# Copyright (c) Alibaba, Inc. and its affiliates.
import ast
import os
from collections import Counter
from contextlib import contextmanager
from typing import Any, Callable, Dict, List, Optional, Union

import numpy as np
from datasets import Dataset as HfDataset
from datasets import Image
from datasets import IterableDataset as HfIterableDataset
from datasets import Sequence, Value

from swift.llm import history_to_messages
from swift.utils import get_logger, is_dist, is_master, safe_ddp_context

DATASET_TYPE = Union[HfDataset, HfIterableDataset]

logger = get_logger()


class RowPreprocessor:
    standard_keys = [
        'messages', 'rejected_response', 'rejected_images', 'label', 'images', 'videos', 'audios', 'tools', 'objects',
        'channel', 'margin'
    ]

    def __init__(self,
                 *,
                 columns: Optional[Dict[str, str]] = None,
                 dataset_sample: Optional[int] = None,
                 random_state: Optional[Union[np.random.RandomState, int]] = 42,
                 traceback_limit: int = 10) -> None:
        self.columns = columns or {}
        self.origin_columns = self.columns.copy()  # Higher priority and raise Error
        images_keys = ['images', 'image']
        audios_keys = ['audios', 'audio']
        videos_keys = ['videos', 'video']
        for mm_type in ['images', 'audios', 'videos']:
            keys = locals()[f'{mm_type}_keys']
            for key in keys:
                self.columns[key] = mm_type

        self.traceback_limit = traceback_limit
        self._traceback_counter = 0
        self.dataset_sample = dataset_sample
        if not isinstance(random_state, np.random.RandomState):
            random_state = np.random.RandomState(random_state)
        self.random_state = random_state

    @staticmethod
    def _check_messages(row: Dict[str, Any]) -> None:
        if 'messages' not in row:
            return
        messages = row['messages']
        assert len(messages) > 0, f'messages: {messages}'
        # fix swift/SlimOrca
        for message in messages:
            keys = set(message.keys()) - {'role', 'content'}
            for key in keys:
                message.pop(key)

        for message in messages:
            role, content = message['role'], message['content']
            # The terms 'tool' and 'tool_response' have the same meaning, ensuring compatibility.
            assert role in {'system', 'user', 'tool_call', 'tool_response', 'tool', 'assistant'}, f'message: {message}'
            assert content is not None, f'message: {message}'

    @staticmethod
    def _cast_mm_data(row: Dict[str, Any]) -> None:
        for key in ['images', 'rejected_images']:
            images = row.get(key, None)
            if images is None:
                continue

            if isinstance(images, str) or (isinstance(images, list) and images and isinstance(images[0], str)):
                if isinstance(images, str):
                    images = [images]
                for i, image in enumerate(images):
                    images[i] = {'bytes': None, 'path': image}
                row[key] = images
            elif isinstance(images, dict):
                row[key] = [images]

        for key in ['videos', 'audios']:
            mm_data = row.get(key)
            if mm_data is None:
                continue
            elif isinstance(mm_data, str):
                row[key] = [mm_data]

    @staticmethod
    def _check_rejected_response(row: Dict[str, Any]) -> None:
        if 'rejected_messages' in row:
            chosen_messages = row['messages']
            rejected_messages = row['rejected_messages']
            messages = []
            rejected_response = None
            for chosen_user, chosen_assistant, rejected_user, rejected_assistant in zip(
                    chosen_messages[::2], chosen_messages[1::2], rejected_messages[::2], rejected_messages[1::2]):
                assert chosen_user == rejected_user
                messages.append(chosen_user)
                messages.append(chosen_assistant)
                if chosen_assistant != rejected_assistant:
                    rejected_response = rejected_assistant['content']
            row['messages'] = messages
            row['rejected_response'] = rejected_response

        if 'rejected_response' in row:
            messages = row['messages']
            rejected_response = row['rejected_response']
            if rejected_response is None or rejected_response == messages[-1]['content']:
                raise ValueError(f'rejected_response: {rejected_response}')

    def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        raise NotImplementedError

    def prepare_dataset(self, dataset: DATASET_TYPE) -> DATASET_TYPE:
        return dataset

