





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   A      (_UpperCAmelCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   ['image_processor', 'tokenizer']
			__lowerCamelCase     :	Union[str, Any]          =   'AutoImageProcessor'
			__lowerCamelCase     :	Dict          =   'AutoTokenizer'






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







					"""simple docstring"""






					A__  				=       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_							,     )
							A__  				=       kwargs.pop("""feature_extractor"""       )

					A__  				=       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_       )
					A__  				=       self.image_processor
					A__  				=       False






			def __call__(      self       :   Dict							,     *__lowerCAmelCase       :   List[str]							,     **__lowerCAmelCase       :   Optional[Any]       )     ->     Tuple:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*SCREAMING_SNAKE_CASE_							,     **SCREAMING_SNAKE_CASE_       )

					A__  				=       kwargs.pop("""images"""							,     SCREAMING_SNAKE_CASE_       )
					A__  				=       kwargs.pop("""text"""							,     SCREAMING_SNAKE_CASE_       )
					if len(SCREAMING_SNAKE_CASE_       ) > 0:
							A__  				=       args[0]
							A__  				=       args[1:]

					if images is None and text is None:
							raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

					if images is not None:
							A__  				=       self.image_processor(SCREAMING_SNAKE_CASE_							,     *SCREAMING_SNAKE_CASE_							,     **SCREAMING_SNAKE_CASE_       )
					if text is not None:
							A__  				=       self.tokenizer(SCREAMING_SNAKE_CASE_							,     **SCREAMING_SNAKE_CASE_       )

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							A__  				=       encodings["""input_ids"""]
							return inputs






			def        a_	(      self       :   Optional[Any]							,     *__lowerCAmelCase       :   Tuple							,     **__lowerCAmelCase       :   Dict       )     ->     Dict:







					"""simple docstring"""






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






			def        a_	(      self       :   Union[str, Any]							,     *__lowerCAmelCase       :   Tuple							,     **__lowerCAmelCase       :   Optional[Any]       )     ->     List[Any]:







					"""simple docstring"""






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






			@contextmanager
			def        a_	(      self       :   List[str]       )     ->     Any:







					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your images inputs, or in a separate call."""       )
					A__  				=       True
					A__  				=       self.tokenizer
					yield
					A__  				=       self.image_processor
					A__  				=       False






			def        a_	(      self       :   Optional[int]							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   Optional[int]=False							,     __lowerCAmelCase       :   Union[str, Any]=None       )     ->     int:







					"""simple docstring"""






					if added_vocab is None:
							A__  				=       self.tokenizer.get_added_vocab()

					A__  				=       {}

					while tokens:
							A__  				=       re.search(R"""<s_(.*?)>"""							,     SCREAMING_SNAKE_CASE_							,     re.IGNORECASE       )
							if start_token is None:
									break
							A__  				=       start_token.group(1       )
							A__  				=       re.search(Rf'</s_{key}>'							,     SCREAMING_SNAKE_CASE_							,     re.IGNORECASE       )
							A__  				=       start_token.group()
							if end_token is None:
									A__  				=       tokens.replace(SCREAMING_SNAKE_CASE_							,     """"""       )
							else:
									A__  				=       end_token.group()
									A__  				=       re.escape(SCREAMING_SNAKE_CASE_       )
									A__  				=       re.escape(SCREAMING_SNAKE_CASE_       )
									A__  				=       re.search(f'{start_token_escaped}(.*?){end_token_escaped}'							,     SCREAMING_SNAKE_CASE_							,     re.IGNORECASE       )
									if content is not None:
											A__  				=       content.group(1       ).strip()
											if r"<s_" in content and r"</s_" in content:  # non-leaf node
													A__  				=       self.tokenajson(SCREAMING_SNAKE_CASE_							,     is_inner_value=SCREAMING_SNAKE_CASE_							,     added_vocab=SCREAMING_SNAKE_CASE_       )
													if value:
															if len(SCREAMING_SNAKE_CASE_       ) == 1:
																	A__  				=       value[0]
															A__  				=       value
											else:  # leaf nodes
													A__  				=       []
													for leaf in content.split(R"""<sep/>"""       ):
															A__  				=       leaf.strip()
															if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																	A__  				=       leaf[1:-2]  # for categorical special tokens
															output[key].append(SCREAMING_SNAKE_CASE_       )
													if len(output[key]       ) == 1:
															A__  				=       output[key][0]

									A__  				=       tokens[tokens.find(SCREAMING_SNAKE_CASE_       ) + len(SCREAMING_SNAKE_CASE_       ) :].strip()
									if tokens[:6] == r"<sep/>":  # non-leaf nodes
											return [output] + self.tokenajson(tokens[6:]							,     is_inner_value=SCREAMING_SNAKE_CASE_							,     added_vocab=SCREAMING_SNAKE_CASE_       )

					if len(SCREAMING_SNAKE_CASE_       ):
							return [output] if is_inner_value else output
					else:
							return [] if is_inner_value else {"text_sequence": tokens}






			@property
			def        a_	(      self       :   Any       )     ->     int:







					"""simple docstring"""






					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        a_	(      self       :   List[Any]       )     ->     Optional[int]:







					"""simple docstring"""






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










import unittest

import numpy as np







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """Expected the same number of rows for A and B. """
				    F'Instead found A of size {shape_a} and B of size {shape_b}'
				)
				raise ValueError(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """Expected the same number of columns for B and C. """
				    F'Instead found B of size {shape_b} and C of size {shape_c}'
				)
				raise ValueError(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				except np.linalg.LinAlgError:
						raise ValueError(
						    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

		return mat_c - mat_b.T @ a_inv @ mat_b


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       np.block([[a, b], [b.T, c]]       )

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1, 3], [6, 3, 5]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()



