




"""simple docstring"""







import argparse

import torch

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


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





def 							lowercase   (snake_case__				:	int ,    snake_case__				:	List[str] ,    snake_case__				:	Optional[int]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			WavLMForSequenceClassification.from_pretrained(snake_case__ ,    config=snake_case__	)
				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 							lowercase   (snake_case__				:	Any ,    snake_case__				:	List[Any] ,    snake_case__				:	Dict	)       ->					Tuple:


				'''simple docstring'''






				lowerCAmelCase		=			WavLMForAudioFrameClassification.from_pretrained(snake_case__ ,    config=snake_case__	)
				lowerCAmelCase		=			downstream_dict["""model.linear.weight"""]
				lowerCAmelCase		=			downstream_dict["""model.linear.bias"""]
				return model





def 							lowercase   (snake_case__				:	Dict ,    snake_case__				:	List[Any] ,    snake_case__				:	Tuple	)       ->					List[Any]:


				'''simple docstring'''






				lowerCAmelCase		=			WavLMForXVector.from_pretrained(snake_case__ ,    config=snake_case__	)
				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 							lowercase   (snake_case__				:	List[Any] ,    snake_case__				:	Union[str, Any] ,    snake_case__				:	Optional[Any] ,    snake_case__				:	List[str]	)       ->					Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase		=			torch.load(snake_case__ ,    map_location="""cpu"""	)

				lowerCAmelCase		=			checkpoint["""Downstream"""]

				lowerCAmelCase		=			WavLMConfig.from_pretrained(snake_case__	)
				lowerCAmelCase		=			WavaVecaFeatureExtractor.from_pretrained(
				    snake_case__ ,    return_attention_mask=snake_case__ ,    do_normalize=snake_case__	)

				lowerCAmelCase		=			hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""	):
								lowerCAmelCase		=			convert_classification(snake_case__ ,    snake_case__ ,    snake_case__	)
				elif arch.endswith("""ForAudioFrameClassification"""	):
								lowerCAmelCase		=			convert_diarization(snake_case__ ,    snake_case__ ,    snake_case__	)
				elif arch.endswith("""ForXVector"""	):
								lowerCAmelCase		=			convert_xvector(snake_case__ ,    snake_case__ ,    snake_case__	)
				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(snake_case__	)
				hf_model.save_pretrained(snake_case__	)


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











"""simple docstring"""







def 							lowercase   (snake_case__				:	int	)       ->					int:


				'''simple docstring'''






				barcode //= 10  # exclude the last digit
				lowerCAmelCase		=			False
				lowerCAmelCase		=			0

				# extract and check each digit
				while barcode != 0:
								lowerCAmelCase		=			1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								lowerCAmelCase		=			not checker

				return (10 - (s % 10)) % 10





def 							lowercase   (snake_case__				:	int	)       ->					bool:


				'''simple docstring'''






				return len(str(snake_case__	)	) == 13 and get_check_digit(snake_case__	) == barcode % 10





def 							lowercase   (snake_case__				:	str	)       ->					int:


				'''simple docstring'''






				if str(snake_case__	).isalpha():
								lowerCAmelCase		=			f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(snake_case__	)
				elif int(snake_case__	) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""	)
				else:
								return int(snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a								=							get_barcode(input('Barcode: ').strip())

		if is_valid(barcode):
				print(f"""'{barcode}' is a valid barcode.""")
		else:
				print(f"""'{barcode}' is NOT a valid barcode.""")





