



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

A__       =			["""UtteranceLevel"""]


@torch.no_grad()
def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       torch.load(snake_case      ,		map_location="""cpu"""     )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'     )

					_lowerCAmelCase         =       checkpoint["""Downstream"""]

					_lowerCAmelCase         =       HubertConfig.from_pretrained(snake_case     )
					_lowerCAmelCase         =       HubertForSequenceClassification.from_pretrained(snake_case      ,		config=snake_case     )
					_lowerCAmelCase         =       WavaVecaFeatureExtractor.from_pretrained(
					    snake_case      ,		return_attention_mask=snake_case      ,		do_normalize=snake_case     )

					if hf_congfig.use_weighted_layer_sum:
										_lowerCAmelCase         =       checkpoint["""Featurizer"""]["""weights"""]

					_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"""]

					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)






from __future__ import annotations






def        lowerCamelCase_  (				UpperCAmelCase_				:  int						)    ->					list[int]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    [0] * max_one

	for i in range(0				,							UpperCAmelCase_						):
		_UpperCamelCase :		List[Any]								=    i

	for i in range(2				,							UpperCAmelCase_						):
		if totients[i] == i:
			for j in range(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						):
				totients[j] -= totients[j] // i

	return totients






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					bool:




	'''simple docstring'''




	return sorted(str(UpperCAmelCase_						)						) == sorted(str(UpperCAmelCase_						)						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int = 1_0_0_0_0_0_0_0						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[Any]								=    1  # i
	_UpperCamelCase :		List[str]								=    0  # φ(i)
	_UpperCamelCase :		str								=    get_totients(max_n + 1						)

	for i in range(2				,							max_n + 1						):
		_UpperCamelCase :		List[str]								=    totients[i]

		if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase_				,							UpperCAmelCase_						):
			_UpperCamelCase :		Optional[int]								=    i
			_UpperCamelCase :		Tuple								=    t

	return min_numerator


if __name__ == "__main__":
							print(f'{solution() = }')




