import logging
from copy import deepcopy
from typing import Any, List, Optional, Sequence, Tuple, Union

import numpy as np
import pandas as pd
from golem.core.dag.graph_utils import graph_structure
from golem.core.log import Log, default_log
from golem.core.optimisers.opt_history_objects.opt_history import OptHistory
from golem.core.tuning.simultaneous import SimultaneousTuner
from golem.utilities.data_structures import ensure_wrapped_in_sequence
from golem.visualisation.opt_viz_extra import visualise_pareto

from fedot.api.api_utils.api_composer import ApiComposer
from fedot.api.api_utils.api_data import ApiDataProcessor
from fedot.api.api_utils.data_definition import FeaturesType, TargetType
from fedot.api.api_utils.input_analyser import InputAnalyser
from fedot.api.api_utils.params import ApiParams
from fedot.api.api_utils.predefined_model import PredefinedModel
from fedot.core.constants import DEFAULT_API_TIMEOUT_MINUTES, DEFAULT_TUNING_ITERATIONS_NUMBER
from fedot.core.data.data import InputData, OutputData
from fedot.core.data.multi_modal import MultiModalData
from fedot.core.data.visualisation import plot_biplot, plot_forecast, plot_roc_auc
from fedot.core.optimisers.objective import PipelineObjectiveEvaluate
from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
from fedot.core.pipelines.pipeline import Pipeline
from fedot.core.pipelines.ts_wrappers import convert_forecast_to_output, out_of_sample_ts_forecast
from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
from fedot.core.repository.metrics_repository import MetricCallable
from fedot.core.repository.tasks import TaskParams, TaskTypesEnum
from fedot.core.utils import set_random_seed
from fedot.explainability.explainer_template import Explainer
from fedot.explainability.explainers import explain_pipeline
from fedot.preprocessing.base_preprocessing import BasePreprocessor
from fedot.remote.remote_evaluator import RemoteEvaluator
from fedot.utilities.composer_timer import fedot_composer_timer
from fedot.utilities.define_metric_by_task import MetricByTask
from fedot.utilities.memory import MemoryAnalytics
from fedot.utilities.project_import_export import export_project_to_zip, import_project_from_zip

NOT_FITTED_ERR_MSG = 'Model not fitted yet'


class Fedot:
    """ The main class for FEDOT AutoML API.

    Alternatively, may be initialized using the class :class:`~fedot.api.builder.FedotBuilder`,
    where all the optional AutoML parameters are documented and separated by meaning.

    Args:
        problem: name of the modelling problem to solve.
            .. details:: Possible options:

                - ``classification`` -> for classification task
                - ``regression`` -> for regression task
                - ``ts_forecasting`` -> for time series forecasting task

        timeout: time for model design (in minutes): ``None`` or ``-1`` means infinite time.
        task_params: additional parameters of the task.
        seed: value for a fixed random seed.
        logging_level: logging levels are the same as in
            `built-in logging library <https://docs.python.org/3/library/logging.html>`_.

            .. details:: Possible options:

                - ``50`` -> critical
                - ``40`` -> error
                - ``30`` -> warning
                - ``20`` -> info
                - ``10`` -> debug
                - ``0`` -> nonset

        safe_mode: if set ``True`` it will cut large datasets to prevent memory overflow and use label encoder
            instead of OneHot encoder if summary cardinality of categorical features is high.
            Default value is ``False``.

        n_jobs: num of ``n_jobs`` for parallelization (set to ``-1`` to use all cpu's). Defaults to ``-1``.

        composer_tuner_params: Additional optional parameters. See their documentation at the methods of
            :class:`~fedot.api.builder.FedotBuilder`.
    """

    def __init__(self,
                 problem: str,
                 timeout: Optional[float] = DEFAULT_API_TIMEOUT_MINUTES,
                 task_params: TaskParams = None,
                 seed: Optional[int] = None,
                 logging_level: int = logging.ERROR,
                 safe_mode: bool = False,
                 n_jobs: int = -1,
                 **composer_tuner_params
                 ):

        set_random_seed(seed)
        self.log = self._init_logger(logging_level)

