


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A							:					Tuple     						=     logging.get_logger(__name__)






class     UpperCamelCase(  __lowercase   ):
					snake_case_		:				List[str]										=	["""input_features""", """is_longer"""]


					def __init__(						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Tuple=6_4     ,    SCREAMING_SNAKE_CASE	:		Any=4_8_0_0_0     ,    SCREAMING_SNAKE_CASE	:		List[str]=4_8_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_0_2_4     ,    SCREAMING_SNAKE_CASE	:		str=0.0     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=False     ,    SCREAMING_SNAKE_CASE	:		float = 0     ,    SCREAMING_SNAKE_CASE	:		float = 1_4_0_0_0     ,    SCREAMING_SNAKE_CASE	:		int = None     ,    SCREAMING_SNAKE_CASE	:		str = "fusion"     ,    SCREAMING_SNAKE_CASE	:		str = "repeatpad"     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    )			->	str:




											'''simple docstring'''

											super().__init__(
											    feature_size=_A     ,    sampling_rate=_A     ,    padding_value=_A     ,    return_attention_mask=_A     ,    **_A     ,    )
											__snake_case				=		top_db
											__snake_case				=		truncation
											__snake_case				=		padding
											__snake_case				=		fft_window_size
											__snake_case				=		(fft_window_size >> 1) + 1
											__snake_case				=		hop_length
											__snake_case				=		max_length_s
											__snake_case				=		max_length_s * sampling_rate
											__snake_case				=		sampling_rate
											__snake_case				=		frequency_min
											__snake_case				=		frequency_max
											__snake_case				=		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins     ,    num_mel_filters=_A     ,    min_frequency=_A     ,    max_frequency=_A     ,    sampling_rate=_A     ,    norm=_A     ,    mel_scale="htk"     ,    )
											__snake_case				=		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins     ,    num_mel_filters=_A     ,    min_frequency=_A     ,    max_frequency=_A     ,    sampling_rate=_A     ,    norm="slaney"     ,    mel_scale="slaney"     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	int:




											'''simple docstring'''

											__snake_case				=		copy.deepcopy(self.__dict__						)
											__snake_case				=		self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
											return output


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		np.array     ,    SCREAMING_SNAKE_CASE	:		Optional[np.array] = None						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		spectrogram(
											    _A     ,    window_function(self.fft_window_size     ,    "hann"						)     ,    frame_length=self.fft_window_size     ,    hop_length=self.hop_length     ,    power=2.0     ,    mel_filters=_A     ,    log_mel="dB"     ,    )
											return log_mel_spectrogram.T


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		np.array_split(list(range(0     ,    total_frames - chunk_frames + 1						)						)     ,    3						)
											if len(ranges[1]						) == 0:
																	# if the audio is too short, we just use the first chunk
																	__snake_case				=		[0]
											if len(ranges[2]						) == 0:
																	# if the audio is too short, we just use the first chunk
																	__snake_case				=		[0]
											# randomly choose index for each part
											__snake_case				=		np.random.choice(ranges[0]						)
											__snake_case				=		np.random.choice(ranges[1]						)
											__snake_case				=		np.random.choice(ranges[2]						)

											__snake_case				=		mel[idx_front : idx_front + chunk_frames, :]
											__snake_case				=		mel[idx_middle : idx_middle + chunk_frames, :]
											__snake_case				=		mel[idx_back : idx_back + chunk_frames, :]

											__snake_case				=		torch.tensor(mel[None, None, :]						)
											__snake_case				=		torch.nn.functional.interpolate(
											    _A     ,    size=[chunk_frames, 6_4]     ,    mode="bilinear"     ,    align_corners=_A						)
											__snake_case				=		mel_shrink[0][0].numpy()
											__snake_case				=		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,    axis=0						)
											return mel_fusion


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		np.array     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Tuple:




											'''simple docstring'''

											if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																							__snake_case				=		True
																							# random crop to max_length (for compatibility) -> this should be handled by self.pad
																							__snake_case				=		len(_A						) - max_length
																							__snake_case				=		np.random.randint(0     ,    overflow + 1						)
																							__snake_case				=		waveform[idx : idx + max_length]
																							__snake_case				=		self._np_extract_fbank_features(_A     ,    self.mel_filters_slaney						)[None, :]
																	elif truncation == "fusion":
																							__snake_case				=		self._np_extract_fbank_features(_A     ,    self.mel_filters						)
																							__snake_case				=		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																							__snake_case				=		mel.shape[0]
																							if chunk_frames == total_frames:
																													# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																													# In this case, we just use the whole audio.
																													__snake_case				=		np.stack([mel, mel, mel, mel]     ,    axis=0						)
																													__snake_case				=		False
																							else:
																													__snake_case				=		self._random_mel_fusion(_A     ,    _A     ,    _A						)
																													__snake_case				=		True
																	else:
																							raise NotImplementedError(f'''data_truncating {truncation} not implemented'''						)

