# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""

import concurrent.futures
import math
import statistics
from typing import List, Optional, Union

import datasets
import evaluate
import numpy as np

# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""


# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of generated time series.
        shape: (num_generation, num_timesteps, num_features)
    references: list of reference
        shape: (num_reference, num_timesteps, num_features)
Returns:
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("bowdbeg/matching_series")
    >>> results = my_new_module.compute(references=[[[0.0, 1.0]]], predictions=[[[0.0, 1.0]]])
    >>> print(results)
    {'matchin': 1.0}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class matching_series(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
                    "references": datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
                }
            ),
            # Homepage of the module for documentation
            homepage="https://huggingface.co/spaces/bowdbeg/matching_series",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"],
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        pass

    def compute(self, *, predictions=None, references=None, **kwargs) -> Optional[dict]:
        """Compute the evaluation module.

        Usage of positional arguments is not allowed to prevent mistakes.

        Args:
            predictions (`list/array/tensor`, *optional*):
                Predictions.
            references (`list/array/tensor`, *optional*):
                References.
            **kwargs (optional):
                Keyword arguments that will be forwarded to the evaluation module [`~evaluate.EvaluationModule.compute`]
                method (see details in the docstring).

        Return:
            `dict` or `None`

            - Dictionary with the results if this evaluation module is run on the main process (`process_id == 0`).
            - `None` if the evaluation module is not run on the main process (`process_id != 0`).

        ```py
        >>> import evaluate
        >>> accuracy =  evaluate.load("accuracy")
        >>> accuracy.compute(predictions=[0, 1, 1, 0], references=[0, 1, 0, 1])
        ```
        """
        all_kwargs = {"predictions": predictions, "references": references, **kwargs}
        if predictions is None and references is None:
            missing_kwargs = {k: None for k in self._feature_names() if k not in all_kwargs}
            all_kwargs.update(missing_kwargs)
        else:
            missing_inputs = [k for k in self._feature_names() if k not in all_kwargs]
            if missing_inputs:
                raise ValueError(
                    f"Evaluation module inputs are missing: {missing_inputs}. All required inputs are {list(self._feature_names())}"
                )
        inputs = {input_name: all_kwargs[input_name] for input_name in self._feature_names()}
        compute_kwargs = {k: kwargs[k] for k in kwargs if k not in self._feature_names()}
        return self._compute(**inputs, **compute_kwargs)

    def _compute(
        self,
        predictions: Union[List, np.ndarray],
        references: Union[List, np.ndarray],
        batch_size: Optional[int] = None,
        cuc_n_calculation: int = 3,
        cuc_n_samples: Union[List[int], str] = "auto",
        metric: str = "mse",
        num_process: int = 1,
        return_distance: bool = False,
        return_matching: bool = False,
        return_each_features: bool = False,
        return_coverages: bool = False,
        return_all: bool = False,
        dtype=np.float32,
        instance_normalization: bool = False,
        eps: float = 1e-10,
    ):
        """
        Compute the scores of the module given the predictions and references
        Args:
            predictions: list of generated time series.
                shape: (num_generation, num_timesteps, num_features)
            references: list of reference
                shape: (num_reference, num_timesteps, num_features)
            batch_size: batch size to use for the computation. If None, the whole dataset is processed at once.
            cuc_n_calculation: number of Coverage Under Curve calculate times
            cuc_n_samples: number of samples to use for Coverage Under Curve calculation. If "auto", it uses the number of samples of the predictions.
        Returns:
        """
        if return_all:
            return_distance = True
            return_matching = True
            return_each_features = True
            return_coverages = True
        predictions = np.array(predictions).astype(dtype)
        references = np.array(references).astype(dtype)

        if instance_normalization:
            predictions = (predictions - predictions.mean(axis=1, keepdims=True)) / predictions.std(
                axis=1, keepdims=True
            )
            references = (references - references.mean(axis=1, keepdims=True)) / references.std(axis=1, keepdims=True)

        assert isinstance(predictions, np.ndarray) and isinstance(references, np.ndarray)
        if predictions.shape[1:] != references.shape[1:]:
            raise ValueError(
                "The number of features in the predictions and references should be the same. predictions: {}, references: {}".format(
                    predictions.shape[1:], references.shape[1:]
                )
            )

        # at first, convert the inputs to numpy arrays
        distance = self.compute_distance(
            predictions=predictions,
            references=references,
            metric=metric,
            batch_size=batch_size,
            num_process=num_process,
            dtype=dtype,
        )

        metrics = self._compute_metrics(
            distance=distance.mean(axis=-1),
            eps=eps,
            cuc_n_calculation=cuc_n_calculation,
            cuc_n_samples=cuc_n_samples,
        )
        metrics_feature = [
            self._compute_metrics(distance[:, :, f], eps, cuc_n_calculation, cuc_n_samples)
            for f in range(predictions.shape[-1])
        ]
        macro_metrics = {
            "macro_" + k: statistics.mean([m[k] for m in metrics_feature])  # type: ignore
            for k in metrics_feature[0].keys()
            if isinstance(metrics_feature[0][k], (int, float))
        }

        out = {}
        out.update({k: v for k, v in metrics.items() if isinstance(v, (int, float))})
        out.update(macro_metrics)

        if return_distance:
            out["distance"] = distance
        if return_matching:
            out.update({k: v for k, v in metrics.items() if "match" in k})
        if return_coverages:
            out["coverages"] = metrics["coverages"]
        if return_each_features:
            out.update(
                {
                    k + "_features": [m[k] for m in metrics_feature]
                    for k in metrics_feature[0].keys()
                    if isinstance(metrics_feature[0][k], (int, float))
                }
            )
            if return_coverages:
                out.update(
                    {
                        "coverages_features": [m["coverages"] for m in metrics_feature],
                    }
                )
        return out

