import json
import logging
import os
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, TYPE_CHECKING, Union

import numpy as np
from golem.core.log import default_log
from golem.utilities.data_structures import ensure_wrapped_in_sequence

from fedot.core.constants import AUTO_PRESET_NAME, BEST_QUALITY_PRESET_NAME
from fedot.core.repository.dataset_types import DataTypesEnum
from fedot.core.repository.json_evaluation import import_enums_from_str, import_strategy_from_str, read_field
from fedot.core.repository.tasks import Task, TaskTypesEnum

EXTRA_TS_INSTALLED = True
try:
    from gph import ripser_parallel as ripser
    dummy_var = ripser  # for pep8
except ModuleNotFoundError:
    EXTRA_TS_INSTALLED = False

if TYPE_CHECKING:
    from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy

AVAILABLE_REPO_NAMES = ['all', 'model', 'data_operation', 'automl']


@dataclass
class OperationMetaInfo:
    id: str
    input_types: List[DataTypesEnum]
    output_types: List[DataTypesEnum]
    task_type: List[TaskTypesEnum]
    supported_strategies: Union['EvaluationStrategy', Dict[str, 'EvaluationStrategy']]
    allowed_positions: List[str]
    tags: Optional[List[str]] = None
    presets: Optional[List[str]] = None

    def current_strategy(self, task: TaskTypesEnum) -> Optional['EvaluationStrategy']:
        """
        Gets available processing strategies depending on the selected task

        Args:
            task: machine learning task (e.g. regression and classification)

        Returns:
            supported strategies for task
        """

        if isinstance(self.supported_strategies, dict):
            return self.supported_strategies.get(task, None)
        return self.supported_strategies


def run_once(function):
    def wrapper(*args, **kwargs):
        if not wrapper.has_run:
            wrapper.has_run = True
            return function(*args, **kwargs)

    wrapper.has_run = False
    return wrapper


class OperationTypesRepository:
    """Class for connecting models and data operations with json files with
    its descriptions and metadata
    """

    __initialized_repositories__ = {}
    # The later the tag, the higher its priority in case of intersection
    DEFAULT_MODEL_TAGS = ['linear', 'non_linear', 'custom_model', 'tree', 'boosting', 'ts_model', 'deep']
    DEFAULT_DATA_OPERATION_TAGS = [
        'data_source', 'feature_scaling', 'imputation', 'feature_reduction', 'feature_engineering', 'encoding',
        'filtering', 'feature_selection', 'ts_to_table', 'smoothing', 'ts_to_ts', 'text', 'decompose', 'imbalanced',
        'data_source_img', 'data_source_text', 'data_source_table', 'data_source_ts', 'feature_space_transformation',
    ]

    __repository_dict__ = {
        'model': {'file': 'model_repository.json', 'initialized_repo': None, 'default_tags': DEFAULT_MODEL_TAGS},
        'data_operation': {'file': 'data_operation_repository.json', 'initialized_repo': None,
                           'default_tags': DEFAULT_DATA_OPERATION_TAGS},
        'automl': {'file': 'automl_repository.json', 'initialized_repo': None, 'default_tags': []}

    }

    def __init__(self, operation_type: str = 'model'):
        self.log = default_log(self)

        self._tags_excluded_by_default = ['non-default', 'expensive', 'deprecated']
        OperationTypesRepository.init_default_repositories()

        self.operation_type = operation_type
        self.repository_name = []
        self._repo = []
        self.default_tags = []
        if operation_type == 'all':
            for op_type in OperationTypesRepository.__repository_dict__.keys():
                self.repository_name.append(OperationTypesRepository.__repository_dict__[op_type]['file'])
                operations = OperationTypesRepository.__repository_dict__[op_type]['initialized_repo']
                self.default_tags += OperationTypesRepository.__repository_dict__[op_type]['default_tags']

                if operations is not None:
                    for operation in operations:
                        if operation not in self._repo:
                            self._repo.append(operation)

        else:
            self.repository_name = OperationTypesRepository.__repository_dict__[operation_type]['file']
            self._repo = OperationTypesRepository.__repository_dict__[operation_type]['initialized_repo']
            self.default_tags = OperationTypesRepository.__repository_dict__[operation_type]['default_tags']

    @classmethod
    def get_available_repositories(cls):
        operation_types = []
        for t in cls.__repository_dict__:
            if cls.__repository_dict__[t]['initialized_repo'] is not None:
                operation_types.append(t)
        return operation_types