        # Attributes for dealing with metrics, data sources and hyperparameters
        self.params = ApiParams(composer_tuner_params, problem, task_params, n_jobs, timeout, seed)

        default_metrics = MetricByTask.get_default_quality_metrics(self.params.task.task_type)
        passed_metrics = self.params.get('metric')
        self.metrics = ensure_wrapped_in_sequence(passed_metrics) if passed_metrics else default_metrics

        self.api_composer = ApiComposer(self.params, self.metrics)

        # Initialize data processors for data preprocessing and preliminary data analysis
        self.data_processor = ApiDataProcessor(task=self.params.task,
                                               use_input_preprocessing=self.params.get('use_input_preprocessing'))
        self.data_analyser = InputAnalyser(safe_mode=safe_mode)

        self.target: Optional[TargetType] = None
        self.prediction: Optional[OutputData] = None
        self._is_in_sample_prediction = True
        self.train_data: Optional[InputData] = None
        self.test_data: Optional[InputData] = None

        # Outputs
        self.current_pipeline: Optional[Pipeline] = None
        self.best_models: Sequence[Pipeline] = ()
        self.history: Optional[OptHistory] = None

        fedot_composer_timer.reset_timer()

    def fit(self,
            features: FeaturesType,
            target: TargetType = 'target',
            predefined_model: Union[str, Pipeline] = None) -> Pipeline:
        """Composes and fits a new pipeline, or fits a predefined one.

        Args:
            features: train data feature values in one of the supported features formats.
            target: train data target values in one of the supported target formats.
            predefined_model: the name of a single model or a :class:`Pipeline` instance, or ``auto``.
                With any value specified, the method does not perform composing and tuning.
                In case of ``auto``, the method generates a single initial assumption and then fits
                the created pipeline.

        Returns:
            :class:`Pipeline` object.
        """

        MemoryAnalytics.start()

        self.target = target

        with fedot_composer_timer.launch_data_definition('fit'):
            self.train_data = self.data_processor.define_data(features=features, target=target, is_predict=False)

        self.params.update_available_operations_by_preset(self.train_data)

        if self.params.get('use_input_preprocessing'):
            # Launch data analyser - it gives recommendations for data preprocessing
            recommendations_for_data, recommendations_for_params = \
                self.data_analyser.give_recommendations(input_data=self.train_data,
                                                        input_params=self.params)
            self.data_processor.accept_and_apply_recommendations(input_data=self.train_data,
                                                                 recommendations=recommendations_for_data)
            self.params.accept_and_apply_recommendations(input_data=self.train_data,
                                                         recommendations=recommendations_for_params)
        else:
            recommendations_for_data = None

        self._init_remote_if_necessary()

        if isinstance(self.train_data, InputData) and self.params.get('use_auto_preprocessing'):
            with fedot_composer_timer.launch_preprocessing():
                self.train_data = self.data_processor.fit_transform(self.train_data)

        # TODO: Workaround for AtomizedModel
        init_asm = self.params.data.get('initial_assumption')
        if predefined_model is None:
            if isinstance(init_asm, Pipeline) and ("atomized" in init_asm.descriptive_id):
                self.log.message('Composition for AtomizedModel currently unavailable')
                predefined_model = init_asm

        with fedot_composer_timer.launch_fitting():
            if predefined_model is not None:
                # Fit predefined model and return it without composing
                self.current_pipeline = PredefinedModel(
                    predefined_model, self.train_data, self.log,
                    use_input_preprocessing=self.params.get('use_input_preprocessing'),
                    api_preprocessor=self.data_processor.preprocessor,
                ).fit()
            else:
                self.current_pipeline, self.best_models, self.history = self.api_composer.obtain_model(self.train_data)

                if self.current_pipeline is None:
                    raise ValueError('No models were found')

                full_train_not_preprocessed = deepcopy(self.train_data)
                # Final fit for obtained pipeline on full dataset

                with fedot_composer_timer.launch_train_inference():
                    if self.history and not self.history.is_empty() or not self.current_pipeline.is_fitted:
                        self._train_pipeline_on_full_dataset(recommendations_for_data, full_train_not_preprocessed)
                        self.log.message('Final pipeline was fitted')
                    else:
                        self.log.message('Already fitted initial pipeline is used')

