code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _a      :				Tuple											=					logging.get_logger(__name__)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      str:
  			__lowerCAmelCase      		=       WavLMForSequenceClassification.from_pretrained(lowercase      ,			config=lowercase			)
  			__lowerCAmelCase      		=       downstream_dict["""projector.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""projector.bias"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.post_net.linear.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.post_net.linear.bias"""]
  			return model
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Tuple:
  			__lowerCAmelCase      		=       WavLMForAudioFrameClassification.from_pretrained(lowercase      ,			config=lowercase			)
  			__lowerCAmelCase      		=       downstream_dict["""model.linear.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.linear.bias"""]
  			return model
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Tuple:
  			__lowerCAmelCase      		=       WavLMForXVector.from_pretrained(lowercase      ,			config=lowercase			)
  			__lowerCAmelCase      		=       downstream_dict["""connector.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""connector.bias"""]
  			for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
  						__lowerCAmelCase      		=       downstream_dict[
  						    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
  						]
  						__lowerCAmelCase      		=       downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
  
  			__lowerCAmelCase      		=       downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  			__lowerCAmelCase      		=       downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  			__lowerCAmelCase      		=       downstream_dict["""objective.W"""]
  			return model
  
  
  
  
  
  @torch.no_grad()
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      int:
  			__lowerCAmelCase      		=       torch.load(lowercase      ,			map_location="""cpu"""			)
  
  			__lowerCAmelCase      		=       checkpoint["""Downstream"""]
  
  			__lowerCAmelCase      		=       WavLMConfig.from_pretrained(lowercase			)
  			__lowerCAmelCase      		=       WavaVecaFeatureExtractor.from_pretrained(
  			    lowercase      ,			return_attention_mask=lowercase      ,			do_normalize=lowercase			)
  
  			__lowerCAmelCase      		=       hf_config.architectures[0]
  			if arch.endswith("""ForSequenceClassification"""			):
  						__lowerCAmelCase      		=       convert_classification(lowercase      ,			lowercase      ,			lowercase			)
  			elif arch.endswith("""ForAudioFrameClassification"""			):
  						__lowerCAmelCase      		=       convert_diarization(lowercase      ,			lowercase      ,			lowercase			)
  			elif arch.endswith("""ForXVector"""			):
  						__lowerCAmelCase      		=       convert_xvector(lowercase      ,			lowercase      ,			lowercase			)
  			else:
  						raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'			)
  
  			if hf_config.use_weighted_layer_sum:
  						__lowerCAmelCase      		=       checkpoint["""Featurizer"""]["""weights"""]
  
  			hf_feature_extractor.save_pretrained(lowercase			)
  			hf_model.save_pretrained(lowercase			)
  
  
  if __name__ == "__main__":
  	_a      :				Dict											=					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.""")
  	_a      :				Any											=					parser.parse_args()
  	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import itertools
  from dataclasses import dataclass
  from io import StringIO
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.features.features import require_storage_cast
  from datasets.table import table_cast
  
  
  _a      :				Tuple											=					datasets.utils.logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 					_UpperCAmelCase							(		datasets.BuilderConfig     ):
  		a     :     Optional[datasets.Features]     						=None
  		a     :     str     						="utf-8"
  		a     :     Optional[str]     						=None
  		a     :     int     						=10 << 20  # 10MB
  		a     :     bool     						=False
  		a     :     str     						="line"
  
  
  
  
  
  class 					_UpperCAmelCase							(		datasets.ArrowBasedBuilder     ):
  		a     :     Optional[int]     						=TextConfig
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return datasets.DatasetInfo(features=self.config.features    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if not self.config.data_files:
  								raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'    )
  					__lowerCAmelCase      		=       dl_manager.download_and_extract(self.config.data_files    )
  					if isinstance(__SCREAMING_SNAKE_CASE,(str, list, tuple)    ):
  								__lowerCAmelCase      		=       data_files
  								if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       [files]
  								__lowerCAmelCase      		=       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE    ) for file in files]
  								return [datasets.SplitGenerator(name=datasets.Split.TRAIN,gen_kwargs={"""files""": files}    )]
  					__lowerCAmelCase      		=       []
  					for split_name, files in data_files.items():
  								if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       [files]
  								__lowerCAmelCase      		=       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE    ) for file in files]
  								splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE,gen_kwargs={"""files""": files}    )    )
  					return splits
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if self.config.features is not None:
  								__lowerCAmelCase      		=       self.config.features.arrow_schema
  								if all(not require_storage_cast(__SCREAMING_SNAKE_CASE    ) for feature in self.config.features.values()    ):
  											# cheaper cast
  											__lowerCAmelCase      		=       pa_table.cast(__SCREAMING_SNAKE_CASE    )
  								else:
  											# more expensive cast; allows str <-> int/float or str to Audio for example
  											__lowerCAmelCase      		=       table_cast(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								return pa_table
  					else:
  								return pa_table.cast(pa.schema({"""text""": pa.string()}    )    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       list(self.config.features    ) if self.config.features is not None else ["""text"""]
  					for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE    )    ):
  								# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
  								with open(__SCREAMING_SNAKE_CASE,encoding=self.config.encoding,errors=self.config.errors    ) as f:
  											if self.config.sample_by == "line":
  														__lowerCAmelCase      		=       0
  														while True:
  																	__lowerCAmelCase      		=       f.read(self.config.chunksize    )
  																	if not batch:
  																				break
  																	batch += f.readline()  # finish current line
  																	# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
  																	__lowerCAmelCase      		=       StringIO(__SCREAMING_SNAKE_CASE    ).readlines()
  																	if not self.config.keep_linebreaks:
  																				__lowerCAmelCase      		=       [line.rstrip("""\n"""    ) for line in batch]
  																	__lowerCAmelCase      		=       pa.Table.from_arrays([pa.array(__SCREAMING_SNAKE_CASE    )],names=__SCREAMING_SNAKE_CASE    )
  																	# Uncomment for debugging (will print the Arrow table size and elements)
  																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
  																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
  																	yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE    )
  																	batch_idx += 1
  											elif self.config.sample_by == "paragraph":
  														__lowerCAmelCase      		=       0
  														__lowerCAmelCase      		=       """"""
  														while True:
  																	__lowerCAmelCase      		=       f.read(self.config.chunksize    )
  																	if not new_batch:
  																				break
  																	batch += new_batch
  																	batch += f.readline()  # finish current line
  																	__lowerCAmelCase      		=       batch.split("""\n\n"""    )
  																	__lowerCAmelCase      		=       pa.Table.from_arrays(
  																	    [pa.array([example for example in batch[:-1] if example]    )],names=__SCREAMING_SNAKE_CASE    )
  																	# Uncomment for debugging (will print the Arrow table size and elements)
  																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
  																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
  																	yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE    )
  																	batch_idx += 1
  																	__lowerCAmelCase      		=       batch[-1]
  														if batch:
  																	__lowerCAmelCase      		=       pa.Table.from_arrays([pa.array([batch]    )],names=__SCREAMING_SNAKE_CASE    )
  																	yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE    )
  											elif self.config.sample_by == "document":
  														__lowerCAmelCase      		=       f.read()
  														__lowerCAmelCase      		=       pa.Table.from_arrays([pa.array([text]    )],names=__SCREAMING_SNAKE_CASE    )
  														yield file_idx, self._cast_table(__SCREAMING_SNAKE_CASE    )
  
  