




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[Any]						,						SCREAMING_SNAKE_CASE						:		Tuple						,						SCREAMING_SNAKE_CASE						:		List[Any] ):






	"""simple docstring"""
	snake_case__						:     Union[str, Any]		=UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__						,						config=UpperCamelCase__ )
	snake_case__						:     Tuple		=downstream_dict['''projector.weight''']
	snake_case__						:     int		=downstream_dict['''projector.bias''']
	snake_case__						:     Any		=downstream_dict['''model.post_net.linear.weight''']
	snake_case__						:     Union[str, Any]		=downstream_dict['''model.post_net.linear.bias''']
	return model







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[Any]						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		int ):






	"""simple docstring"""
	snake_case__						:     List[Any]		=UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__						,						config=UpperCamelCase__ )
	snake_case__						:     Optional[Any]		=downstream_dict['''model.linear.weight''']
	snake_case__						:     Optional[Any]		=downstream_dict['''model.linear.bias''']
	return model







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[str]						,						SCREAMING_SNAKE_CASE						:		Optional[int]						,						SCREAMING_SNAKE_CASE						:		Dict ):






	"""simple docstring"""
	snake_case__						:     Optional[Any]		=UniSpeechSatForXVector.from_pretrained(UpperCamelCase__						,						config=UpperCamelCase__ )
	snake_case__						:     int		=downstream_dict['''connector.weight''']
	snake_case__						:     Optional[Any]		=downstream_dict['''connector.bias''']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
		snake_case__						:     Optional[int]		=downstream_dict[
		    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
		]
		snake_case__						:     Dict		=downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

	snake_case__						:     Tuple		=downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
	snake_case__						:     Any		=downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
	snake_case__						:     Dict		=downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
	snake_case__						:     str		=downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
	snake_case__						:     List[str]		=downstream_dict['''objective.W''']
	return model







@torch.no_grad()
def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		str						,						SCREAMING_SNAKE_CASE						:		Any						,						SCREAMING_SNAKE_CASE						:		Dict						,						SCREAMING_SNAKE_CASE						:		Optional[Any] ):






	"""simple docstring"""
	snake_case__						:     int		=torch.load(UpperCamelCase__						,						map_location='''cpu''' )

	snake_case__						:     Optional[Any]		=checkpoint['''Downstream''']

	snake_case__						:     List[str]		=UniSpeechSatConfig.from_pretrained(UpperCamelCase__ )
	snake_case__						:     int		=WavaVecaFeatureExtractor.from_pretrained(
	    UpperCamelCase__						,						return_attention_mask=UpperCamelCase__						,						do_normalize=UpperCamelCase__ )

	snake_case__						:     Tuple		=hf_config.architectures[0]
	if arch.endswith('''ForSequenceClassification''' ):
		snake_case__						:     List[str]		=convert_classification(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__ )
	elif arch.endswith('''ForAudioFrameClassification''' ):
		snake_case__						:     Optional[int]		=convert_diarization(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__ )
	elif arch.endswith('''ForXVector''' ):
		snake_case__						:     str		=convert_xvector(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__ )
	else:
		raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )

	if hf_config.use_weighted_layer_sum:
		snake_case__						:     Any		=checkpoint['''Featurizer''']['''weights''']

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


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











from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "AutoImageProcessor"
							lowerCamelCase_							     =       "AutoTokenizer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]				,					__A     :Dict		) -> Dict:



									"""simple docstring"""



									super().__init__(__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.image_processor


							def __call__(				self     :int				,					__A     :str=None				,					__A     :int=None				,					__A     :Union[str, Any]=None				,					**__A     :str		) -> Optional[Any]:



									"""simple docstring"""



									if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none."""		)

									if text is not None:
											SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					return_tensors=__A				,					**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :List[str]		) -> List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :List[str]				,					*__A     :Any				,					**__A     :Any		) -> Tuple:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									return ["input_ids", "attention_mask", "pixel_values"]