



import argparse

import torch

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


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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						WavLMForSequenceClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["projector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["projector.bias"]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.post_net.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.post_net.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["model.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						downstream_dict["model.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMForXVector.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						downstream_dict["connector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
  __SCREAMING_SNAKE_CASE							:     str							=						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							:     Optional[int]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
 __SCREAMING_SNAKE_CASE							:     List[str]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
 __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict["objective.W"]
 return model






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  str		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						torch.load(__lowerCamelCase		,						map_location="cpu"  )

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						checkpoint["Downstream"]

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMConfig.from_pretrained(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						WavaVecaFeatureExtractor.from_pretrained(
     __lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						do_normalize=__lowerCamelCase  )

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

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

 hf_feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


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










"""simple docstring"""


def   a__			(					__lowercase )						->						bool:
				# must be
				_A       	=      [list(__lowercase ) for row in matrix]
				if matrix and isinstance(__lowercase  ,      __lowercase ):
								if isinstance(matrix[0]  ,      __lowercase ):
												_A       	=      0
												for row in matrix:
																if prev_len == 0:
																				_A       	=      len(__lowercase )
																				_A       	=      True
																else:
																				_A       	=      prev_len == len(__lowercase )
								else:
												_A       	=      True
				else:
								_A       	=      False

				return result






def   a__			(					__lowercase )						->						None:
				if check_matrix(__lowercase ) and len(__lowercase ) > 0:
								_A       	=      [list(__lowercase ) for row in a]
								_A       	=      len(__lowercase )
								if isinstance(a[0]  ,      __lowercase ):
												_A       	=      len(a[0] )
								else:
												for dat in a:
																print(__lowercase )
												return

								# horizotal printing increasing
								for i in range(0  ,      __lowercase ):
												print(a[0][i] )
								# vertical printing down
								for i in range(1  ,      __lowercase ):
												print(a[i][mat_col - 1] )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2  ,      -1  ,      -1 ):
																print(a[mat_row - 1][i] )
        # vertical printing up
								for i in range(mat_row - 2  ,      0  ,      -1 ):
												print(a[i][0] )
								_A       	=      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(__lowercase ) > 0:
												spiral_print_clockwise(__lowercase )
								else:
												return
				else:
								print("Not a valid matrix" )
								return






def   a__			(					__lowercase )						->						list[int]:
				if matrix:
								return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__lowercase ) )[::-1] )
				else:
								return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a_											=     [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)