






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
snake_case    				=		logging.get_logger(__name__)


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    UniSpeechSatForSequenceClassification.from_pretrained(lowercase						,     config=lowercase				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE       :      int							=    downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE       :      str							=    downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE       :      Tuple							=    downstream_dict["model.post_net.linear.bias"]
  return model


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Any							=    UniSpeechSatForAudioFrameClassification.from_pretrained(lowercase						,     config=lowercase				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    downstream_dict["model.linear.weight"]
  SCREAMING_SNAKE_CASE       :      List[str]							=    downstream_dict["model.linear.bias"]
  return model


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    UniSpeechSatForXVector.from_pretrained(lowercase						,     config=lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    downstream_dict["connector.weight"]
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    downstream_dict["connector.bias"]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    downstream_dict[
        F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
    ]
    SCREAMING_SNAKE_CASE       :      int							=    downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

  SCREAMING_SNAKE_CASE       :      Tuple							=    downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
  SCREAMING_SNAKE_CASE       :      str							=    downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
  SCREAMING_SNAKE_CASE       :      int							=    downstream_dict["objective.W"]
  return model


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    torch.load(lowercase						,     map_location="cpu"				)

  SCREAMING_SNAKE_CASE       :      Dict							=    checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE       :      Any							=    UniSpeechSatConfig.from_pretrained(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    WavaVecaFeatureExtractor.from_pretrained(
      lowercase						,     return_attention_mask=lowercase						,     do_normalize=lowercase				)

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hf_config.architectures[0]
  if arch.endswith("ForSequenceClassification"				):
    SCREAMING_SNAKE_CASE       :      List[Any]							=    convert_classification(lowercase						,     lowercase						,     lowercase				)
  elif arch.endswith("ForAudioFrameClassification"				):
    SCREAMING_SNAKE_CASE       :      Dict							=    convert_diarization(lowercase						,     lowercase						,     lowercase				)
  elif arch.endswith("ForXVector"				):
    SCREAMING_SNAKE_CASE       :      str							=    convert_xvector(lowercase						,     lowercase						,     lowercase				)
  else:
    raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''				)

  if hf_config.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE       :      Tuple							=    checkpoint["Featurizer"]["weights"]

  hf_feature_extractor.save_pretrained(lowercase				)
  hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
   snake_case    				=		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.""")
   snake_case    				=		parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)














from __future__ import annotations

from cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






