


"""simple docstring"""






import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 			A__		(					_UpperCAmelCase    :      NDArray[floataa]				,    _UpperCAmelCase    :      NDArray[floataa]	)							->	NDArray[floataa]:

	'''simple docstring'''


	snake_case__,					snake_case__  :						Any		=      np.shape(_UpperCAmelCase	)

	snake_case__  :						NDArray[floataa]		=      np.zeros((rows, 1)				,    dtype=_UpperCAmelCase	)
	for row in reversed(range(_UpperCAmelCase	)	):
		snake_case__  :						Dict		=      0
		for col in range(row + 1				,    _UpperCAmelCase	):
			total += coefficients[row, col] * x[col]

		snake_case__  :						List[str]		=      (vector[row] - total) / coefficients[row, row]

	return x



def 			A__		(					_UpperCAmelCase    :      NDArray[floataa]				,    _UpperCAmelCase    :      NDArray[floataa]	)							->	NDArray[floataa]:

	'''simple docstring'''


	snake_case__,					snake_case__  :						str		=      np.shape(_UpperCAmelCase	)
	if rows != columns:
		return np.array(()				,    dtype=_UpperCAmelCase	)

	# augmented matrix
	snake_case__  :						NDArray[floataa]		=      np.concatenate((coefficients, vector)				,    axis=1	)
	snake_case__  :						List[Any]		=      augmented_mat.astype("float64"	)

	# scale the matrix leaving it triangular
	for row in range(rows - 1	):
		snake_case__  :						Any		=      augmented_mat[row, row]
		for col in range(row + 1				,    _UpperCAmelCase	):
			snake_case__  :						Optional[Any]		=      augmented_mat[col, row] / pivot
			augmented_mat[col, :] -= factor * augmented_mat[row, :]

	snake_case__  :						List[str]		=      retroactive_resolution(
	    augmented_mat[:, 0:columns]				,    augmented_mat[:, columns : columns + 1]	)

	return x


if __name__ == "__main__":
					import doctest

					doctest.testmod()









"""simple docstring"""






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


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''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__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

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




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