    def compute_cuc(
        self,
        match: np.ndarray,
        n_reference: int,
        n_calculation: int,
        n_samples: Union[List[int], str],
    ):
        """
        Compute Coverage Under Curve
        Args:
            match: best match for each generated time series
            n_reference: number of reference time series
            n_calculation: number of Coverage Under Curve calculate times
            n_samples: number of samples to use for Coverage Under Curve calculation. If "auto", it uses the number of samples of the predictions.
        Returns:
        """
        n_generaiton = len(match)
        if n_samples == "auto":
            exp = int(math.log2(n_generaiton))
            n_samples = [int(2**i) for i in range(exp)]
            n_samples.append(n_generaiton)
        assert isinstance(n_samples, list) and all(isinstance(n, int) for n in n_samples)

        coverages = []
        for n_sample in n_samples:
            coverage = 0
            for _ in range(n_calculation):
                sample = np.random.choice(match, size=n_sample, replace=False)  # type: ignore
                coverage += len(np.unique(sample)) / n_reference
            coverages.append(coverage / n_calculation)
        cuc = (np.trapz(coverages, n_samples) / len(n_samples) / max(n_samples)).item()
        return coverages, cuc

    @staticmethod
    def _compute_distance(x, y, metric: str = "mse", axis: int = -1):
        if metric.lower() == "mse":
            return np.mean((x - y) ** 2, axis=axis)
        elif metric.lower() == "mae":
            return np.mean(np.abs(x - y), axis=axis)
        elif metric.lower() == "rmse":
            return np.sqrt(np.mean((x - y) ** 2, axis=axis))
        else:
            raise ValueError("Unknown metric: {}".format(metric))

    def compute_distance(
        self,
        predictions: np.ndarray,
        references: np.ndarray,
        metric: str,
        batch_size: Optional[int] = None,
        num_process: int = 1,
        dtype=np.float32,
    ):
        # distance between predictions and references for all example combinations for each features
        # shape: (num_generation, num_reference, num_features)
        if batch_size is not None:
            if num_process > 1:
                distance = np.zeros((len(predictions), len(references), predictions.shape[-1]), dtype=dtype)
                idxs = [
                    (i, j)
                    for i in range(0, len(predictions) + batch_size, batch_size)
                    for j in range(0, len(references) + batch_size, batch_size)
                ]
                args = [
                    (predictions[i : i + batch_size, None], references[None, j : j + batch_size], metric, -2)
                    for i, j in idxs
                ]
                with concurrent.futures.ProcessPoolExecutor(max_workers=num_process) as executor:
                    results = executor.map(
                        self._compute_distance,
                        *zip(*args),
                    )
                    for (i, j), d in zip(idxs, results):
                        distance[i : i + batch_size, j : j + batch_size] = d

            else:
                distance = np.zeros((len(predictions), len(references), predictions.shape[-1]), dtype=dtype)
                # iterate over the predictions and references in batches
                for i in range(0, len(predictions) + batch_size, batch_size):
                    for j in range(0, len(references) + batch_size, batch_size):
                        d = self._compute_distance(
                            predictions[i : i + batch_size, None],
                            references[None, j : j + batch_size],
                            metric=metric,
                            axis=-2,
                        )
                        distance[i : i + batch_size, j : j + batch_size] = d
        else:
            distance = self._compute_distance(predictions[:, None], references[None, :], metric=metric, axis=-2)
        return distance

    def _compute_metrics(
        self,
        distance: np.ndarray,
        eps: float = 1e-10,
        cuc_n_calculation: int = 3,
        cuc_n_samples: Union[List[int], str] = "auto",
    ) -> dict[str, float | list[float]]:
        """
        Compute metrics from the distance matrix
        Args:
            distance: distance matrix. shape: (num_generation, num_reference)
        Returns:
        """
        index_distance = distance.diagonal(axis1=0, axis2=1).mean().item()

        # matching scores
        # best match for each generated time series
        # shape: (num_generation,)
        best_match = np.argmin(distance, axis=-1)
        precision_distance = distance[np.arange(len(best_match)), best_match].mean().item()

        # best match for each reference time series
        # shape: (num_reference,)
        best_match_inv = np.argmin(distance, axis=0)
        recall_distance = distance[best_match_inv, np.arange(len(best_match_inv))].mean().item()

        f1_distance = 2 / (1 / (precision_distance + eps) + 1 / (recall_distance + eps))
        mean_distance = (precision_distance + recall_distance) / 2

        # matching precision, recall and f1
        matching_recall = np.unique(best_match).size / len(best_match_inv)
        matching_precision = np.unique(best_match_inv).size / len(best_match)
        matching_f1 = 2 / (1 / (matching_precision + eps) + 1 / (matching_recall + eps))

        # cuc
        coverages, cuc = self.compute_cuc(best_match, len(best_match_inv), cuc_n_calculation, cuc_n_samples)
        return {
            "precision_distance": precision_distance,
            "f1_distance": f1_distance,
            "recall_distance": recall_distance,
            "mean_distance": mean_distance,
            "index_distance": index_distance,
            "matching_precision": matching_precision,
            "matching_recall": matching_recall,
            "matching_f1": matching_f1,
            "cuc": cuc,
            "coverages": coverages,
            "match": best_match,
            "match_inv": best_match_inv,
        }