    @staticmethod
    def batched_to_rows(batched_row: Dict[str, Any]):
        keys = list(batched_row.keys())
        batch_size = len(batched_row[keys[0]])
        return [{key: batched_row[key][i] for key in keys} for i in range(batch_size)]

    @staticmethod
    def rows_to_batched(rows: List[Dict[str, Any]]):
        batched = {}
        for i, row in enumerate(rows):
            for k, v in row.items():
                if k not in batched:
                    batched[k] = [None] * i
                batched[k].append(v)
            # Make all the lengths of v the same.
            for k in set(batched.keys()) - set(row.keys()):
                batched[k].append(None)
        return batched

    @staticmethod
    def _remove_prefix_keys(row, prefix: str):
        for k in list(row.keys()):
            if k.startswith(prefix):
                new_k = k[len(prefix):]
                new_v = row.pop(k)
                if new_k not in row:
                    row[new_k] = new_v

    @staticmethod
    def _check_objects(row):
        objects = row.get('objects')
        if objects is None:
            return
        new_objects = {}
        # Ensure the order
        for k in ['ref', 'bbox', 'bbox_type', 'image_id']:
            if k in objects.keys():
                new_objects[k] = objects[k]
        row['objects'] = new_objects
        bbox = new_objects['bbox']

        # check bbox
        for box in bbox:
            assert len(box) in {2, 4}, f'len(box): {len(box)}'
            if len(box) == 2:
                continue
            if box[0] > box[2]:
                box[0], box[2] = box[2], box[0]
            if box[1] > box[3]:
                box[1], box[3] = box[3], box[1]

    def batched_preprocess(self, batched_row: Dict[str, Any], *, strict: bool,
                           ignore_max_length_error: bool) -> Dict[str, Any]:
        from ...template import MaxLengthError
        batched_row = dict(batched_row)
        assert len(batched_row) > 0
        self._remove_prefix_keys(batched_row, '__@')  # compat streaming
        rows = self.batched_to_rows(batched_row)

        new_rows = []
        for row in rows:
            try:
                row = self.preprocess(row)
                # support [row1, row2, ...]
                if row is None:
                    row = []
                if isinstance(row, dict):
                    row = [row]
                for r in row:
                    self._check_objects(r)
                    self._check_messages(r)
                    self._check_rejected_response(r)
                    self._cast_mm_data(r)
            except Exception as e:
                if strict:
                    logger.warning('To avoid errors, you can pass `strict=False`.')
                    raise
                if isinstance(e, MaxLengthError) and ignore_max_length_error:
                    pass
                elif self.traceback_limit is not None and self._traceback_counter < self.traceback_limit:
                    import traceback
                    logger.info(traceback.format_exc())
                    logger.warning('👆👆👆There are errors in the dataset, the data will be deleted')
                    self._traceback_counter += 1
                row = []
            new_rows += row
        res = self.rows_to_batched(new_rows)
        self._remove_prefix_keys(res, '__#')  # compat GRPO
        if len(res) == 0:
            res['messages'] = []

        return res

    @staticmethod
    def get_features_dataset(dataset: DATASET_TYPE) -> DATASET_TYPE:
        if dataset.features is None:
            assert isinstance(dataset, HfIterableDataset)
            dataset = dataset._resolve_features()
        return dataset

    @staticmethod
    def safe_rename_columns(dataset, columns):
        dataset = RowPreprocessor.get_features_dataset(dataset)
        columns_keys = {k.lower(): k for k in dataset.features.keys()}  # lower -> lower/upper
        safe_columns = {columns_keys[k.lower()]: v for k, v in columns.items() if k.lower() in columns_keys}

        counter = Counter(safe_columns.values())
        for k, new_k in list(safe_columns.items()):
            if counter[new_k] > 1:
                # For example, if "response" and "answer" match, then no processing is done.
                safe_columns.pop(k)
                continue

