
'''simple docstring'''
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase					:    List[str]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""input_values""", """padding_mask"""]







				def __init__(       self						,				A_ = 1						,				A_ = 24000						,				A_ = 0.0						,				A_ = None						,				A_ = None						,				**A_						,				)->		Optional[Any]:







											'''simple docstring'''






											super().__init__(feature_size=A_						,				sampling_rate=A_						,				padding_value=A_						,				**A_     )
											UpperCamelCase   			=     chunk_length_s
											UpperCamelCase   			=     overlap







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											if self.chunk_length_s is None:
																		return None
											else:
																		return int(self.chunk_length_s * self.sampling_rate     )







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											if self.chunk_length_s is None or self.overlap is None:
																		return None
											else:
																		return max(1						,				int((1.0 - self.overlap) * self.chunk_length     )     )







				def __call__(       self						,				A_						,				A_ = None						,				A_ = False						,				A_ = None						,				A_ = None						,				A_ = None						,				)->		BatchFeature:







											'''simple docstring'''






											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																									    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
																									    F''' {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.'     )

											if padding and truncation:
																		raise ValueError('Both padding and truncation were set. Make sure you only set one.'     )
											elif padding is None:
																		# by default let's pad the inputs
																		UpperCamelCase   			=     True

											UpperCamelCase   			=     bool(
											    isinstance(A_						,				(list, tuple)     ) and (isinstance(raw_audio[0]						,				(np.ndarray, tuple, list)     ))     )

											if is_batched:
																		UpperCamelCase   			=     [np.asarray(A_						,				dtype=np.floataa     ).T for audio in raw_audio]
											elif not is_batched and not isinstance(A_						,				np.ndarray     ):
																		UpperCamelCase   			=     np.asarray(A_						,				dtype=np.floataa     )
											elif isinstance(A_						,				np.ndarray     ) and raw_audio.dtype is np.dtype(np.floataa     ):
																		UpperCamelCase   			=     raw_audio.astype(np.floataa     )

											# always return batch
											if not is_batched:
																		UpperCamelCase   			=     [np.asarray(A_     ).T]

											# verify inputs are valid
											for idx, example in enumerate(A_     ):
																		if example.ndim > 2:
																									raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''     )
																		if self.feature_size == 1 and example.ndim != 1:
																									raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''     )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																									raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''     )

											UpperCamelCase   			=     None
											UpperCamelCase   			=     BatchFeature({'input_values': raw_audio}     )
											if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																									UpperCamelCase   			=     min(array.shape[0] for array in raw_audio     )
																									UpperCamelCase   			=     int(np.floor(max_length / self.chunk_stride     )     )
																									UpperCamelCase   			=     (nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																									UpperCamelCase   			=     max(array.shape[0] for array in raw_audio     )
																									UpperCamelCase   			=     int(np.ceil(max_length / self.chunk_stride     )     )
																									UpperCamelCase   			=     (nb_step - 1) * self.chunk_stride + self.chunk_length
																									UpperCamelCase   			=     'max_length'
																		else:
																									UpperCamelCase   			=     input_values

        # normal padding on batch
											if padded_inputs is None:
																		UpperCamelCase   			=     self.pad(
																		    A_						,				max_length=A_						,				truncation=A_						,				padding=A_						,				return_attention_mask=A_						,				)
																		if padding:
																									UpperCamelCase   			=     padded_inputs.pop('attention_mask'     )

											UpperCamelCase   			=     []
											for example in padded_inputs.pop('input_values'     ):
																		if self.feature_size == 1:
																									UpperCamelCase   			=     example[..., None]
																		input_values.append(example.T     )

											UpperCamelCase   			=     input_values
											if return_tensors is not None:
																		UpperCamelCase   			=     padded_inputs.convert_to_tensors(A_     )

											return padded_inputs






'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




