




'''simple docstring'''






import argparse

import torch

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


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


def  _snake_case       (					A			,   A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     WavaVecaForSequenceClassification.from_pretrained(A			,   config=A	)
							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  _snake_case       (					A			,   A			,   A	)						->   List[str]:
							lowerCAmelCase__	       =     WavaVecaForAudioFrameClassification.from_pretrained(A			,   config=A	)
							lowerCAmelCase__	       =     downstream_dict['''model.linear.weight''']
							lowerCAmelCase__	       =     downstream_dict['''model.linear.bias''']
							return model


def  _snake_case       (					A			,   A			,   A	)						->   str:
							lowerCAmelCase__	       =     WavaVecaForXVector.from_pretrained(A			,   config=A	)
							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  _snake_case       (					A			,   A			,   A			,   A	)						->   int:
							lowerCAmelCase__	       =     torch.load(A			,   map_location='''cpu'''	)

							lowerCAmelCase__	       =     checkpoint['''Downstream''']

							lowerCAmelCase__	       =     WavaVecaConfig.from_pretrained(A	)
							lowerCAmelCase__	       =     WavaVecaFeatureExtractor.from_pretrained(
							    A			,   return_attention_mask=A			,   do_normalize=A	)

							lowerCAmelCase__	       =     hf_config.architectures[0]
							if arch.endswith('''ForSequenceClassification'''	):
														lowerCAmelCase__	       =     convert_classification(A			,   A			,   A	)
							elif arch.endswith('''ForAudioFrameClassification'''	):
														lowerCAmelCase__	       =     convert_diarization(A			,   A			,   A	)
							elif arch.endswith('''ForXVector'''	):
														lowerCAmelCase__	       =     convert_xvector(A			,   A			,   A	)
							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(A	)
							hf_model.save_pretrained(A	)


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)



'''simple docstring'''






def  _snake_case       (					A = 10	)						->   str:
							if not isinstance(A			,   A	) or n < 0:
														raise ValueError('''Invalid input'''	)
							lowerCAmelCase__	       =     10**n
							lowerCAmelCase__	       =     28433 * (pow(2			,   7830457			,   A	)) + 1
							return str(number % modulus	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(f"""{solution(10) = }""")