
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


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				lowerCamelCase__    ):
				_snake_case		    = ['''input_features''', '''is_longer''']




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[int]=64,    SCREAMING_SNAKE_CASE_	:				List[str]=4_80_00,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=4_80,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=10,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=10_24,    SCREAMING_SNAKE_CASE_	:				str=0.0,    SCREAMING_SNAKE_CASE_	:				str=False,    SCREAMING_SNAKE_CASE_	:				List[str] = 0,    SCREAMING_SNAKE_CASE_	:				Tuple = 1_40_00,    SCREAMING_SNAKE_CASE_	:				List[Any] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = "fusion",    SCREAMING_SNAKE_CASE_	:				List[Any] = "repeatpad",    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    feature_size=__lowerCamelCase,    sampling_rate=__lowerCamelCase,    padding_value=__lowerCamelCase,    return_attention_mask=__lowerCamelCase,    **__lowerCamelCase,    )
										snake_case       :     List[Any] =    top_db
										snake_case       :     Tuple =    truncation
										snake_case       :     str =    padding
										snake_case       :     Dict =    fft_window_size
										snake_case       :     str =    (fft_window_size >> 1) + 1
										snake_case       :     Tuple =    hop_length
										snake_case       :     Optional[Any] =    max_length_s
										snake_case       :     List[Any] =    max_length_s * sampling_rate
										snake_case       :     List[Any] =    sampling_rate
										snake_case       :     Dict =    frequency_min
										snake_case       :     List[str] =    frequency_max
										snake_case       :     List[str] =    mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins,    num_mel_filters=__lowerCamelCase,    min_frequency=__lowerCamelCase,    max_frequency=__lowerCamelCase,    sampling_rate=__lowerCamelCase,    norm=__lowerCamelCase,    mel_scale='''htk''',    )
										snake_case       :     Any =    mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins,    num_mel_filters=__lowerCamelCase,    min_frequency=__lowerCamelCase,    max_frequency=__lowerCamelCase,    sampling_rate=__lowerCamelCase,    norm='''slaney''',    mel_scale='''slaney''',    )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     List[str] =    copy.deepcopy(self.__dict__      )
										snake_case       :     Union[str, Any] =    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 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any] = None      ):
										snake_case       :     Optional[Any] =    spectrogram(
										    __lowerCamelCase,    window_function(self.fft_window_size,    '''hann'''      ),    frame_length=self.fft_window_size,    hop_length=self.hop_length,    power=2.0,    mel_filters=__lowerCamelCase,    log_mel='''dB''',    )
										return log_mel_spectrogram.T




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     Optional[Any] =    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       :     Dict =    [0]
										if len(ranges[2]      ) == 0:
																# if the audio is too short, we just use the first chunk
																snake_case       :     Optional[Any] =    [0]
										# randomly choose index for each part
										snake_case       :     int =    np.random.choice(ranges[0]      )
										snake_case       :     Optional[Any] =    np.random.choice(ranges[1]      )
										snake_case       :     Tuple =    np.random.choice(ranges[2]      )

										snake_case       :     Union[str, Any] =    mel[idx_front : idx_front + chunk_frames, :]
										snake_case       :     Dict =    mel[idx_middle : idx_middle + chunk_frames, :]
										snake_case       :     Any =    mel[idx_back : idx_back + chunk_frames, :]

										snake_case       :     Union[str, Any] =    torch.tensor(mel[None, None, :]      )
										snake_case       :     Optional[int] =    torch.nn.functional.interpolate(
										    __lowerCamelCase,    size=[chunk_frames, 64],    mode='''bilinear''',    align_corners=__lowerCamelCase      )
										snake_case       :     str =    mel_shrink[0][0].numpy()
										snake_case       :     List[str] =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],    axis=0      )
										return mel_fusion




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int      ):






										if waveform.shape[0] > max_length:
																if truncation == "rand_trunc":
																						snake_case       :     Union[str, Any] =    True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						snake_case       :     Union[str, Any] =    len(__lowerCamelCase      ) - max_length
																						snake_case       :     List[str] =    np.random.randint(0,    overflow + 1      )
																						snake_case       :     Any =    waveform[idx : idx + max_length]
																						snake_case       :     List[str] =    self._np_extract_fbank_features(__lowerCamelCase,    self.mel_filters_slaney      )[None, :]
																elif truncation == "fusion":
																						snake_case       :     Union[str, Any] =    self._np_extract_fbank_features(__lowerCamelCase,    self.mel_filters      )
																						snake_case       :     Dict =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						snake_case       :     Dict =    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       :     str =    np.stack([mel, mel, mel, mel],    axis=0      )
																												snake_case       :     int =    False
																						else:
																												snake_case       :     List[str] =    self._random_mel_fusion(__lowerCamelCase,    __lowerCamelCase,    __lowerCamelCase      )
																												snake_case       :     Optional[Any] =    True
																else:
																						raise NotImplementedError(F"""data_truncating {truncation} not implemented"""      )

