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#
# 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.
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# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
							from datasets import load_dataset







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	str	 ='''microsoft/speecht5_tts'''
							lowercase_	:	Dict	 =(
							    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
							    '''text to read (in English) and returns a waveform object containing the sound.'''
							)
							lowercase_	:	Dict	 ='''text_reader'''
							lowercase_	:	int	 =SpeechTaProcessor
							lowercase_	:	Tuple	 =SpeechTaForTextToSpeech
							lowercase_	:	int	 =SpeechTaHifiGan

							lowercase_	:	Optional[int]	 =['''text''']
							lowercase_	:	Optional[Any]	 =['''audio''']




							def 			A__				(    self):
												if self.post_processor is None:
																	lowercase						     =						'''microsoft/speecht5_hifigan'''
												super().setup()




							def 			A__				(    self       ,A__       ,A__=None):
												lowercase						     =						self.pre_processor(text=A__       ,return_tensors='''pt'''       ,truncation=A__)

												if speaker_embeddings is None:
																	if not is_datasets_available():
																						raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.''')

																	lowercase						     =						load_dataset('''Matthijs/cmu-arctic-xvectors'''       ,split='''validation''')
																	lowercase						     =						torch.tensor(embeddings_dataset[7_3_0_5]['''xvector''']).unsqueeze(0)

												return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




							def 			A__				(    self       ,A__):
												with torch.no_grad():
																	return self.model.generate_speech(**A__)





							def 			A__				(    self       ,A__):
												with torch.no_grad():
																	return self.post_processor(A__).cpu().detach()






import numpy as np

import datasets


lowercase__		:Dict			  =   "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowercase__		:List[Any]			  =   "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowercase__		:Dict			  =   "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION		)
class     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
												        {
												            '''X''': datasets.Sequence(datasets.Value('''float'''       ,id='''sequence''')       ,id='''X'''),
												        })       ,)





							def 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

												# Assert that arrays are 2D
												if len(X.shape) != 2:
																	raise ValueError('''Expected `X` to be a 2D vector''')
												if len(reference_distribution.shape) != 2:
																	raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
												if reference_distribution.shape[0] < 2:
																	raise ValueError(
																	    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')

												# Get mahalanobis distance for each prediction
												lowercase						     =						X - np.mean(A__)
												lowercase						     =						np.cov(reference_distribution.T)
												try:
																	lowercase						     =						np.linalg.inv(A__)
												except np.linalg.LinAlgError:
																	lowercase						     =						np.linalg.pinv(A__)
												lowercase						     =						np.dot(A__       ,A__)
												lowercase						     =						np.dot(A__       ,X_minus_mu.T).diagonal()

												return {"mahalanobis": mahal_dist}




