


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__      :  Tuple						      =							"""AutoImageProcessor"""
      lowerCAmelCase__      :  Optional[int]						      =							"""AutoTokenizer"""



      def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Dict=None     ,		UpperCamelCase					:					Any=None     ,		**UpperCamelCase					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''     ,		UpperCamelCase     ,		)
                    lowercase__       =				kwargs.pop('''feature_extractor'''				)

             lowercase__       =				image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('''You have to specify an image_processor.'''				)
             if tokenizer is None:
                    raise ValueError('''You have to specify a tokenizer.'''				)

             super().__init__(UpperCamelCase     ,		UpperCamelCase				)
             lowercase__       =				self.image_processor



      def __call__(self					:					Dict     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[Any]=None     ,		UpperCamelCase					:					int=None     ,		**UpperCamelCase					:					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:
                    lowercase__       =				self.tokenizer(UpperCamelCase     ,		return_tensors=UpperCamelCase     ,		**UpperCamelCase				)

             if images is not None:
                    lowercase__       =				self.image_processor(UpperCamelCase     ,		return_tensors=UpperCamelCase     ,		**UpperCamelCase				)

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



      def    UpperCamelCase__	(self					:					Optional[Any]     ,		*UpperCamelCase					:					Optional[Any]     ,		**UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







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



      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		*UpperCamelCase					:					Optional[Any]     ,		**UpperCamelCase					:					List[str]				):

             '''simple docstring'''







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



      @property
      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







             lowercase__       =				self.tokenizer.model_input_names
             lowercase__       =				self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)



      @property
      def    UpperCamelCase__	(self					:					Union[str, Any]				):

             '''simple docstring'''







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






      @property
      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







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








'''simple docstring'''

import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A ) -> Dict:






       """simple docstring"""







       lowercase__       =				ElectraConfig.from_json_file(A )
       print(f"Building PyTorch model from configuration: {config}" )

       if discriminator_or_generator == "discriminator":
              lowercase__       =				ElectraForPreTraining(A )
       elif discriminator_or_generator == "generator":
              lowercase__       =				ElectraForMaskedLM(A )
       else:
              raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

       # Load weights from tf checkpoint
       load_tf_weights_in_electra(
           A						,				A						,				A						,				discriminator_or_generator=A )

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


if __name__ == "__main__":
 lowerCamelCase							:     Tuple								=			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(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--discriminator_or_generator',
     default=None,
     type=str,
     required=True,
     help=(
         'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
         '\'generator\'.'
     ),
 )
 lowerCamelCase							:     Optional[int]								=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )




