






"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			HfArgumentParser(lowerCAmelCase		)
       UpperCAmelCase	    =			parser.parse_args_into_dataclasses()[0]
       UpperCAmelCase	    =			TensorFlowBenchmark(args=lowerCAmelCase		)
       try:
              UpperCAmelCase	    =			parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              UpperCAmelCase	    =			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
              UpperCAmelCase	    =			""" """.join(str(lowerCAmelCase		).split(""" """		)[:-1]		)
              UpperCAmelCase	    =			""""""
              UpperCAmelCase	    =			eval(str(lowerCAmelCase		).split(""" """		)[-1]		)
              UpperCAmelCase	    =			[]
              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(lowerCAmelCase		)
              if len(lowerCAmelCase		) > 0:
                     UpperCAmelCase	    =			full_error_msg + begin_error_msg + str(lowerCAmelCase		)
              raise ValueError(lowerCAmelCase		)
       benchmark.run()


if __name__ == "__main__":
 main()








"""simple docstring"""







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[Any]            =      {
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			target_bandwidths
         UpperCAmelCase	    =			sampling_rate
         UpperCAmelCase	    =			audio_channels
         UpperCAmelCase	    =			normalize
         UpperCAmelCase	    =			chunk_length_s
         UpperCAmelCase	    =			overlap
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_filters
         UpperCAmelCase	    =			num_residual_layers
         UpperCAmelCase	    =			upsampling_ratios
         UpperCAmelCase	    =			norm_type
         UpperCAmelCase	    =			kernel_size
         UpperCAmelCase	    =			last_kernel_size
         UpperCAmelCase	    =			residual_kernel_size
         UpperCAmelCase	    =			dilation_growth_rate
         UpperCAmelCase	    =			use_causal_conv
         UpperCAmelCase	    =			pad_mode
         UpperCAmelCase	    =			compress
         UpperCAmelCase	    =			num_lstm_layers
         UpperCAmelCase	    =			trim_right_ratio
         UpperCAmelCase	    =			codebook_size
         UpperCAmelCase	    =			codebook_dim if codebook_dim is not None else hidden_size
         UpperCAmelCase	    =			use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
                raise ValueError(
                    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''				)

         super().__init__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      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 		UpperCamelCase_				(      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				)				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			np.prod(self.upsampling_ratios				)
         return math.ceil(self.sampling_rate / hop_length				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)
