

from __future__ import annotations

from collections.abc import Callable
def     A__					(						SCREAMING_SNAKE_CASE_      :			Callable[[int | float], int | float]							,  SCREAMING_SNAKE_CASE_      :			int | float							,  SCREAMING_SNAKE_CASE_      :			int | float							,  SCREAMING_SNAKE_CASE_      :			int = 1_00							,  )     ->     float:






   """simple docstring"""







   _UpperCAmelCase											=      x_start
   _UpperCAmelCase											=      fnc(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      0.0

   for _ in range(SCREAMING_SNAKE_CASE_	):
      # Approximates small segments of curve as linear and solve
      # for trapezoidal area
      _UpperCAmelCase											=      (x_end - x_start) / steps + xa
      _UpperCAmelCase											=      fnc(SCREAMING_SNAKE_CASE_	)
      area += abs(fxa + fxa	) * (xa - xa) / 2

      # Increment step
      _UpperCAmelCase											=      xa
      _UpperCAmelCase											=      fxa
   return area


if __name__ == "__main__":
   def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]	)     ->     Union[str, Any]:






      """simple docstring"""







      return x**3

   print("f(x) = x^3")
   print("The area between the curve, x = -10, x = 10 and the x axis is:")
   UpperCAmelCase_											=  10
   while i <= 10_00_00:
      UpperCAmelCase_											=  trapezoidal_area(f, -5, 5, i)
      print(f'''with {i} steps: {area}''')
      i *= 10






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_											=  logging.get_logger(__name__)
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Dict	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''projector.weight''']
   _UpperCAmelCase											=      downstream_dict['''projector.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			List[Any]	)     ->     Optional[int]:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''model.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''connector.weight''']
   _UpperCAmelCase											=      downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
      _UpperCAmelCase											=      downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _UpperCAmelCase											=      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase											=      downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			List[str]							,  SCREAMING_SNAKE_CASE_      :			int							,  SCREAMING_SNAKE_CASE_      :			List[str]	)     ->     Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase											=      torch.load(SCREAMING_SNAKE_CASE_							,  map_location='''cpu'''	)

   _UpperCAmelCase											=      checkpoint['''Downstream''']

   _UpperCAmelCase											=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_							,  return_attention_mask=SCREAMING_SNAKE_CASE_							,  do_normalize=SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''	):
      _UpperCAmelCase											=      convert_classification(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForAudioFrameClassification'''	):
      _UpperCAmelCase											=      convert_diarization(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForXVector'''	):
      _UpperCAmelCase											=      convert_xvector(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

   if hf_config.use_weighted_layer_sum:
      _UpperCAmelCase											=      checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  argparse.ArgumentParser()
   parser.add_argument(
       "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
   )
   parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
   parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
   UpperCAmelCase_											=  parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)