											else:
																	__snake_case				=		False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																							if padding == "repeat":
																													__snake_case				=		int(max_length / len(_A						)						)
																													__snake_case				=		np.stack(np.tile(_A     ,    n_repeat + 1						)						)[:max_length]
																							if padding == "repeatpad":
																													__snake_case				=		int(max_length / len(_A						)						)
																													__snake_case				=		np.stack(np.tile(_A     ,    _A						)						)
																							__snake_case				=		np.pad(_A     ,    (0, max_length - waveform.shape[0])     ,    mode="constant"     ,    constant_values=0						)

																	if truncation == "fusion":
																							__snake_case				=		self._np_extract_fbank_features(_A     ,    self.mel_filters						)
																							__snake_case				=		np.stack([input_mel, input_mel, input_mel, input_mel]     ,    axis=0						)
																	else:
																							__snake_case				=		self._np_extract_fbank_features(_A     ,    self.mel_filters_slaney						)[None, :]

											return input_mel, longer






					def __call__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,    SCREAMING_SNAKE_CASE	:		str = None     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	List[str]:




											'''simple docstring'''

											__snake_case				=		truncation if truncation is not None else self.truncation
											__snake_case				=		padding if padding else self.padding

											if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																							raise ValueError(
																							    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																							    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																							    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''						)
											else:
																	logger.warning(
																	    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																	    "Failing to do so can result in silent errors that might be hard to debug."						)

											__snake_case				=		isinstance(_A     ,    np.ndarray						) and len(raw_speech.shape						) > 1
											if is_batched_numpy and len(raw_speech.shape						) > 2:
																	raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''						)
											__snake_case				=		is_batched_numpy or (
											    isinstance(_A     ,    (list, tuple)						) and (isinstance(raw_speech[0]     ,    (np.ndarray, tuple, list)						))
											)

											if is_batched:
																	__snake_case				=		[np.asarray(_A     ,    dtype=np.floataa						) for speech in raw_speech]
											elif not is_batched and not isinstance(_A     ,    np.ndarray						):
																	__snake_case				=		np.asarray(_A     ,    dtype=np.floataa						)
											elif isinstance(_A     ,    np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
																	__snake_case				=		raw_speech.astype(np.floataa						)

											# always return batch
											if not is_batched:
																	__snake_case				=		[np.asarray(_A						)]

											# convert to mel spectrogram, truncate and pad if needed.
											__snake_case				=		[
											    self._get_input_mel(_A     ,    max_length if max_length else self.nb_max_samples     ,    _A     ,    _A						)
											    for waveform in raw_speech
											]

											__snake_case				=		[]
											__snake_case				=		[]
											for mel, longer in padded_inputs:
																	input_mel.append(_A						)
																	is_longer.append(_A						)

											if truncation == "fusion" and sum(_A						) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	__snake_case				=		np.random.randint(0     ,    len(_A						)						)
																	__snake_case				=		True

											if isinstance(input_mel[0]     ,    _A						):
																	__snake_case				=		[np.asarray(_A     ,    dtype=np.floataa						) for feature in input_mel]

											# is_longer is a list of bool
											__snake_case				=		[[longer] for longer in is_longer]

											__snake_case				=		{"input_features": input_mel, "is_longer": is_longer}
											__snake_case				=		BatchFeature(_A						)

											if return_tensors is not None:
																	__snake_case				=		input_features.convert_to_tensors(_A						)

											return input_features









from sklearn.metrics import mean_squared_error

import datasets


A							:					List[Any]     						=     '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

A							:					Optional[int]     						=     '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


A							:					str     						=     '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											return datasets.MetricInfo(
											    description=_DESCRIPTION     ,    citation=_CITATION     ,    inputs_description=_KWARGS_DESCRIPTION     ,    features=datasets.Features(self._get_feature_types()						)     ,    reference_urls=[
											        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
											    ]     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value("float"						)						),
																	    "references": datasets.Sequence(datasets.Value("float"						)						),
																	}
											else:
																	return {
																	    "predictions": datasets.Value("float"						),
																	    "references": datasets.Value("float"						),
																	}






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		int="uniform_average"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		mean_squared_error(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    sample_weight=SCREAMING_SNAKE_CASE     ,    multioutput=SCREAMING_SNAKE_CASE     ,    squared=SCREAMING_SNAKE_CASE						)

											return {"mse": mse}





