


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


A							:					int     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	["""input_values""", """padding_mask"""]


					def __init__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		int = 1     ,    SCREAMING_SNAKE_CASE	:		int = 2_4_0_0_0     ,    SCREAMING_SNAKE_CASE	:		float = 0.0     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(feature_size=SCREAMING_SNAKE_CASE     ,    sampling_rate=SCREAMING_SNAKE_CASE     ,    padding_value=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		chunk_length_s
											__snake_case				=		overlap


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	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	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[bool, str, PaddingStrategy]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = False     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = 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
																	__snake_case				=		True

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

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

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

											# verify inputs are valid
											for idx, example in enumerate(SCREAMING_SNAKE_CASE						):
																	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'''						)

											__snake_case				=		None
											__snake_case				=		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:
																							__snake_case				=		min(array.shape[0] for array in raw_audio						)
																							__snake_case				=		int(np.floor(max_length / self.chunk_stride						)						)
																							__snake_case				=		(nb_step - 1) * self.chunk_stride + self.chunk_length
																	elif padding:
																							__snake_case				=		max(array.shape[0] for array in raw_audio						)
																							__snake_case				=		int(np.ceil(max_length / self.chunk_stride						)						)
																							__snake_case				=		(nb_step - 1) * self.chunk_stride + self.chunk_length
																							__snake_case				=		"max_length"
																	else:
																							__snake_case				=		input_values

        # normal padding on batch
											if padded_inputs is None:
																	__snake_case				=		self.pad(
																	    SCREAMING_SNAKE_CASE     ,    max_length=SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE     ,    padding=SCREAMING_SNAKE_CASE     ,    return_attention_mask=SCREAMING_SNAKE_CASE     ,    )
																	if padding:
																							__snake_case				=		padded_inputs.pop("attention_mask"						)

											__snake_case				=		[]
											for example in padded_inputs.pop("input_values"						):
																	if self.feature_size == 1:
																							__snake_case				=		example[..., None]
																	input_values.append(example.T						)

											__snake_case				=		input_values
											if return_tensors is not None:
																	__snake_case				=		padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE						)

											return padded_inputs









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





