

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__	:  Tuple 		=logging.get_logger(__name__)






def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Dict:
							__SCREAMING_SNAKE_CASE								=      WavLMForSequenceClassification.from_pretrained(lowercase_							,     config=lowercase_     )
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''projector.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''projector.bias''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.post_net.linear.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.post_net.linear.bias''']
							return model






def 			__lowercase     (				a__							,     a__							,     a__     )       ->				int:
							__SCREAMING_SNAKE_CASE								=      WavLMForAudioFrameClassification.from_pretrained(lowercase_							,     config=lowercase_     )
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.linear.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.linear.bias''']
							return model






def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Dict:
							__SCREAMING_SNAKE_CASE								=      WavLMForXVector.from_pretrained(lowercase_							,     config=lowercase_     )
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''connector.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''connector.bias''']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
														__SCREAMING_SNAKE_CASE								=      downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														__SCREAMING_SNAKE_CASE								=      downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
							__SCREAMING_SNAKE_CASE								=      downstream_dict['''objective.W''']
							return model






@torch.no_grad()
def 			__lowercase     (				a__							,     a__							,     a__							,     a__     )       ->				Any:
							__SCREAMING_SNAKE_CASE								=      torch.load(lowercase_							,     map_location='cpu'     )

							__SCREAMING_SNAKE_CASE								=      checkpoint['''Downstream''']

							__SCREAMING_SNAKE_CASE								=      WavLMConfig.from_pretrained(lowercase_     )
							__SCREAMING_SNAKE_CASE								=      WavaVecaFeatureExtractor.from_pretrained(
							    lowercase_							,     return_attention_mask=lowercase_							,     do_normalize=lowercase_     )

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

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

							hf_feature_extractor.save_pretrained(lowercase_     )
							hf_model.save_pretrained(lowercase_     )


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











def 			__lowercase     (				a__							,     a__     )       ->				List[str]:
							if n == 0:
														return 1

							elif n % 2 == 1:
														return binary_exponentiation(a__							,     n - 1     ) * a

							else:
														__SCREAMING_SNAKE_CASE								=      binary_exponentiation(a__							,     n / 2     )
														return b * b


if __name__ == "__main__":
			try:
						lowerCAmelCase__	:  str 		=int(input('''Enter Base : ''').strip())
						lowerCAmelCase__	:  Optional[int] 		=int(input('''Enter Power : ''').strip())
			except ValueError:
						print('''Invalid literal for integer''')

			lowerCAmelCase__	:  Any 		=binary_exponentiation(BASE, POWER)
			print(F'''{BASE}^({POWER}) : {RESULT}''')






