
import warnings

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['image_processor', 'tokenizer']
							_snake_case  		=	'AutoImageProcessor'
							_snake_case  		=	'AutoTokenizer'



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_      )-> Union[str, Any]:





													'''simple docstring'''






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

													__UpperCamelCase  	=      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__(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.image_processor



							def __call__(	self  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''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:
																			__UpperCamelCase  	=      self.tokenizer(SCREAMING_SNAKE_CASE_  ,						return_tensors=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )

													if images is not None:
																			__UpperCamelCase  	=      self.image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )

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



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[Any]:





													'''simple docstring'''






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



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






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



							@property
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






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



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





													'''simple docstring'''






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



							@property
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






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





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)