    @classmethod
    def init_automl_repository(cls):
        default_automl_repo_file = cls.__repository_dict__['automl']['file']
        return cls.assign_repo('automl', default_automl_repo_file)

    @classmethod
    @run_once
    def init_default_repositories(cls):
        # default model repo
        default_model_repo_file = cls.__repository_dict__['model']['file']
        cls.assign_repo('model', default_model_repo_file)

        # default data_operation repo
        default_data_operation_repo_file = cls.__repository_dict__['data_operation']['file']
        cls.assign_repo('data_operation', default_data_operation_repo_file)

    @classmethod
    def assign_repo(cls, operation_type: str, repo_file: str):
        if operation_type not in cls.__repository_dict__:
            raise Warning(f'The {operation_type} is not supported. The model type will be set')

        repo_path = create_repository_path(repo_file)
        if repo_file not in cls.__initialized_repositories__.keys():
            cls.__initialized_repositories__[repo_file] = cls._initialise_repo(repo_path)
        cls.__repository_dict__[operation_type]['file'] = repo_file
        cls.__repository_dict__[operation_type]['initialized_repo'] = cls.__initialized_repositories__[repo_file]

        return OperationTypesRepository(operation_type)

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):
        self.repo_path = None
        OperationTypesRepository.__repository_dict__[self.operation_type]['initialized_repo'] = None
        default_model_repo_file = OperationTypesRepository.__repository_dict__['model']['file']
        OperationTypesRepository.assign_repo('model', default_model_repo_file)

    def __repr__(self):
        return f"{self.__class__.__name__} for {self.repository_name}"

    @property
    def is_initialized(self):
        return OperationTypesRepository.__repository_dict__[self.operation_type]['initialized_repo'] is None

    @classmethod
    def _initialise_repo(cls, repo_path: str) -> List[OperationMetaInfo]:
        """Method parse ``JSON`` repository with operations descriptions and
        wrapped information into :obj:`OperationMetaInfo`, then put it into the list

        Returns:
            List[OperationMetaInfo]: list with :obj:`OperationMetaInfo` for every operation from ``json`` repository
        """

        repository_json = load_repository(repo_path)

        metadata_json = repository_json['metadata']
        operations_json = repository_json['operations']

        operations_list = []
        for current_operation_key in operations_json:
            # Get information about operation
            # properties - information about operation by key, for example tags
            # metadata - information about meta of the operation
            properties = operations_json.get(current_operation_key)
            metadata = metadata_json[properties['meta']]

            task_types = import_enums_from_str(metadata['tasks'])
            input_type = import_enums_from_str(properties.get('input_type', metadata.get('input_type')))
            output_type = import_enums_from_str(properties.get('output_type', metadata.get('output_type')))

            # Get available strategies for obtained metadata
            supported_strategies = OperationTypesRepository.get_strategies_by_metadata(metadata)

            accepted_node_types = read_field(metadata, 'accepted_node_types', ['any'])
            forbidden_node_types = read_field(metadata, 'forbidden_node_types', [])

            # Get tags for meta and for operation
            meta_tags = read_field(metadata, 'tags', [])
            operation_tags = read_field(properties, 'tags', [])
            presets = read_field(properties, 'presets', [])

            allowed_positions = ['primary', 'secondary', 'root']

            if accepted_node_types and accepted_node_types != 'all':
                allowed_positions = accepted_node_types
            if forbidden_node_types:
                allowed_positions = [pos for pos in allowed_positions if
                                     pos not in forbidden_node_types]

            # Unit tags
            tags = meta_tags + operation_tags

            operation = OperationMetaInfo(id=current_operation_key,
                                          input_types=input_type,
                                          output_types=output_type,
                                          task_type=task_types,
                                          supported_strategies=supported_strategies,
                                          allowed_positions=allowed_positions,
                                          tags=tags,
                                          presets=presets)
            operations_list.append(operation)

        return operations_list

    @staticmethod
    def get_strategies_by_metadata(metadata: dict) -> Union['EvaluationStrategy', Dict[str, 'EvaluationStrategy']]:
        """
        Obtains strategy instance by the metadata

