




import warnings

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


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    =["""image_processor""", """tokenizer"""]
				__a				:			int					    ="""VideoMAEImageProcessor"""
				__a				:			str					    =("""CLIPTokenizer""", """CLIPTokenizerFast""")



				def __init__(			self							,			UpperCAmelCase_=None							,			UpperCAmelCase_=None							,			**UpperCAmelCase_    ):
											lowerCAmelCase =		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_							,			)
																		lowerCAmelCase =		kwargs.pop('''feature_extractor'''    )

											lowerCAmelCase =		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__(UpperCAmelCase_							,			UpperCAmelCase_    )
											lowerCAmelCase =		self.image_processor



				def __call__(			self							,			UpperCAmelCase_=None							,			UpperCAmelCase_=None							,			UpperCAmelCase_=None							,			**UpperCAmelCase_    ):

											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:
																		lowerCAmelCase =		self.tokenizer(UpperCAmelCase_							,			return_tensors=UpperCAmelCase_							,			**UpperCAmelCase_    )

											if videos is not None:
																		lowerCAmelCase =		self.image_processor(UpperCAmelCase_							,			return_tensors=UpperCAmelCase_							,			**UpperCAmelCase_    )

											if text is not None and videos is not None:
																		lowerCAmelCase =		image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**UpperCAmelCase_    )							,			tensor_type=UpperCAmelCase_    )



				def    __snake_case		(			self							,			*UpperCAmelCase_							,			**UpperCAmelCase_    ):
											return self.tokenizer.batch_decode(*UpperCAmelCase_							,			**UpperCAmelCase_    )



				def    __snake_case		(			self							,			*UpperCAmelCase_							,			**UpperCAmelCase_    ):
											return self.tokenizer.decode(*UpperCAmelCase_							,			**UpperCAmelCase_    )



				@property
				def    __snake_case		(			self    ):
											return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



				@property
				def    __snake_case		(			self    ):
											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    __snake_case		(			self    ):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,			UpperCAmelCase_							,			)
											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    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

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

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

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

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


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



