
"""simple docstring"""






import warnings

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




class 			_SCREAMING_SNAKE_CASE					(			UpperCAmelCase__							):
			UpperCAmelCase_  :Optional[int]				 =						["""image_processor""", """tokenizer"""]
			UpperCAmelCase_  :Dict				 =						"""CLIPImageProcessor"""
			UpperCAmelCase_  :Any				 =						("""CLIPTokenizer""", """CLIPTokenizerFast""")






			def __init__(					self       ,   __A=None       ,   __A=None       ,   **__A       )			->     List[Any]:
						lowerCAmelCase_    :List[str]			     =  None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,   SCREAMING_SNAKE_CASE__       ,   )
									lowerCAmelCase_    :Tuple			     =  kwargs.pop("""feature_extractor"""       )

						lowerCAmelCase_    :Optional[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__(SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__       )






			def __call__(					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   **__A       )			->     Union[str, Any]:

						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:
									lowerCAmelCase_    :List[str]			     =  self.tokenizer(SCREAMING_SNAKE_CASE__       ,   return_tensors=SCREAMING_SNAKE_CASE__       ,   **SCREAMING_SNAKE_CASE__       )

						if images is not None:
									lowerCAmelCase_    :Optional[Any]			     =  self.image_processor(SCREAMING_SNAKE_CASE__       ,   return_tensors=SCREAMING_SNAKE_CASE__       ,   **SCREAMING_SNAKE_CASE__       )

						if text is not None and images is not None:
									lowerCAmelCase_    :List[str]			     =  image_features.pixel_values
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__       )       ,   tensor_type=SCREAMING_SNAKE_CASE__       )






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






			def  __lowerCAmelCase (					self       ,   *__A       ,   **__A       )			->     str:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,   **SCREAMING_SNAKE_CASE__       )






			@property
			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :List[str]			     =  self.tokenizer.model_input_names
						lowerCAmelCase_    :Optional[Any]			     =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






			@property
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,   SCREAMING_SNAKE_CASE__       ,   )
						return self.image_processor_class






			@property
			def  __lowerCAmelCase (					self       )			->     List[Any]:
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,   SCREAMING_SNAKE_CASE__       ,   )
						return self.image_processor







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       list[int]						,		snake_case_       :       list[int]      )					->     tuple[float, float]:






 '''simple docstring'''

 if not len(snake_case_      ) == len(snake_case_      ) == 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
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa

 # Calculate the determinants of the matrices
 __lowerCAmelCase											=						aa * ba - aa * ba
 __lowerCAmelCase											=						ca * ba - ca * ba
 __lowerCAmelCase											=						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:
   __lowerCAmelCase											=						determinant_x / determinant
   __lowerCAmelCase											=						determinant_y / determinant
   # Non-Trivial Solution (Consistent system)
   return (x, y)






