
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[Any]    =	logging.get_logger(__name__)

class 							_UpperCamelCase					(       snake_case_	):






			'''simple docstring'''

			__UpperCamelCase   :    List[Any]          =			["""input_values""", """padding_mask"""]

			def __init__(							self			:							int    ,						snake_case_			:							Tuple = 1    ,						snake_case_			:							List[Any] = 2_4000    ,						snake_case_			:							Optional[Any] = 0.0    ,						snake_case_			:							Union[str, Any] = None    ,						snake_case_			:							List[str] = None    ,						**snake_case_			:							Optional[Any]    ,						):
							super().__init__(feature_size=snake_case_    ,						sampling_rate=snake_case_    ,						padding_value=snake_case_    ,						**snake_case_   )
							UpperCamelCase_:				Dict						=    chunk_length_s
							UpperCamelCase_:				str						=    overlap

			@property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							if self.chunk_length_s is None:
											return None
							else:
											return int(self.chunk_length_s * self.sampling_rate   )

			@property
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							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			:							List[str]    ,						snake_case_			:							Any    ,						snake_case_			:							Optional[Any] = None    ,						snake_case_			:							Any = False    ,						snake_case_			:							Dict = None    ,						snake_case_			:							Any = None    ,						snake_case_			:							Optional[Any] = 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_:				int						=    True

							UpperCamelCase_:				Tuple						=    bool(
							    isinstance(snake_case_    ,						(list, tuple)   ) and (isinstance(raw_audio[0]    ,						(np.ndarray, tuple, list)   ))   )

							if is_batched:
											UpperCamelCase_:				int						=    [np.asarray(snake_case_    ,						dtype=np.floataa   ).T for audio in raw_audio]
							elif not is_batched and not isinstance(snake_case_    ,						np.ndarray   ):
											UpperCamelCase_:				Dict						=    np.asarray(snake_case_    ,						dtype=np.floataa   )
							elif isinstance(snake_case_    ,						np.ndarray   ) and raw_audio.dtype is np.dtype(np.floataa   ):
											UpperCamelCase_:				List[Any]						=    raw_audio.astype(np.floataa   )

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

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

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

        # normal padding on batch
							if padded_inputs is None:
											UpperCamelCase_:				Optional[int]						=    self.pad(
											    snake_case_    ,						max_length=snake_case_    ,						truncation=snake_case_    ,						padding=snake_case_    ,						return_attention_mask=snake_case_    ,						)
											if padding:
															UpperCamelCase_:				Dict						=    padded_inputs.pop("""attention_mask"""   )

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

							UpperCamelCase_:				int						=    input_values
							if return_tensors is not None:
											UpperCamelCase_:				Optional[int]						=    padded_inputs.convert_to_tensors(snake_case_   )

							return padded_inputs








import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


