




'''simple docstring'''







import warnings

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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = ["""image_processor""", """tokenizer"""]
					lowercase__    = """AutoImageProcessor"""
					lowercase__    = """AutoTokenizer"""






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  **__UpperCamelCase ):
							_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.",  __UpperCamelCase,  )
									_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 have to specify an image_processor." )
							if tokenizer is None:
									raise ValueError("You have to specify a tokenizer." )

							super().__init__(__UpperCamelCase,  __UpperCamelCase )
							_snake_case	       =       self.image_processor






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

							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(__UpperCamelCase,  return_tensors=__UpperCamelCase,  **__UpperCamelCase )

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

							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(**__UpperCamelCase ),  tensor_type=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return self.tokenizer.batch_decode(*__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return self.tokenizer.decode(*__UpperCamelCase,  **__UpperCamelCase )






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_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 				__SCREAMING_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 				__SCREAMING_SNAKE_CASE	(    self ):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",  __UpperCamelCase,  )
							return self.image_processor











'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





