






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      :       str				  =   logging.get_logger(__name__)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["input_features", "is_longer"]




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			int=64 ,	UpperCamelCase_       :			Optional[int]=48_000 ,	UpperCamelCase_       :			str=480 ,	UpperCamelCase_       :			int=10 ,	UpperCamelCase_       :			List[str]=1_024 ,	UpperCamelCase_       :			Optional[int]=0.0 ,	UpperCamelCase_       :			List[Any]=False ,	UpperCamelCase_       :			float = 0 ,	UpperCamelCase_       :			float = 14_000 ,	UpperCamelCase_       :			int = None ,	UpperCamelCase_       :			str = "fusion" ,	UpperCamelCase_       :			str = "repeatpad" ,	**UpperCamelCase_       :			Tuple ,	):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	copy.deepcopy(self.__dict__				)
         __A         =	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 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			np.array ,	UpperCamelCase_       :			Optional[np.array] = None				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         __A         =	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
             __A         =	[0]
         if len(ranges[2]				) == 0:
             # if the audio is too short, we just use the first chunk
             __A         =	[0]
         # randomly choose index for each part
         __A         =	np.random.choice(ranges[0]				)
         __A         =	np.random.choice(ranges[1]				)
         __A         =	np.random.choice(ranges[2]				)

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

         __A         =	torch.tensor(mel[None, None, :]				)
         __A         =	torch.nn.functional.interpolate(
             UpperCamelCase_ ,	size=[chunk_frames, 64] ,	mode="""bilinear""" ,	align_corners=UpperCamelCase_				)
         __A         =	mel_shrink[0][0].numpy()
         __A         =	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,	axis=0				)
         return mel_fusion




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			np.array ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple				):


         """simple docstring"""



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

         else:
             __A         =	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":
                     __A         =	int(max_length / len(UpperCamelCase_				)				)
                     __A         =	np.stack(np.tile(UpperCamelCase_ ,	n_repeat + 1				)				)[:max_length]
                 if padding == "repeatpad":
                     __A         =	int(max_length / len(UpperCamelCase_				)				)
                     __A         =	np.stack(np.tile(UpperCamelCase_ ,	UpperCamelCase_				)				)
                 __A         =	np.pad(UpperCamelCase_ ,	(0, max_length - waveform.shape[0]) ,	mode="""constant""" ,	constant_values=0				)

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

         return input_mel, longer





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


         """simple docstring"""



         __A         =	truncation if truncation is not None else self.truncation
         __A         =	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."""				)

         __A         =	isinstance(UpperCamelCase_ ,	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}"				)
         __A         =	is_batched_numpy or (
             isinstance(UpperCamelCase_ ,	(list, tuple)				) and (isinstance(raw_speech[0] ,	(np.ndarray, tuple, list)				))
         )

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

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

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

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

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

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

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

         __A         =	{"""input_features""": input_mel, """is_longer""": is_longer}
         __A         =	BatchFeature(UpperCamelCase_				)

         if return_tensors is not None:
             __A         =	input_features.convert_to_tensors(UpperCamelCase_				)

         return input_features












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__a      :       List[str]				  =   pytest.mark.integration



@pytest.mark.parametrize("""path"""			,				["""paws""", """csv"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]    )						->      Tuple:





    """simple docstring"""

    inspect_dataset(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""    )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
@pytest.mark.parametrize("""path"""			,				["""accuracy"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

    inspect_metric(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.parametrize(
    """path, config_name, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	get_dataset_config_info(__lowercase			,				config_name=__lowercase    )
    assert info.config_name == config_name
    assert list(info.splits.keys()    ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			List[str]    )						->      int:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_config_info(__lowercase			,				config_name=__lowercase    )



@pytest.mark.parametrize(
    """path, expected"""			,				[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]    )						->      Any:





    """simple docstring"""

    __A         =	get_dataset_config_names(__lowercase    )
    assert expected in config_names



@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""			,				[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			Tuple			,				__lowercase   :			List[Any]    )						->      List[str]:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert list(infos.keys()    ) == expected_configs
    __A         =	expected_configs[0]
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			str			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Union[str, Any]:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_split_names(__lowercase			,				config_name=__lowercase    )




