
"""simple docstring"""
import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__					,  config=UpperCamelCase__   )
	_UpperCAmelCase			=    downstream_dict["projector.weight"]
	_UpperCAmelCase			=    downstream_dict["projector.bias"]
	_UpperCAmelCase			=    downstream_dict["model.post_net.linear.weight"]
	_UpperCAmelCase			=    downstream_dict["model.post_net.linear.bias"]
	return model




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__					,  config=UpperCamelCase__   )
	_UpperCAmelCase			=    downstream_dict["model.linear.weight"]
	_UpperCAmelCase			=    downstream_dict["model.linear.bias"]
	return model




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    WavaVecaForXVector.from_pretrained(UpperCamelCase__					,  config=UpperCamelCase__   )
	_UpperCAmelCase			=    downstream_dict["connector.weight"]
	_UpperCAmelCase			=    downstream_dict["connector.bias"]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
		_UpperCAmelCase			=    downstream_dict[
		    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
		]
		_UpperCAmelCase			=    downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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




@torch.no_grad()
def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    torch.load(UpperCamelCase__					,  map_location="cpu"   )

	_UpperCAmelCase			=    checkpoint["Downstream"]

	_UpperCAmelCase			=    WavaVecaConfig.from_pretrained(UpperCamelCase__   )
	_UpperCAmelCase			=    WavaVecaFeatureExtractor.from_pretrained(
	    UpperCamelCase__					,  return_attention_mask=UpperCamelCase__					,  do_normalize=UpperCamelCase__   )

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

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

	hf_feature_extractor.save_pretrained(UpperCamelCase__   )
	hf_model.save_pretrained(UpperCamelCase__   )


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












"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