										else:
																snake_case       :     str =    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       :     str =    int(max_length / len(__lowerCamelCase      )      )
																												snake_case       :     int =    np.stack(np.tile(__lowerCamelCase,    n_repeat + 1      )      )[:max_length]
																						if padding == "repeatpad":
																												snake_case       :     Any =    int(max_length / len(__lowerCamelCase      )      )
																												snake_case       :     Any =    np.stack(np.tile(__lowerCamelCase,    __lowerCamelCase      )      )
																						snake_case       :     List[Any] =    np.pad(__lowerCamelCase,    (0, max_length - waveform.shape[0]),    mode='''constant''',    constant_values=0      )

																if truncation == "fusion":
																						snake_case       :     Tuple =    self._np_extract_fbank_features(__lowerCamelCase,    self.mel_filters      )
																						snake_case       :     List[Any] =    np.stack([input_mel, input_mel, input_mel, input_mel],    axis=0      )
																else:
																						snake_case       :     Any =    self._np_extract_fbank_features(__lowerCamelCase,    self.mel_filters_slaney      )[None, :]

										return input_mel, longer




				def __call__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[str] = None,    SCREAMING_SNAKE_CASE_	:				List[Any] = None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any] = None,    SCREAMING_SNAKE_CASE_	:				int = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    **SCREAMING_SNAKE_CASE_	:				Any,    ):
										snake_case       :     int =    truncation if truncation is not None else self.truncation
										snake_case       :     Union[str, Any] =    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       :     int =    isinstance(__lowerCamelCase,    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       :     Optional[int] =    is_batched_numpy or (
										    isinstance(__lowerCamelCase,    (list, tuple)      ) and (isinstance(raw_speech[0],    (np.ndarray, tuple, list)      ))
										)

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

										# always return batch
										if not is_batched:
																snake_case       :     Tuple =    [np.asarray(__lowerCamelCase      )]

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

										snake_case       :     Optional[int] =    []
										snake_case       :     Optional[int] =    []
										for mel, longer in padded_inputs:
																input_mel.append(__lowerCamelCase      )
																is_longer.append(__lowerCamelCase      )

										if truncation == "fusion" and sum(__lowerCamelCase      ) == 0:
																# if no audio is longer than 10s, then randomly select one audio to be longer
																snake_case       :     Any =    np.random.randint(0,    len(__lowerCamelCase      )      )
																snake_case       :     Union[str, Any] =    True

										if isinstance(input_mel[0],    __lowerCamelCase      ):
																snake_case       :     Any =    [np.asarray(__lowerCamelCase,    dtype=np.floataa      ) for feature in input_mel]

										# is_longer is a list of bool
										snake_case       :     Any =    [[longer] for longer in is_longer]

										snake_case       :     Optional[int] =    {'''input_features''': input_mel, '''is_longer''': is_longer}
										snake_case       :     Dict =    BatchFeature(__lowerCamelCase      )

										if return_tensors is not None:
																snake_case       :     Any =    input_features.convert_to_tensors(__lowerCamelCase      )

										return input_features



'''simple docstring'''





import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def      A (       A_							:					str       ):
						monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings'''    ,							set()       )

@pytest.fixture
def      A (       A_							:					Optional[Any]       ):

						class 						a   :




										def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     List[str] =    metric_id




						class 						a   :
										_snake_case		    = [MetricMock(__magic_name__    ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]




										def 	__snake_case							(							self	:				str      ):
																return self._metrics




						monkeypatch.setattr('''datasets.inspect.huggingface_hub'''    ,							HfhMock()       )





@pytest.mark.parametrize(
    '''func, args'''    ,							[(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))]       )
def      A (       A_							:					str    ,							A_							:					Dict    ,							A_							:					int    ,							A_							:					int    ,							A_							:					str       ):
						if "tmp_path" in args:
												snake_case       :     List[str] =    tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args       )
						with pytest.warns(A_    ,							match='''https://huggingface.co/docs/evaluate'''       ):
												func(*A_       )

