping_criteria = list(set(stopping_criteria + self.stop_words))
        if stopping_criteria:
            generation_kwargs['stopping_criteria'] = _get_stopping_criteria(stopping_criteria, self.tokenizer, batch_size)
        if max_out_len is not None:
            generation_kwargs['max_new_tokens'] = max_out_len
        if min_out_len is not None:
            generation_kwargs['min_new_tokens'] = min_out_len
        generation_kwargs['pad_token_id'] = self.tokenizer.pad_token_id

        # step-2: conduct model forward to generate output
        outputs = self.model.generate(**tokens, **generation_kwargs)
        outputs = outputs[:, tokens['input_ids'].shape[1]:]

        # step-3: decode the output
        decodeds = self.tokenizer.batch_decode(outputs)
        for stop in stopping_criteria:
            decodeds = [t.split(stop)[0] for t in decodeds]

        return decodeds

    def get_token_len(self, prompt: str) -> int:
        m = _convert_chat_messages([prompt])[0]
        t = self.tokenizer.apply_chat_template(m, add_generation_prompt=True, return_dict=True)
        return len(t['input_ids'])

def  _convert_base_messages(inputs):
    outputs = []
    for _input in inputs:
        if isinstance(_input, str):
            outputs.append(_input)
        else:
            messages = []
            for item in _input:
                messages.append(item['prompt'])
            outputs.append(''.join(messages))
    return outputs


class HuggingFaceBaseModel(HuggingFacewithChatTemplate):

    def __init__(self,
                 path: str,
                 model_kwargs: dict = dict(),
                 tokenizer_path: Optional[str] = None,
                 tokenizer_kwargs: dict = dict(),
                 peft_path: Optional[str] = None,
                 peft_kwargs: dict = dict(),
                 tokenizer_only: bool = False,
                 generation_kwargs: dict = dict(),
                 max_seq_len: Optional[int] = None,
                 pad_token_id: Optional[int] = None,
                 stop_words: Optional[str] = [],
                 **other_kwargs):

        self.logger = get_logger()
        self.path = path
        self.tokenizer_only = tokenizer_only
        self.template_parser = LMTemplateParser()
        self.max_seq_len = _get_possible_max_seq_len(max_seq_len, path)
        self._load_tokenizer(tokenizer_path or path, tokenizer_kwargs, pad_token_id)
        if not tokenizer_only:
            self._load_model(path=path, kwargs=model_kwargs, peft_path=peft_path, peft_kwargs=peft_kwargs)
        self.generation_kwargs = generation_kwargs
        self.stop_words = stop_words

        for k, v in other_kwargs.items():
            if v is not None:
                self.logger.warning(f'Unused argument {k}={v}')

    def generate(self,
                 inputs: List[str],
                 max_out_len: int,
                 min_out_len: Optional[int] = None,
                 stopping_criteria: List[str] = [],
                 **kwargs) -> List[str]:
        messages = _convert_base_messages(inputs)
        batch_size = len(messages)

        tokenize_kwargs = dict(
            return_tensors='pt',
            padding=True,
            truncation=True,
            add_special_tokens=True,
            max_length=self.max_seq_len
        )
        tokens = self.tokenizer.batch_encode_plus(messages, **tokenize_kwargs)
        tokens = {k: v.to(self.model.device) for k, v in tokens.items()}

        generation_kwargs = self.generation_kwargs.copy()
        generation_kwargs.update(kwargs)
        stopping_criteria = list(set(stopping_criteria + self.stop_words))
        if stopping_criteria:
            generation_kwargs['stopping_criteria'] = _get_stopping_criteria(stopping_criteria, self.tokenizer, batch_size)
        if max_out_len is not None:
            generation_kwargs['max_new_tokens'] = max_out_len
        if min_out_len is not None:
            generation_kwargs['min_new_tokens'] = min_out_len
        generation_kwargs['pad_token_id'] = self.tokenizer.pad_token_id

        # step-2: conduct model forward to generate output
        outputs = self.model.generate(**tokens, **generation_kwargs)
        outputs = outputs[:, tokens['input_ids'].shape[1]:]

        # step-3: decode the output
        decodeds = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
        for stop in stopping_criteria:
            decodeds = [token.split(stop)[0] for token in decodeds]

        return decodeds

    def get_ppl(self, inputs: List[str], mask_length: Optional[List[int]] = None) -> List[float]:
        """Get perplexity scores given a list of inputs.

        Args:
            inputs (List[str]): A list of strings.
            mask_length (Optional[List[int]]): A list of mask lengths. If
                provided, the perplexity scores will be calculated with the
                first mask_length[i] tokens masked out. It's okay to skip
                its implementation if advanced features in PPLInfernecer is
                not needed.

        Returns:
            List[float]: A list of perplexity scores.
        """
        assert self.tokenizer.pad_token
        import torch
        import torch.nn.functional as F
        pad_token_id = self.tokenizer.pad_token_id
        messages = _convert_base_messages(inputs)

        tokenize_kwargs = dict(
            return_tensors='pt',
            padding=True,
            truncation=True,
            add_special_tokens=True,
            max_length=self.max_seq_len
        )
        tokens = self.tokenizer.batch_encode_plus(messages, **tokenize_kwargs)
        tokens = {k: v.to(self.model.device) for k, v in tokens.items()}
        outputs = self.model(**tokens)[0]

        batch_size, seq_len, vocab_size = outputs.shape
        shift_logits = outputs[:, :-1, :].contiguous().float()
        shift_labels = tokens['input_ids'][:, 1:].contiguous()
        loss = F.cross_entropy(
            shift_logits.view(-1, vocab_size),
            shift_labels.view(-1),
            ignore_index=pad_token_id,
            reduction='none').view(batch_size, seq_len - 1)
        lens = (tokens['input_ids'] != pad_token_id).sum(-1).cpu().numpy()

        if mask_length is not None:
            import numpy as np
            mask = torch.zeros_like(shift_labels)  # [batch,seqlen]
            for i in range(len(mask)):
                for j in range(mask_length[i] - 1, len(mask[i])):
                    mask[i][j] = 1
            loss = loss * mask
            lens -= np.array(mask_length)

        ce_loss = loss.float().sum(-1).cpu().detach().numpy() / lens
        return ce_loss

    def get_loglikelihood(self, inputs: List[str], conts:  List[str]) -> List[float]:
        mask_length = [self.get_token_len(c, add_special_tokens=False) for c in conts]
        return - self.get_ppl(inputs, mask_length)

    def get_token_len(self, prompt: str, add_special_tokens: bool=True) -> int:
        m = _convert_base_messages([prompt])[0]
        t = self.tokenizer(m, add_special_tokens=add_special_tokens)
        return len(t['input_ids'])