        Args:
            metadata: information about meta of the operation

        Returns:
            available strategies for current metadata
        """
        strategies_json = metadata['strategies']
        if isinstance(strategies_json, list):
            supported_strategies = import_strategy_from_str(strategies_json)
        elif isinstance(strategies_json, dict):
            supported_strategies = {}
            for strategy_dct_key, strategy_str_value in strategies_json.items():
                # Convert string into class path for import
                import_path = import_enums_from_str(strategy_dct_key)
                strategy_class = import_strategy_from_str(strategy_str_value)

                supported_strategies.update({import_path: strategy_class})
        else:
            raise TypeError('strategies are of unknown type')
        return supported_strategies

    def operation_info_by_id(self, operation_id: str) -> Optional[OperationMetaInfo]:
        """ Get operation by it's name (id) """

        operation_id = get_operation_type_from_id(operation_id)

        operations_with_id = [m for m in self._repo if m.id == operation_id]
        if len(operations_with_id) > 1:
            raise ValueError('Several operations with same id in repository')
        if len(operations_with_id) == 0:
            self.log.warning(f'Operation {operation_id} not found in the repository')
            return None
        return operations_with_id[0]

    def operations_with_tag(self, tags: List[str], is_full_match: bool = False):
        """ Method returns operations from repository with specific tags

                :param tags: list of required tags
                :param is_full_match: are all tags are required

                :return list of suitable operations names
        """
        operations_info = [m for m in self._repo if
                           _is_operation_contains_tag(tags, m.tags, is_full_match)]
        return [m.id for m in operations_info]

    def suitable_operation(self, task_type: TaskTypesEnum = None,
                           data_type: DataTypesEnum = None,
                           tags: List[str] = None, is_full_match: bool = False,
                           forbidden_tags: List[str] = None,
                           preset: str = None) -> List[str]:
        """Method returns operations from repository for desired task and / or
        tags. Filtering method.

        Args:
            task_type: task to filter
            data_type: data type to filter
            tags: operations with which tags are required
            is_full_match: requires all tags to match, or at least one
            forbidden_tags: operations with such tags shouldn't be returned
            preset: return operations from desired preset
        """

        if not forbidden_tags:
            forbidden_tags = []

        if not tags:
            for excluded_default_tag in self._tags_excluded_by_default:
                # Forbidden tags by default
                forbidden_tags.append(excluded_default_tag)

        no_task = task_type is None
        operations_info = []
        for o in self._repo:
            is_desired_task = task_type in o.task_type or no_task
            tags_good = not tags or _is_operation_contains_tag(tags, o.tags, is_full_match)
            tags_bad = not forbidden_tags or not _is_operation_contains_tag(forbidden_tags, o.tags, False)
            is_desired_preset = _is_operation_contains_preset(o.presets, preset)
            if is_desired_task and tags_good and tags_bad and is_desired_preset:
                operations_info.append(o)

        if data_type:
            # ignore text and image data types: there are no operations with these `input_type`
            ignore_data_type = data_type in [DataTypesEnum.text, DataTypesEnum.image]
            if data_type == DataTypesEnum.ts:
                valid_data_types = [DataTypesEnum.ts, DataTypesEnum.table]
            else:
                valid_data_types = ensure_wrapped_in_sequence(data_type)
            if not ignore_data_type:
                operations_info = [o for o in operations_info if
                                   np.any([data_type in o.input_types for data_type in valid_data_types])]

        return [m.id for m in operations_info]

    @property
    def operations(self):
        return self._repo

    def get_first_suitable_operation_tag(self, operation: str, tags_to_find: Optional[List[str]] = None) \
            -> Optional[str]:
        """Finds the first suitable tag for the operation in the repository.

        Args:
            operation: name of the operation
            tags_to_find: list of suitable tags. The later the tag, the higher its priority in case of intersection

        Returns:
            Optional[List[str]]: first suitable tag or ``None``
        """

        tags_to_find = tags_to_find or self.default_tags

        info = self.operation_info_by_id(operation)
        if info is None:
            return None
        for tag in reversed(tags_to_find):
            if tag in info.tags:
                return tag
        return None


def get_visualization_tags_map() -> Dict[str, List[str]]:
    """
    Returns map between repository tags and list of corresponding models for visualizations.
    """
    # Search for tags.
    operations_map = {}
    for repo_name in ('model', 'data_operation'):
        repo = OperationTypesRepository(repo_name)
        for operation in repo.operations:
            tag = repo.get_first_suitable_operation_tag(operation.id, repo.default_tags)
            operations_map[tag] = (operations_map.get(tag) or []) + [operation.id]
    # Sort tags.
    tags_model = OperationTypesRepository.DEFAULT_MODEL_TAGS
    tags_data = OperationTypesRepository.DEFAULT_DATA_OPERATION_TAGS
    operations_map = {tag: operations_map[tag] for tag in tags_model + tags_data if tag in operations_map}
    return operations_map


def _is_operation_contains_tag(candidate_tags: List[str],
                               operation_tags: List[str],
                               is_full_match: bool) -> bool:
    """The function checks which operations are suitable for the selected tags

