






import warnings

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

class 		_snake_case	(	A__       ):
			_lowercase		:     Tuple        =				['''image_processor''', '''tokenizer''']
			_lowercase		:     List[Any]        =				'''ViTImageProcessor'''
			_lowercase		:     Optional[Any]        =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')






			def __init__(							self		,       a=None		,       a=None		,       **a)   ->							Optional[Any]:
						SCREAMING_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.'		,       a		,       )
									SCREAMING_SNAKE_CASE							=			kwargs.pop('feature_extractor')

						SCREAMING_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__(a		,       a)






			def __call__(							self		,       a=None		,       a=None		,       a=None		,       a=None		,       **a)   ->							int:
						if text is None and visual_prompt is None and images is None:
									raise ValueError('You have to specify either text, visual prompt or images.')

						if text is not None and visual_prompt is not None:
									raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.')

						if text is not None:
									SCREAMING_SNAKE_CASE							=			self.tokenizer(a		,       return_tensors=a		,       **a)

						if visual_prompt is not None:
									SCREAMING_SNAKE_CASE							=			self.image_processor(a		,       return_tensors=a		,       **a)

						if images is not None:
									SCREAMING_SNAKE_CASE							=			self.image_processor(a		,       return_tensors=a		,       **a)

						if visual_prompt is not None and images is not None:
									SCREAMING_SNAKE_CASE							=			{
									    'pixel_values': image_features.pixel_values,
									    'conditional_pixel_values': prompt_features.pixel_values,
									}
									return encoding
						elif text is not None and images is not None:
									SCREAMING_SNAKE_CASE							=			image_features.pixel_values
									return encoding
						elif text is not None:
									return encoding
						elif visual_prompt is not None:
									SCREAMING_SNAKE_CASE							=			{
									    'conditional_pixel_values': prompt_features.pixel_values,
									}
									return encoding
						else:
									return BatchEncoding(data=dict(**a)		,       tensor_type=a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							List[str]:
						return self.tokenizer.batch_decode(*a		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							int:
						return self.tokenizer.decode(*a		,       **a)






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,       a		,       )
						return self.image_processor_class







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,       a		,       )
						return self.image_processor






def 		UpperCamelCase     (       _UpperCAmelCase  :   list[int] ,			_UpperCAmelCase  :   list[int]  )   ->			tuple[float, float]:






						'''simple docstring'''

						if not len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa

						# Calculate the determinants of the matrices
						_lowercase       :	List[Any]           =   aa * ba - aa * ba
						_lowercase       :	Tuple           =   ca * ba - ca * ba
						_lowercase       :	Union[str, Any]           =   aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowercase       :	str           =   determinant_x / determinant
																		_lowercase       :	Union[str, Any]           =   determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)