        # e.g. Keep {'query': 'query'} to ensure that the query has the highest priority.
        safe_columns = {k: v for k, v in safe_columns.items() if k != v}
        if safe_columns:
            dataset = dataset.rename_columns(safe_columns)

        return dataset

    def _rename_columns(self, dataset: DATASET_TYPE) -> DATASET_TYPE:
        dataset = self.safe_rename_columns(dataset, self.origin_columns)
        dataset = self.safe_rename_columns(dataset, self.columns)
        if isinstance(dataset, HfIterableDataset):
            # fix: https://github.com/huggingface/datasets/issues/6408
            columns = {k: f'__@{k}' for k in RowPreprocessor.standard_keys if k in dataset.features}
            if columns:
                dataset = dataset.rename_columns(columns)
        return dataset

    @staticmethod
    def remove_useless_columns(dataset: DATASET_TYPE) -> DATASET_TYPE:
        dataset = RowPreprocessor.get_features_dataset(dataset)
        features = dataset.features
        k_list = [k for k in RowPreprocessor.standard_keys if k in features]
        if len(k_list) != len(features):
            dataset = dataset.select_columns(k_list)
        return dataset

    @staticmethod
    @contextmanager
    def _patch_arrow_writer():
        # fix AI-ModelScope/ms_agent_for_agentfabric:all
        from datasets.arrow_writer import ArrowWriter

        def _new_init(self, schema=None, features=None, *args, **kwargs):

            if features is not None:
                features['messages'] = [{'role': Value(dtype='string'), 'content': Value(dtype='string')}]
                features['images'] = [{'bytes': Value(dtype='binary'), 'path': Value(dtype='string')}]
                features['objects'] = {
                    'ref': Sequence(feature=Value(dtype='string'), length=-1),
                    'bbox': Sequence(feature=Sequence(feature=Value(dtype='float64'), length=-1), length=-1),
                    'bbox_type': Value(dtype='string'),
                    'image_id': Sequence(feature=Value(dtype='int64'), length=-1),
                }
            ArrowWriter.__origin_init__(self, schema, features, *args, **kwargs)

        ArrowWriter.__origin_init__ = ArrowWriter.__init__
        ArrowWriter.__init__ = _new_init
        try:
            yield
        finally:
            ArrowWriter.__init__ = ArrowWriter.__origin_init__
            del ArrowWriter.__origin_init__

    def _cast_pil_image(self, dataset):
        features = dataset.features
        for col in ['images', 'rejected_images']:
            if (col in features and isinstance(features[col], Image) and getattr(features[col], 'decode', False)):
                dataset = dataset.cast_column(col, Image(decode=False))
        return dataset

    def __call__(
        self,
        dataset: DATASET_TYPE,
        *,
        num_proc: int = 1,
        load_from_cache_file: bool = True,
        strict: bool = False,
        batch_size: Optional[int] = None,
    ) -> DATASET_TYPE:
        from ..utils import sample_dataset
        if batch_size is None:
            batch_size = 1000 if isinstance(dataset, HfDataset) else 16
        if self.dataset_sample is not None:
            dataset = sample_dataset(dataset, self.dataset_sample, True, self.random_state)

        map_kwargs = {'batched': True, 'batch_size': batch_size}
        if isinstance(dataset, HfDataset):
            if not load_from_cache_file and is_dist() and not is_master():
                load_from_cache_file = True
            map_kwargs.update({
                'num_proc': num_proc,
                'load_from_cache_file': load_from_cache_file,
            })
        # compat GRPO: The solution field will be retained.
        dataset = RowPreprocessor.get_features_dataset(dataset)
        if 'solution' in dataset.features:
            with safe_ddp_context(None, True):
                dataset = dataset.map(lambda x: {'__#solution': x['solution']}, **map_kwargs)
        dataset = self._rename_columns(dataset)
        dataset = self.prepare_dataset(dataset)
        dataset = self._cast_pil_image(dataset)

