


import argparse

import torch

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


logging.set_verbosity_info()
snake_case				:    int       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Any 							=       UniSpeechSatForSequenceClassification.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Tuple 							=       downstream_dict['''projector.weight''']
      a						:List[Any] 							=       downstream_dict['''projector.bias''']
      a						:Optional[int] 							=       downstream_dict['''model.post_net.linear.weight''']
      a						:Union[str, Any] 							=       downstream_dict['''model.post_net.linear.bias''']
      return model
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:List[str] 							=       UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Dict 							=       downstream_dict['''model.linear.weight''']
      a						:Any 							=       downstream_dict['''model.linear.bias''']
      return model
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Optional[int] 							=       UniSpeechSatForXVector.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Tuple 							=       downstream_dict['''connector.weight''']
      a						:int 							=       downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
            a						:Optional[int] 							=       downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            a						:str 							=       downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      a						:List[Any] 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      a						:str 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      a						:int 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      a						:List[Any] 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      a						:str 							=       downstream_dict['''objective.W''']
      return model
@torch.no_grad()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:Dict 							=       torch.load(UpperCAmelCase_ ,  map_location='''cpu'''						)

      a						:List[str] 							=       checkpoint['''Downstream''']

      a						:Optional[Any] 							=       UniSpeechSatConfig.from_pretrained(UpperCAmelCase_						)
      a						:Optional[Any] 							=       WavaVecaFeatureExtractor.from_pretrained(
          UpperCAmelCase_ ,  return_attention_mask=UpperCAmelCase_ ,  do_normalize=UpperCAmelCase_						)

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

      if hf_config.use_weighted_layer_sum:
            a						:Union[str, Any] 							=       checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(UpperCAmelCase_						)
      hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
     snake_case				:    Tuple       				=     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				:    Optional[int]       				=     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











class 			a__    (					snake_case__      ):
						pass






class 			a__    (					snake_case__      ):
						pass






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [
											    [],
											    [],
											    [],
											]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																if len(self.queues[priority]			) >= 1_0_0:
																					raise OverflowError("Maximum queue size is 100"			)
																self.queues[priority].append(_A			)
											except IndexError:
																raise ValueError("Valid priorities are 0, 1, and 2"			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for queue in self.queues:
																if queue:
																					return queue.pop(0			)
											raise UnderFlowError("All queues are empty"			)






						def __str__(   self			):




											"""simple docstring"""



											return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues			)			)






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if len(self.queue			) == 1_0_0:
																raise OverFlowError("Maximum queue size is 100"			)
											self.queue.append(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.queue:
																raise UnderFlowError("The queue is empty"			)
											else:
																__lowerCAmelCase				   =       min(self.queue			)
																self.queue.remove(_A			)
																return data






						def __str__(   self			):




											"""simple docstring"""



											return str(self.queue			)







def 	_a      (						):
					__lowerCAmelCase				   =       FixedPriorityQueue()
					fpq.enqueue(0      ,      10     )
					fpq.enqueue(1      ,      70     )
					fpq.enqueue(0      ,      1_00     )
					fpq.enqueue(2      ,      1     )
					fpq.enqueue(2      ,      5     )
					fpq.enqueue(1      ,      7     )
					fpq.enqueue(2      ,      4     )
					fpq.enqueue(1      ,      64     )
					fpq.enqueue(0      ,      1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )







def 	_a      (						):
					__lowerCAmelCase				   =       ElementPriorityQueue()
					epq.enqueue(10     )
					epq.enqueue(70     )
					epq.enqueue(1_00     )
					epq.enqueue(1     )
					epq.enqueue(5     )
					epq.enqueue(7     )
					epq.enqueue(4     )
					epq.enqueue(64     )
					epq.enqueue(1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()




