






import argparse

import torch

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


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




def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							Tuple					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						WavaVecaForSequenceClassification.from_pretrained(__snake_case					,	config=__snake_case  )
					UpperCAmelCase_   :				Optional[Any]						=						downstream_dict['projector.weight']
					UpperCAmelCase_   :				List[Any]						=						downstream_dict['projector.bias']
					UpperCAmelCase_   :				Tuple						=						downstream_dict['model.post_net.linear.weight']
					UpperCAmelCase_   :				List[str]						=						downstream_dict['model.post_net.linear.bias']
					return model




def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							str					,	__snake_case   :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						WavaVecaForAudioFrameClassification.from_pretrained(__snake_case					,	config=__snake_case  )
					UpperCAmelCase_   :				List[str]						=						downstream_dict['model.linear.weight']
					UpperCAmelCase_   :				Union[str, Any]						=						downstream_dict['model.linear.bias']
					return model




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						WavaVecaForXVector.from_pretrained(__snake_case					,	config=__snake_case  )
					UpperCAmelCase_   :				List[Any]						=						downstream_dict['connector.weight']
					UpperCAmelCase_   :				List[str]						=						downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
										UpperCAmelCase_   :				int						=						downstream_dict[
										    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
										]
										UpperCAmelCase_   :				str						=						downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

					UpperCAmelCase_   :				str						=						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					UpperCAmelCase_   :				int						=						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					UpperCAmelCase_   :				List[Any]						=						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					UpperCAmelCase_   :				int						=						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					UpperCAmelCase_   :				List[Any]						=						downstream_dict['objective.W']
					return model




@torch.no_grad()
def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							str					,	__snake_case   :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Dict						=						torch.load(__snake_case					,	map_location='cpu'  )

					UpperCAmelCase_   :				Dict						=						checkpoint['Downstream']

					UpperCAmelCase_   :				Any						=						WavaVecaConfig.from_pretrained(__snake_case  )
					UpperCAmelCase_   :				int						=						WavaVecaFeatureExtractor.from_pretrained(
					    __snake_case					,	return_attention_mask=__snake_case					,	do_normalize=__snake_case  )

					UpperCAmelCase_   :				Optional[Any]						=						hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'  ):
										UpperCAmelCase_   :				Union[str, Any]						=						convert_classification(__snake_case					,	__snake_case					,	__snake_case  )
					elif arch.endswith('ForAudioFrameClassification'  ):
										UpperCAmelCase_   :				str						=						convert_diarization(__snake_case					,	__snake_case					,	__snake_case  )
					elif arch.endswith('ForXVector'  ):
										UpperCAmelCase_   :				Optional[Any]						=						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:
										UpperCAmelCase_   :				Dict						=						checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(__snake_case  )
					hf_model.save_pretrained(__snake_case  )


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)











def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


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

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