        ignore_max_length_error = True if isinstance(dataset, HfDataset) and num_proc > 1 else False
        with self._patch_arrow_writer(), safe_ddp_context(None, True):
            try:
                dataset_mapped = dataset.map(
                    self.batched_preprocess,
                    fn_kwargs={
                        'strict': strict,
                        'ignore_max_length_error': ignore_max_length_error
                    },
                    remove_columns=list(dataset.features.keys()),
                    **map_kwargs)
            except NotImplementedError:
                pass
        if isinstance(dataset_mapped, HfDataset) and len(dataset) != len(dataset_mapped):
            logger.info(
                f'Dataset filtered, origin length: {len(dataset)}, filtered dataset length: {len(dataset_mapped)}')

        return dataset_mapped


class ResponsePreprocessor(RowPreprocessor):
    """Dataset compatible with older versions of ms-swift"""

    def __init__(self, *, columns: Optional[Dict[str, str]] = None, **kwargs) -> None:
        super().__init__(columns=columns, **kwargs)
        system_keys = ['system', 'system_prompt']
        query_keys = ['query', 'prompt', 'input', 'instruction', 'question', 'problem']
        response_keys = ['response', 'answer', 'output', 'targets', 'target', 'answer_key', 'answers', 'solution'
                         ] + ['text', 'completion', 'content']
        for key in system_keys:
            self.columns[key] = 'system'
        for key in query_keys:
            self.columns[key] = 'query'
        for key in response_keys:
            self.columns[key] = 'response'

    def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        response = row.pop('response', None)
        if response is not None:
            if isinstance(response, (list, tuple)):
                from transformers.utils import strtobool
                # sometimes response is a list, pick one randomly
                if strtobool(os.environ.get('RANDOM_DATASET_RESPONSE', 'True')):
                    response = self.random_state.choice(response)
                else:
                    response = response[0]
        history = row.pop('history', None) or []
        query = row.pop('query', None)
        system = row.pop('system', None)
        if isinstance(history, str):  # e.g. "[['query1', 'response1']]"
            history = ast.literal_eval(history)
        history.append([query, response])

        row.update({'messages': history_to_messages(history, system)})
        return row


class AlpacaPreprocessor(ResponsePreprocessor):

    @classmethod
    def concat_inst_input(cls, instruction, input_):
        if instruction and input_:
            query = f'{instruction}\n{input_}'
        else:
            query = instruction or input_
        assert isinstance(query, str), f'query: {query}'
        return query

    def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        instruction = row.pop('instruction', None)
        input_ = row.pop('input', None)
        output = row.pop('output', None)
        if output is not None:
            row['response'] = output
        row['query'] = self.concat_inst_input(instruction, input_)
        return super().preprocess(row)


def default_repair_messages(s: Union[str, Any]) -> Any:
    if isinstance(s, str):
        return ast.literal_eval(s)
    return s


class MessagesPreprocessor(RowPreprocessor):

    def __init__(
            self,
            *,
            # If set to None, automatic matching will be performed.
            role_key: Optional[str] = None,  # 'role', 'from'
            content_key: Optional[str] = None,  # 'content', 'value'
            user_role: Optional[str] = None,  # 'user', 'human'
            assistant_role: Optional[str] = None,  # 'assistant', 'gpt', 'bot'
            system_role: str = 'system',
            # 'conversation', 'conversations' -> 'messages'
            columns: Optional[Dict[str, str]] = None,
            repair_messages: Callable[[Union[str, List[Dict[str, str]]]],
                                      Optional[List[Dict[str, str]]]] = default_repair_messages,
            inner_key: Optional[str] = None,
            **kwargs):
        super().__init__(columns=columns, **kwargs)
        self.role_keys = ['role', 'from'] if role_key is None else [role_key]
        self.content_keys = ['content', 'value'] if content_key is None else [content_key]
        self.user_roles = ['user', 'human'] if user_role is None else [user_role]
        self.assistant_roles = ['assistant', 'gpt', 'bot'] if assistant_role is None else [assistant_role]
        self.tool_call_roles = ['function_call']
        self.tool_response_roles = ['function_response', 'observation', 'observations']

        self.system_role = system_role
        self.repair_messages = repair_messages
        self.inner_key = inner_key

        message_keys = ['messages', 'conversation', 'conversations']
        for key in message_keys:
            self.columns[key] = 'messages'
        # sharegptq
        system_keys = ['system', 'system_prompt']
        if system_role not in system_keys:
            system_keys.append(system_role)
        for key in system_keys:
            self.columns[key] = 'system'