        # Merge API & pipelines encoders if it is required
        self.current_pipeline.preprocessor = BasePreprocessor.merge_preprocessors(
            api_preprocessor=self.data_processor.preprocessor,
            pipeline_preprocessor=self.current_pipeline.preprocessor,
            use_auto_preprocessing=self.params.get('use_auto_preprocessing')
        )

        self.log.message(f'Final pipeline: {graph_structure(self.current_pipeline)}')

        MemoryAnalytics.finish()

        return self.current_pipeline

    def tune(self,
             input_data: Optional[FeaturesType] = None,
             target: TargetType = 'target',
             metric_name: Optional[Union[str, MetricCallable]] = None,
             iterations: int = DEFAULT_TUNING_ITERATIONS_NUMBER,
             timeout: Optional[float] = None,
             cv_folds: Optional[int] = None,
             n_jobs: Optional[int] = None,
             show_progress: bool = False) -> Pipeline:
        """Method for hyperparameters tuning of current pipeline

        Args:
            input_data: data for tuning pipeline in one of the supported formats.
            target: data target values in one of the supported target formats.
            metric_name: name of metric for quality tuning.
            iterations: numbers of tuning iterations.
            timeout: time for tuning (in minutes). If ``None`` or ``-1`` means tuning until max iteration reach.
            cv_folds: number of folds on data for cross-validation.
            n_jobs: num of ``n_jobs`` for parallelization (``-1`` for use all cpu's).
            show_progress: shows progress of tuning if ``True``.

        Returns:
            :class:`Pipeline` object.
        """
        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        with fedot_composer_timer.launch_tuning('post'):
            if input_data is None:
                input_data = self.train_data
            else:
                input_data = self.data_processor.define_data(features=input_data, target=target, is_predict=False)
            cv_folds = cv_folds or self.params.get('cv_folds')
            n_jobs = n_jobs or self.params.n_jobs

            metric = metric_name if metric_name else self.metrics[0]

            pipeline_tuner = (TunerBuilder(self.params.task)
                              .with_tuner(SimultaneousTuner)
                              .with_cv_folds(cv_folds)
                              .with_n_jobs(n_jobs)
                              .with_metric(metric)
                              .with_iterations(iterations)
                              .with_timeout(timeout)
                              .build(input_data))

            self.current_pipeline = pipeline_tuner.tune(self.current_pipeline, show_progress=show_progress)
            self.api_composer.was_tuned = pipeline_tuner.was_tuned

            # Tuner returns a not fitted pipeline, and it is required to fit on train dataset
            self.current_pipeline.fit(self.train_data)

        return self.current_pipeline

    def predict(self,
                features: FeaturesType,
                save_predictions: bool = False,
                in_sample: bool = True,
                validation_blocks: Optional[int] = None) -> np.ndarray:
        """Predicts new target using already fitted model.

        For time-series performs forecast with depth ``forecast_length`` if ``in_sample=False``.
        If ``in_sample=True`` performs in-sample forecast using features as sample.

        Args:
            features: an array with features of test data.
            save_predictions: if ``True`` - save predictions as csv-file in working directory.
            in_sample: used while time-series prediction. If ``in_sample=True`` performs in-sample forecast using
                features with number if iterations specified in ``validation_blocks``.
            validation_blocks: number of validation blocks for in-sample forecast.