    Args:
        candidate_tags: ``list`` with tags that the operation must have in order
            to fit the selected task
        operation_tags: ``list`` with tags with names as in repository json file
            which correspond to the considering operation
        is_full_match: requires all tags to match, or at least one

    Returns:
        bool: is there a match on the tags
    """

    matches = (tag in operation_tags for tag in candidate_tags)
    if is_full_match:
        return all(matches)
    else:
        return any(matches)


def _is_operation_contains_preset(operation_presets: List[str], preset: str) -> bool:
    """Checking whether the operation is suitable for current preset
    """

    if preset is None:
        # None means that best_quality preset are using so return all operations
        return True

    return preset in operation_presets


def atomized_model_type():
    return 'atomized_operation'


def atomized_model_meta_tags():
    return ['random'], ['any'], ['atomized']


def get_operations_for_task(task: Optional[Task], data_type: Optional[DataTypesEnum] = None, mode='all', tags=None,
                            forbidden_tags=None, preset: str = None):
    """Function returns aliases of operations.

    Args:
        task: task to solve
        data_type: type of input data
        mode: mode to return operations

            .. details:: the possible parameters of ``mode``:

                - ``all`` -> return list with all operations
                - ``model`` -> return only list with models
                - ``data_operation`` -> return only list with data_operations

        tags: tags for grabbing when filtering
        forbidden_tags: tags for skipping when filtering
        preset: operations from this preset will be obtained

    Returns:
        list:  operation aliases
    """

    # Preset None means that all operations will be returned
    if preset is not None:
        if BEST_QUALITY_PRESET_NAME in preset or AUTO_PRESET_NAME in preset:
            preset = None

    if task is not None and task.task_type is TaskTypesEnum.ts_forecasting and not EXTRA_TS_INSTALLED:
        if not forbidden_tags:
            forbidden_tags = []
        logging.log(100,
                    "Extra dependencies for time series forecasting are not installed. It can infuence the "
                    "performance. Please install it by 'pip install fedot[extra]'")
        forbidden_tags.append('ts-extra')
    task_type = task.task_type if task else None
    if mode in AVAILABLE_REPO_NAMES:
        repo = OperationTypesRepository(mode)
        model_types = repo.suitable_operation(task_type, data_type=data_type, tags=tags, forbidden_tags=forbidden_tags,
                                              preset=preset)
        return model_types
    else:
        raise ValueError(f'Such mode "{mode}" is not supported')


def get_operation_type_from_id(operation_id):
    operation_type = _operation_name_without_postfix(operation_id)
    return operation_type


def _operation_name_without_postfix(operation_id):
    """
    Args:
        operation_id: operation name with optional postfix - text after / sign
    Returns:
        operation type - all characters before postfix (all characters if no postfix found)
    """

    postfix_sign = '/'
    # if the operation id has custom postfix
    if postfix_sign in operation_id:
        if operation_id.count(postfix_sign) > 1:
            raise ValueError(f'Incorrect number of postfixes in {operation_id}')
        return operation_id.split('/')[0]
    else:
        return operation_id


def load_repository(repo_path: str) -> dict:
    # Loads the repository for various cases and loads the necessary additional data "base_repository.json".
    with open(repo_path) as repository_json_file:
        repository_json = json.load(repository_json_file)

    if 'base_repository' in repository_json:
        base_repository_json_file = create_repository_path(repository_json['base_repository'])

        with open(base_repository_json_file) as repository_json_file:
            base_repository_json = json.load(repository_json_file)

        merged_dict = defaultdict(dict)

        merged_dict.update(base_repository_json)
        for key, nested_dict in repository_json.items():
            if key not in merged_dict:
                merged_dict[key] = nested_dict
            else:
                merged_dict[key].update(nested_dict)

        repository_json = dict(merged_dict)

    return repository_json


def create_repository_path(repository_name: str) -> str:
    # Path till repository file
    file = os.path.join('data', repository_name)
    # Path till current file with script
    repo_folder_path = str(os.path.dirname(__file__))
    repo_path = os.path.join(repo_folder_path, file)

    return repo_path
