



"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= '''ClapFeatureExtractor'''
	_SCREAMING_SNAKE_CASE	:		str  						= ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	Union[str, Any]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def __call__(      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     kwargs.pop('sampling_rate'     ,       _SCREAMING_SNAKE_CASE    )

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

					if text is not None:
									SCREAMING_SNAKE_CASE						:							Any							=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					if audios is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     self.feature_extractor(
									    _SCREAMING_SNAKE_CASE     ,       sampling_rate=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					if text is not None and audios is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     audio_features.input_features
									return encoding
					elif text is not None:
									return encoding
					else:
									return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE    )     ,       tensor_type=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       *_SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	int:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       int     ,       *_SCREAMING_SNAKE_CASE				:       Any     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	Any:





					"""simple docstring"""


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







	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.tokenizer.model_input_names
					SCREAMING_SNAKE_CASE						:							int							=     self.feature_extractor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )










"""simple docstring"""

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__A							,		__A							,		__A     )

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
				torch.save(model.state_dict()							,		__A     )


if __name__ == "__main__":
							A_						:    int				=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--bert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained BERT model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							A_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





