


import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    # Initialise PyTorch model
    _lowercase			:				Dict 						=						RemBertConfig.from_json_file(lowerCamelCase_					)
    print('Building PyTorch model from configuration: {}'.format(str(lowerCamelCase_					)					)					)
    _lowercase			:				Optional[int] 						=						RemBertModel(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print('Save PyTorch model to {}'.format(lowerCamelCase_					)					)
    torch.save(model.state_dict()				,					lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: List[Any]				   =					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(
     "--rembert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained RemBERT 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."
 )
 SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







'''simple docstring'''





import warnings

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



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[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:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





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







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



