



import argparse

import torch

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


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





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





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





def 						__lowerCamelCase       (      __magic_name__	:  Optional[int]     , __magic_name__	:  Dict     , __magic_name__	:  Any						):
							a__:       int											=WavLMForXVector.from_pretrained(__magic_name__     , config=__magic_name__						)
							a__:       Any											=downstream_dict["connector.weight"]
							a__:       Union[str, Any]											=downstream_dict["connector.bias"]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
														a__:       Union[str, Any]											=downstream_dict[
														    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
														]
														a__:       Union[str, Any]											=downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

							a__:       List[Any]											=downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
							a__:       Tuple											=downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
							a__:       Dict											=downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
							a__:       int											=downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
							a__:       Optional[Any]											=downstream_dict["objective.W"]
							return model





@torch.no_grad()
def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  Any     , __magic_name__	:  int     , __magic_name__	:  Any						):
							a__:       Any											=torch.load(__magic_name__     , map_location="cpu"						)

							a__:       Union[str, Any]											=checkpoint["Downstream"]

							a__:       List[Any]											=WavLMConfig.from_pretrained(__magic_name__						)
							a__:       List[str]											=WavaVecaFeatureExtractor.from_pretrained(
							    __magic_name__     , return_attention_mask=__magic_name__     , do_normalize=__magic_name__						)

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

							if hf_config.use_weighted_layer_sum:
														a__:       Optional[Any]											=checkpoint["Featurizer"]["weights"]

							hf_feature_extractor.save_pretrained(__magic_name__						)
							hf_model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__UpperCAmelCase						  =     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.''')
		__UpperCAmelCase						  =     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 typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


						def   _lowerCamelCase			( self						:  int			):
													return self.head.data if self.head is not None else None


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=temp.next


# Code execution starts here
if __name__ == "__main__":
		# Start with the empty stack
		__UpperCAmelCase						  =     Stack()

		# Insert 4 at the beginning. So stack becomes 4->None
		print('''Stack operations using Doubly LinkedList''')
		stack.push(4)

		# Insert 5 at the beginning. So stack becomes 4->5->None
		stack.push(5)

		# Insert 6 at the beginning. So stack becomes 4->5->6->None
		stack.push(6)

		# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
		stack.push(7)

		# Print the stack
		stack.print_stack()

		# Print the top element
		print('''\nTop element is ''', stack.top())

		# Print the stack size
		print('''Size of the stack is ''', len(stack))

		# pop the top element
		stack.pop()

		# pop the top element
		stack.pop()

		# two elements have now been popped off
		stack.print_stack()

		# Print True if the stack is empty else False
		print('''\nstack is empty:''', stack.is_empty())