    @staticmethod
    def _is_sharegpt_format(message: Dict[str, str]) -> bool:
        if 'role' in message or 'content' in message:
            return False
        return True

    def sharegpt_to_messages(self, messages: List[Dict[str, str]], system: Optional[str]) -> List[Dict[str, str]]:
        self._to_std_key(messages, 'user', self.user_roles)
        self._to_std_key(messages, 'assistant', self.assistant_roles)
        new_messages = []
        if system is not None:
            new_messages.append({'role': 'system', 'content': system})
        for message in messages:
            user_message = {'role': 'user', 'content': message['user']}
            assistant_message = {'role': 'assistant', 'content': message['assistant']}
            new_messages.append(user_message)
            new_messages.append(assistant_message)
        return new_messages

    def to_std_messages(self, messages: List[Dict[str, str]], system: Optional[str]) -> None:
        if messages[0]['role'] == self.system_role:
            messages[0]['role'] = 'system'
        elif system is not None:
            messages.insert(0, {'role': 'system', 'content': system})
        for message in messages:
            role = message['role']
            if role in self.user_roles:
                message['role'] = 'user'
            elif role in self.assistant_roles:
                message['role'] = 'assistant'
            elif role.replace('-', '_') in self.tool_call_roles:
                message['role'] = 'tool_call'
            elif role.replace('-', '_') in self.tool_response_roles:
                message['role'] = 'tool_response'

    @staticmethod
    def _to_std_key(messages: List[Dict[str, str]], std_key: str, optional_keys: List[str]) -> None:
        for message in messages:
            for key in optional_keys:
                if key in message:
                    message[std_key] = message.pop(key)

    def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        if 'rejected_messages' in row:
            row['rejected_messages'] = MessagesPreprocessor.preprocess(
                self, {'messages': row['rejected_messages']})['messages']
        messages = row['messages']
        if self.inner_key is not None:
            messages = messages[self.inner_key]
        messages: Optional[List[Dict[str, str]]] = self.repair_messages(messages)
        if not messages or isinstance(messages, str):
            return
        self._to_std_key(messages, 'role', self.role_keys)
        self._to_std_key(messages, 'content', self.content_keys)
        system = row.pop('system', None)
        if self._is_sharegpt_format(messages[0]):
            messages = self.sharegpt_to_messages(messages, system)
        else:
            self.to_std_messages(messages, system)  # inplace
        row['messages'] = messages
        return row


class ClsPreprocessor(ResponsePreprocessor):

    def preprocess(self, row: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        res = super().preprocess(row)
        res['label'] = int(res['label'])
        return res


class AutoPreprocessor:

    def __init__(self, *, columns: Optional[Dict[str, str]] = None, **kwargs) -> None:
        self.columns = columns or {}
        self.kwargs = kwargs

    def _get_preprocessor(self, dataset: DATASET_TYPE) -> RowPreprocessor:
        features = dataset.features
        for key in ['conversation', 'conversations', 'messages']:
            if key in features:
                return MessagesPreprocessor(**self.kwargs)
        if 'instruction' in features and 'input' in features:
            return AlpacaPreprocessor(**self.kwargs)
        return ResponsePreprocessor(**self.kwargs)

    def __call__(
        self,
        dataset: DATASET_TYPE,
        *,
        num_proc: int = 1,
        load_from_cache_file: bool = True,
        strict: bool = False,
    ) -> DATASET_TYPE:
        dataset = RowPreprocessor.safe_rename_columns(dataset, self.columns)
        preprocessor = self._get_preprocessor(dataset)
        return preprocessor(dataset, num_proc=num_proc, load_from_cache_file=load_from_cache_file, strict=strict)
