"""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__						:						int		  =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["input_values", "padding_mask"]







					def __init__(       self     :			str					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			int = 24_000					,lowerCamelCase__     :			float = 0.0					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			float = None					,**lowerCamelCase__     :			Any					,):
									super().__init__(feature_size=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,padding_value=lowerCamelCase__					,**lowerCamelCase__    )
									UpperCAmelCase__    =							chunk_length_s
									UpperCAmelCase__    =							overlap







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									if self.chunk_length_s is None:
													return None
									else:
													return int(self.chunk_length_s * self.sampling_rate    )







					@property
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									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     :			Union[str, Any]					,lowerCamelCase__     :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase__     :			Optional[Union[bool, str, PaddingStrategy]] = None					,lowerCamelCase__     :			Optional[bool] = False					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			Optional[int] = None					,):
									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(lowerCamelCase__					,(list, tuple)    ) and (isinstance(raw_audio[0]					,(np.ndarray, tuple, list)    ))    )

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

									# verify inputs are valid
									for idx, example in enumerate(lowerCamelCase__    ):
													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(
													    lowerCamelCase__					,max_length=lowerCamelCase__					,truncation=lowerCamelCase__					,padding=lowerCamelCase__					,return_attention_mask=lowerCamelCase__					,)
													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(lowerCamelCase__    )

									return padded_inputs




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
						import ctypes
						import msvcrt  # noqa
						class        snake_case (				ctypes.Structure       ):




											"""simple docstring"""

											snake_case__       					=				[("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25l' )
								sys.stdout.flush()



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25h' )
								sys.stdout.flush()







@contextmanager
def 						a_		(      ):
				try:
								hide_cursor()
								yield
				finally:
								show_cursor()



