


"""simple docstring"""
import warnings

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





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      ["""image_processor""", """tokenizer"""]
							__lowercase:						int							 =      """CLIPImageProcessor"""
							__lowercase:						Optional[Any]							 =      ("""CLIPTokenizer""", """CLIPTokenizerFast""")



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



														"""simple docstring"""



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

														snake_case_          =						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__(_lowerCAmelCase						,    _lowerCAmelCase    )



							def __call__(			self							: List[Any]						,    UpperCAmelCase_							: Optional[Any]=None						,    UpperCAmelCase_							: Dict=None						,    UpperCAmelCase_							: str=None						,    **UpperCAmelCase_							: Union[str, Any]    )   ->Optional[Any]:



														"""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:
																					snake_case_          =						self.tokenizer(_lowerCAmelCase						,    return_tensors=_lowerCAmelCase						,    **_lowerCAmelCase    )

														if images is not None:
																					snake_case_          =						self.image_processor(_lowerCAmelCase						,    return_tensors=_lowerCAmelCase						,    **_lowerCAmelCase    )

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



							def 						lowerCAmelCase    (			self							: Tuple						,    *UpperCAmelCase_							: str						,    **UpperCAmelCase_							: int    )   ->Optional[int]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: List[str]						,    *UpperCAmelCase_							: Dict						,    **UpperCAmelCase_							: Union[str, Any]    )   ->List[str]:



														"""simple docstring"""



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



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



														"""simple docstring"""



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



							@property
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->List[Any]:



														"""simple docstring"""



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



							@property
							def 						lowerCAmelCase    (			self							: Optional[Any]    )   ->Union[str, Any]:



														"""simple docstring"""



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








"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "\n".join(
							    f"""{number} * {i} = {number * i}""" for i in range(1			,	number_of_terms + 1			)			)


if __name__ == "__main__":
				print(multiplication_table(number=5, number_of_terms=10))



