
"""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


UpperCAmelCase__   :					Optional[Any]												=				logging.get_logger(__name__)



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     ['''input_values''', '''padding_mask''']



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 2_40_00     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__(feature_size=SCREAMING_SNAKE_CASE__     , sampling_rate=SCREAMING_SNAKE_CASE__     , padding_value=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     chunk_length_s
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     overlap



				@property
				def 			__magic_name__					(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 			__magic_name__					(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     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 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
																		SCREAMING_SNAKE_CASE__		: Tuple     =     True

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     bool(
											    isinstance(SCREAMING_SNAKE_CASE__     , (list, tuple)					) and (isinstance(raw_audio[0]     , (np.ndarray, tuple, list)					))					)

											if is_batched:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     [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					):
																		SCREAMING_SNAKE_CASE__		: str     =     np.asarray(SCREAMING_SNAKE_CASE__     , dtype=np.floataa					)
											elif isinstance(SCREAMING_SNAKE_CASE__     , np.ndarray					) and raw_audio.dtype is np.dtype(np.floataa					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     raw_audio.astype(np.floataa					)

											# always return batch
											if not is_batched:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [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'''					)

											SCREAMING_SNAKE_CASE__		: str     =     None
											SCREAMING_SNAKE_CASE__		: Dict     =     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:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     min(array.shape[0] for array in raw_audio					)
																									SCREAMING_SNAKE_CASE__		: int     =     int(np.floor(max_length / self.chunk_stride					)					)
																									SCREAMING_SNAKE_CASE__		: List[str]     =     (nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     max(array.shape[0] for array in raw_audio					)
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     int(np.ceil(max_length / self.chunk_stride					)					)
																									SCREAMING_SNAKE_CASE__		: List[str]     =     (nb_step - 1) * self.chunk_stride + self.chunk_length
																									SCREAMING_SNAKE_CASE__		: str     =     """max_length"""
																		else:
																									SCREAMING_SNAKE_CASE__		: str     =     input_values

        # normal padding on batch
											if padded_inputs is None:
																		SCREAMING_SNAKE_CASE__		: Any     =     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:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     padded_inputs.pop("""attention_mask"""					)

											SCREAMING_SNAKE_CASE__		: int     =     []
											for example in padded_inputs.pop("""input_values"""					):
																		if self.feature_size == 1:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     example[..., None]
																		input_values.append(example.T					)

											SCREAMING_SNAKE_CASE__		: List[str]     =     input_values
											if return_tensors is not None:
																		SCREAMING_SNAKE_CASE__		: str     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__					)

											return padded_inputs






"""simple docstring"""




import torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