        Returns:
            An array with prediction values.
        """
        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        with fedot_composer_timer.launch_data_definition('predict'):
            self.test_data = self.data_processor.define_data(target=self.target, features=features, is_predict=True)
        self._is_in_sample_prediction = in_sample

        if isinstance(self.test_data, InputData) and self.params.get('use_auto_preprocessing'):
            with fedot_composer_timer.launch_preprocessing():
                self.test_data = self.data_processor.transform(self.test_data, self.current_pipeline)

        with fedot_composer_timer.launch_predicting():
            self.prediction = self.data_processor.define_predictions(current_pipeline=self.current_pipeline,
                                                                     test_data=self.test_data,
                                                                     in_sample=self._is_in_sample_prediction,
                                                                     validation_blocks=validation_blocks)

        if save_predictions:
            self.save_predict(self.prediction)

        return self.prediction.predict

    def predict_proba(self,
                      features: FeaturesType,
                      save_predictions: bool = False,
                      probs_for_all_classes: bool = False) -> np.ndarray:
        """Predicts the probability of new target using already fitted classification model

        Args:
            features: an array with features of test data.
            save_predictions: if ``True`` - save predictions as ``.csv`` file in working directory.
            probs_for_all_classes: if ``True`` - return probability for each class even for binary classification.

        Returns:
            An array with prediction values.
        """

        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        with fedot_composer_timer.launch_predicting():
            if self.params.task.task_type == TaskTypesEnum.classification:
                self.test_data = self.data_processor.define_data(target=self.target,
                                                                 features=features, is_predict=True)

                mode = 'full_probs' if probs_for_all_classes else 'probs'

                self.prediction = self.current_pipeline.predict(self.test_data, output_mode=mode)

                if save_predictions:
                    self.save_predict(self.prediction)
            else:
                raise ValueError('Probabilities of predictions are available only for classification')

        return self.prediction.predict

    def forecast(self,
                 pre_history: Optional[Union[str, Tuple[np.ndarray, np.ndarray], InputData, dict]] = None,
                 horizon: Optional[int] = None,
                 save_predictions: bool = False) -> np.ndarray:
        """Forecasts the new values of time series. If horizon is bigger than forecast length of fitted model -
        out-of-sample forecast is applied (not supported for multi-modal data).

        Args:
            pre_history: an array with features for pre-history of the forecast.
            horizon: amount of steps to forecast.
            save_predictions: if ``True`` save predictions as csv-file in working directory.

        Returns:
            An array with prediction values.
        """
        self._check_forecast_applicable()

        forecast_length = self.train_data.task.task_params.forecast_length
        horizon = horizon or forecast_length
        if pre_history is None:
            pre_history = self.train_data
            pre_history.target = None
        self.test_data = self.data_processor.define_data(target=self.target,
                                                         features=pre_history,
                                                         is_predict=True)
        predict = out_of_sample_ts_forecast(self.current_pipeline, self.test_data, horizon)
        self.prediction = convert_forecast_to_output(self.test_data, predict)
        self._is_in_sample_prediction = False
        if save_predictions:
            self.save_predict(self.prediction)
        return self.prediction.predict

    def _check_forecast_applicable(self):
        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        if self.params.task.task_type != TaskTypesEnum.ts_forecasting:
            raise ValueError('Forecasting can be used only for the time series')

    def load(self, path):
        """Loads saved graph from disk

        Args:
            path: path to ``json`` file with model.
        """
        self.current_pipeline = Pipeline(use_input_preprocessing=self.params.get('use_input_preprocessing'))
        self.current_pipeline.load(path)
        self.data_processor.preprocessor = self.current_pipeline.preprocessor

    def plot_pareto(self):
        metric_names = [str(metric) for metric in self.metrics]
        # archive_history stores archives of the best models.
        # Each archive is sorted from the best to the worst model,
        # so the best_candidates is sorted too.
        best_candidates = self.history.archive_history[-1]
        visualise_pareto(front=best_candidates,
                         objectives_names=metric_names,
                         show=True)

    def plot_prediction(self, in_sample: Optional[bool] = None, target: Optional[Any] = None):
        """Plots prediction obtained from a graph.

