


import argparse

import torch

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


logging.set_verbosity_info()
_lowercase       :      Any	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[Any]						) ->   Union[str, Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Any						) ->   List[str]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Tuple						) ->   int:





	"""simple docstring"""







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

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





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]						) ->   Union[str, Any]:





	"""simple docstring"""







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

	A								=				checkpoint["""Downstream"""]

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

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

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

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


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







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



