


'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    ['image_processor', 'tokenizer']
		_a   			=    'CLIPImageProcessor'
		_a   			=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')




		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :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.'''   ,       UpperCamelCase_   ,       )
												UpperCamelCase__       :Tuple							 =			kwargs.pop('''feature_extractor'''							)

							UpperCamelCase__       :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__(UpperCamelCase_   ,       UpperCamelCase_							)




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

							'''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__       :str							 =			self.tokenizer(UpperCamelCase_   ,       return_tensors=UpperCamelCase_   ,       **UpperCamelCase_							)

							if images is not None:
												UpperCamelCase__       :Union[str, Any]							 =			self.image_processor(UpperCamelCase_   ,       return_tensors=UpperCamelCase_   ,       **UpperCamelCase_							)

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




		def    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


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




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.tokenizer.model_input_names
							UpperCamelCase__       :Tuple							 =			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



'''simple docstring'''
from argparse import ArgumentParser

from .env import EnvironmentCommand


def       a				(      )  ->     Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			ArgumentParser('''Diffusers CLI tool'''			, usage='''diffusers-cli <command> [<args>]'''       )
					UpperCamelCase__       :Union[str, Any]							 =			parser.add_subparsers(help='''diffusers-cli command helpers'''       )

					# Register commands
					EnvironmentCommand.register_subcommand(__a       )

					# Let's go
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										parser.print_help()
										exit(1       )

					# Run
					UpperCamelCase__       :Optional[int]							 =			args.func(__a       )
					service.run()


if __name__ == "__main__":
		main()