        Args:
            in_sample: if current prediction is in_sample (for time-series forecasting), plots predictions as future
                values.
            target: user-specified name of target variable for :class:`MultiModalData`.
        """
        task = self.params.task

        if self.prediction is not None:
            if task.task_type == TaskTypesEnum.ts_forecasting:
                in_sample = in_sample or self._is_in_sample_prediction
                plot_forecast(self.test_data, self.prediction, in_sample, target)
            elif task.task_type == TaskTypesEnum.regression:
                plot_biplot(self.prediction)
            elif task.task_type == TaskTypesEnum.classification:
                self.predict_proba(self.test_data)
                plot_roc_auc(self.test_data, self.prediction)
            else:
                self.log.error('Not supported yet')
                raise NotImplementedError(f"For task {task} plot prediction is not supported")
        else:
            self.log.error('No prediction to visualize')
            raise ValueError('Prediction from model is empty')

    def get_metrics(self,
                    target: Union[np.ndarray, pd.Series] = None,
                    metric_names: Union[str, List[str]] = None,
                    in_sample: Optional[bool] = None,
                    validation_blocks: Optional[int] = None,
                    rounding_order: int = 3) -> dict:
        """Gets quality metrics for a fitted graph

        Args:
            target: an array with target values of test data. If ``None``, target specified for fit is used.
            metric_names: names of required metrics.
            in_sample: used for time series forecasting.
                If True prediction will be obtained as ``.predict(..., in_sample=True)``.
            validation_blocks: number of validation blocks for time series in-sample forecast.
            rounding_order: number of decimal places for metrics

        Returns:
            Values of quality metrics.
        """
        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        if target is not None:
            if self.test_data is None:
                self.test_data = InputData(idx=np.arange(len(self.prediction.predict)),
                                           features=None,
                                           target=target[:len(self.prediction.predict)],
                                           task=self.train_data.task,
                                           data_type=self.train_data.data_type)
            else:
                self.test_data.target = target[:len(self.prediction.predict)]

        metrics = ensure_wrapped_in_sequence(metric_names) if metric_names else self.metrics
        metric_names = [str(metric) for metric in metrics]

        in_sample = in_sample if in_sample is not None else self._is_in_sample_prediction

        if not in_sample:
            validation_blocks = None

        objective = MetricsObjective(metrics)
        obj_eval = PipelineObjectiveEvaluate(objective=objective,
                                             data_producer=lambda: (yield self.train_data, self.test_data),
                                             validation_blocks=validation_blocks,
                                             eval_n_jobs=self.params.n_jobs,
                                             do_unfit=False)

        metrics = obj_eval.evaluate(self.current_pipeline).values
        metrics = {metric_name: round(abs(metric), rounding_order) for (metric_name, metric) in
                   zip(metric_names, metrics)}

        return metrics

    def save_predict(self, predicted_data: OutputData):
        # TODO unify with OutputData.save_to_csv()
        """ Saves pipeline forecasts in csv file """
        if len(predicted_data.predict.shape) >= 2:
            prediction = predicted_data.predict.tolist()
        else:
            prediction = predicted_data.predict
        pd.DataFrame({'Index': predicted_data.idx,
                      'Prediction': prediction}).to_csv('./predictions.csv', index=False)
        self.log.message('Predictions was saved in current directory.')

    def export_as_project(self, project_path='fedot_project.zip'):
        export_project_to_zip(zip_name=project_path, opt_history=self.history,
                              pipeline=self.current_pipeline,
                              train_data=self.train_data, test_data=self.test_data)

    def import_as_project(self, project_path='fedot_project.zip'):
        self.current_pipeline, self.train_data, self.test_data, self.history = \
            import_project_from_zip(zip_path=project_path)
        # TODO workaround to init internal fields of API and data
        self.train_data = self.data_processor.define_data(features=self.train_data, is_predict=False)
        self.test_data = self.data_processor.define_data(features=self.test_data, is_predict=True)
        self.predict(self.test_data)

    def explain(self, features: FeaturesType = None,
                method: str = 'surrogate_dt', visualization: bool = True, **kwargs) -> Explainer:
        """Creates explanation for :attr:`~Fedot.current_pipeline` according to the selected ``method``.

