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

class        _UpperCamelCase				(					__snake_case  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = ["""image_processor""", """tokenizer"""]
			__UpperCamelCase      :						Any					      = """AutoImageProcessor"""
			__UpperCamelCase      :						Any					      = """AutoTokenizer"""

			def __init__(       self :       Tuple						,	snake_case_ :       str						,	snake_case_ :       List[str]      ):
							super().__init__(__UpperCamelCase						,	__UpperCamelCase      )
							UpperCamelCase_:							str	=						self.image_processor

			def __call__(       self :       str						,	snake_case_ :       int=None						,	snake_case_ :       Any=None						,	snake_case_ :       int=None						,	**snake_case_ :       Optional[int]      ):


							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_:							List[str]	=						self.tokenizer(__UpperCamelCase						,	return_tensors=__UpperCamelCase						,	**__UpperCamelCase      )

							if images is not None:
											UpperCamelCase_:							List[str]	=						self.image_processor(__UpperCamelCase						,	return_tensors=__UpperCamelCase						,	**__UpperCamelCase      )

							if text is not None and images is not None:
											UpperCamelCase_:							Dict	=						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 :       List[str]						,	*snake_case_ :       List[str]						,	**snake_case_ :       Optional[int]      ):
							return self.tokenizer.batch_decode(*__UpperCamelCase						,	**__UpperCamelCase      )

			def       lowerCAmelCase__	(       self :       Dict						,	*snake_case_ :       Any						,	**snake_case_ :       Any      ):
							return self.tokenizer.decode(*__UpperCamelCase						,	**__UpperCamelCase      )




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








lowerCamelCase_						: Optional[Any]         =  """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase_						: Union[str, Any]         =  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase_						: Optional[Any]         =  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






