




import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase	:       Any     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      List[Any],						A	:      List[Any],						A	:      Any     ):






   '''simple docstring'''







   a					=    UniSpeechSatForSequenceClassification.from_pretrained(A,						config=A     )
   a					=    downstream_dict["projector.weight"]
   a					=    downstream_dict["projector.bias"]
   a					=    downstream_dict["model.post_net.linear.weight"]
   a					=    downstream_dict["model.post_net.linear.bias"]
   return model




def     __magic_name__       (				A	:      Any,						A	:      Tuple,						A	:      List[str]     ):






   '''simple docstring'''







   a					=    UniSpeechSatForAudioFrameClassification.from_pretrained(A,						config=A     )
   a					=    downstream_dict["model.linear.weight"]
   a					=    downstream_dict["model.linear.bias"]
   return model




def     __magic_name__       (				A	:      str,						A	:      Optional[Any],						A	:      List[str]     ):






   '''simple docstring'''







   a					=    UniSpeechSatForXVector.from_pretrained(A,						config=A     )
   a					=    downstream_dict["connector.weight"]
   a					=    downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
      a					=    downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      a					=    downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   a					=    downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   a					=    downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   a					=    downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   a					=    downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   a					=    downstream_dict["objective.W"]
   return model




@torch.no_grad()
def     __magic_name__       (				A	:      List[Any],						A	:      Optional[Any],						A	:      int,						A	:      List[Any]     ):






   '''simple docstring'''







   a					=    torch.load(A,						map_location="cpu"     )

   a					=    checkpoint["Downstream"]

   a					=    UniSpeechSatConfig.from_pretrained(A     )
   a					=    WavaVecaFeatureExtractor.from_pretrained(
       A,						return_attention_mask=A,						do_normalize=A     )

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

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

   hf_feature_extractor.save_pretrained(A     )
   hf_model.save_pretrained(A     )


if __name__ == "__main__":
  __lowerCAmelCase	:       Union[str, Any]     =	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.')
  __lowerCAmelCase	:       Optional[int]     =	parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase	:       Tuple     =	subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase	:       Tuple     =	subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()

__lowerCAmelCase	:       Dict     =	'|'.join(sys.argv[1:])
__lowerCAmelCase	:       List[Any]     =	re.compile(rF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase	:       List[Any]     =	[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')