        An :class:`Explainer` instance will return.

        Args:
            features: samples to be explained. If ``None``, ``train_data`` from last fit will be used.
            method: explanation method, defaults to ``surrogate_dt``
            visualization: print and plot the explanation simultaneously, defaults to ``True``.
        Notes:
            An explanation can be retrieved later by executing :meth:`Explainer.visualize`.
        """
        pipeline = self.current_pipeline
        if features is None:
            data = self.train_data
        else:
            data = self.data_processor.define_data(features=features,
                                                   is_predict=False)
        explainer = explain_pipeline(pipeline=pipeline, data=data, method=method,
                                     visualization=visualization, **kwargs)

        return explainer

    def return_report(self) -> pd.DataFrame:
        """ Function returns a report on time consumption.

            The following steps are presented in this report:
            - 'Data Definition (fit)': Time spent on data definition in fit().
            - 'Data Preprocessing': Total time spent on preprocessing data, includes fitting and predicting stages.
            - 'Fitting (summary)': Total time spent on Composing, Tuning and Training Inference.
            - 'Composing': Time spent on searching for the best pipeline.
            - 'Train Inference': Time spent on training the pipeline found during composing.
            - 'Tuning (composing)': Time spent on hyperparameters tuning in the whole fitting, if with_tune is True.
            - 'Tuning (after)': Time spent on .tune() (hyperparameters tuning) after composing.
            - 'Data Definition (predict)': Time spent on data definition in predict().
            - 'Predicting': Time spent on predicting (inference).
        """
        report = fedot_composer_timer.report

        if self.current_pipeline is None:
            raise ValueError(NOT_FITTED_ERR_MSG)

        report = pd.DataFrame(data=report.values(), index=report.keys())
        return report.iloc[:, 0].dt.components.iloc[:, :-2]

    @staticmethod
    def _init_logger(logging_level: int):
        # reset logging level for Singleton
        Log().reset_logging_level(logging_level)
        return default_log(prefix='FEDOT logger')

    def _init_remote_if_necessary(self):
        remote = RemoteEvaluator()
        if remote.is_enabled and remote.remote_task_params is not None:
            task = self.params.task
            if task.task_type is TaskTypesEnum.ts_forecasting:
                task_str = (f'Task(TaskTypesEnum.ts_forecasting, '
                            f'TsForecastingParams(forecast_length={task.task_params.forecast_length}))')
            else:
                task_str = f'Task({str(task.task_type)})'
            remote.remote_task_params.task_type = task_str
            remote.remote_task_params.is_multi_modal = isinstance(self.train_data, MultiModalData)

            if isinstance(self.target, str) and remote.remote_task_params.target is None:
                remote.remote_task_params.target = self.target

    def _train_pipeline_on_full_dataset(self, recommendations: Optional[dict],
                                        full_train_not_preprocessed: Union[InputData, MultiModalData]):
        """Applies training procedure for obtained pipeline if dataset was clipped
        """

        if recommendations is not None:
            # if data was cut we need to refit pipeline on full data
            self.data_processor.accept_and_apply_recommendations(full_train_not_preprocessed,
                                                                 {k: v for k, v in recommendations.items()
                                                                  if k != 'cut'})
        self.current_pipeline.fit(
            full_train_not_preprocessed,
            n_jobs=self.params.n_jobs
        )
