
'''simple docstring'''
import warnings

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







class 			__A					(			_snake_case      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''VideoMAEImageProcessor'''
 lowerCamelCase												=('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__( self      :     Dict					, lowerCamelCase      :     Optional[int]=None					, lowerCamelCase      :     Optional[Any]=None					, **lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A		:  Optional[int]       				=		None
   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""					, lowercase__					, )
     __A		:  Union[str, Any]       				=		kwargs.pop("""feature_extractor"""							)

   __A		:  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__(lowercase__					, lowercase__							)
   __A		:  int       				=		self.image_processor
 def __call__( self      :     Any					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     Union[str, Any]=None					, lowerCamelCase      :     Tuple=None					, **lowerCamelCase      :     Any							):



   """simple docstring"""





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

   if text is not None:
     __A		:  Union[str, Any]       				=		self.tokenizer(lowercase__					, return_tensors=lowercase__					, **lowercase__							)

   if videos is not None:
     __A		:  List[str]       				=		self.image_processor(lowercase__					, return_tensors=lowercase__					, **lowercase__							)

   if text is not None and videos is not None:
     __A		:  int       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase__							)					, tensor_type=lowercase__							)
 def 		lowercase_( self      :     Tuple					, *lowerCamelCase      :     Optional[int]					, **lowerCamelCase      :     Optional[int]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowercase__					, **lowercase__							)
 def 		lowercase_( self      :     List[Any]					, *lowerCamelCase      :     str					, **lowerCamelCase      :     Any							):



   """simple docstring"""





   return self.tokenizer.decode(*lowercase__					, **lowercase__							)
 @property
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
 @property
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





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






 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





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








import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

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


if __name__ == "__main__":
		__magic_name__       =     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.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






