






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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Any			=	['''input_values''', '''padding_mask''']


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


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


     @property
     def 				_A       (				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       :	Dict					, UpperCAmelCase_       :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					, UpperCAmelCase_       :	Optional[Union[bool, str, PaddingStrategy]] = None					, UpperCAmelCase_       :	Optional[bool] = False					, UpperCAmelCase_       :	Optional[int] = None					, UpperCAmelCase_       :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase_       :	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
         SCREAMING_SNAKE_CASE       :      int							=    True

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

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

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

       # verify inputs are valid
       for idx, example in enumerate(UpperCAmelCase_      ):
         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       :      List[str]							=    None
       SCREAMING_SNAKE_CASE       :      List[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:
           SCREAMING_SNAKE_CASE       :      Tuple							=    min(array.shape[0] for array in raw_audio      )
           SCREAMING_SNAKE_CASE       :      Dict							=    int(np.floor(max_length / self.chunk_stride      )      )
           SCREAMING_SNAKE_CASE       :      int							=    (nb_step - 1) * self.chunk_stride + self.chunk_length
         elif padding:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    max(array.shape[0] for array in raw_audio      )
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    int(np.ceil(max_length / self.chunk_stride      )      )
           SCREAMING_SNAKE_CASE       :      int							=    (nb_step - 1) * self.chunk_stride + self.chunk_length
           SCREAMING_SNAKE_CASE       :      Dict							=    "max_length"
         else:
           SCREAMING_SNAKE_CASE       :      List[str]							=    input_values

        # normal padding on batch
       if padded_inputs is None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    self.pad(
             UpperCAmelCase_					, max_length=UpperCAmelCase_					, truncation=UpperCAmelCase_					, padding=UpperCAmelCase_					, return_attention_mask=UpperCAmelCase_					, )
         if padding:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    padded_inputs.pop("attention_mask"      )

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

       SCREAMING_SNAKE_CASE       :      Tuple							=    input_values
       if return_tensors is not None:
         SCREAMING_SNAKE_CASE       :      Any							=    padded_inputs.convert_to_tensors(UpperCAmelCase_      )

       return padded_inputs














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    HfArgumentParser(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    parser.parse_args_into_dataclasses()[0]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TensorFlowBenchmark(args=lowercase				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    parser.parse_args_into_dataclasses()[0]
  except ValueError as e:
    SCREAMING_SNAKE_CASE       :      int							=    "Arg --no_{0} is no longer used, please use --no-{0} instead."
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    " ".join(str(lowercase				).split(" "				)[:-1]				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ""
    SCREAMING_SNAKE_CASE       :      Any							=    eval(str(lowercase				).split(" "				)[-1]				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    []
    for arg in depreciated_args:
      # arg[2:] removes '--'
      if arg[2:] in TensorFlowBenchmark.deprecated_args:
        # arg[5:] removes '--no_'
        full_error_msg += arg_error_msg.format(arg[5:]				)
      else:
        wrong_args.append(lowercase				)
    if len(lowercase				) > 0:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    full_error_msg + begin_error_msg + str(lowercase				)
    raise ValueError(lowercase				)
  benchmark.run()


if __name__ == "__main__":
   main()






