






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					['''image_processor''', '''tokenizer''']
	lowerCAmelCase						=					'''FlavaImageProcessor'''
	lowerCAmelCase						=					('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(			self			,     a=None			,     a=None			,     **a   )			->       Dict:
								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			,     )
															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 need to specify an `image_processor`.'   )
								if tokenizer is None:
															raise ValueError('You need to specify a `tokenizer`.'   )

								super().__init__(a			,     a   )
								snake_case_           =				self.image_processor




	def __call__(			self			,     a = None			,     a = None			,     a = True			,     a = False			,     a = False			,     a = None			,     a = 0			,     a = None			,     a = None			,     a = None			,     a = None			,     a = None			,     a = False			,     a = False			,     a = False			,     a = False			,     a = True			,     a = None			,     **a			,     )			->       Optional[int]:

								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(
															    text=a			,     add_special_tokens=a			,     padding=a			,     truncation=a			,     max_length=a			,     stride=a			,     pad_to_multiple_of=a			,     return_token_type_ids=a			,     return_attention_mask=a			,     return_overflowing_tokens=a			,     return_special_tokens_mask=a			,     return_offsets_mapping=a			,     return_length=a			,     verbose=a			,     return_tensors=a			,     **a			,     )
								if images is not None:
															snake_case_           =				self.image_processor(
															    a			,     return_image_mask=a			,     return_codebook_pixels=a			,     return_tensors=a			,     **a			,     )

								if text is not None and images is not None:
															encoding.update(a   )
															return encoding
								elif text is not None:
															return encoding
								else:
															return BatchEncoding(data=dict(**a   )			,     tensor_type=a   )




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       str:
								return self.tokenizer.batch_decode(*a			,     **a   )




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       Any:
								return self.tokenizer.decode(*a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict:
								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 				_UpperCamelCase     (			self   )			->       Any:
								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 				_UpperCamelCase     (			self   )			->       List[str]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,     a			,     )
								return self.image_processor













import unittest

import numpy as np





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = None				,	):
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)

							if shape_a[0] != shape_b[0]:
														snake_case_           =				(
														    '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]:
														snake_case_           =				(
														    '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_)

							snake_case_           =				pseudo_inv
							if a_inv is None:
														try:
																					snake_case_           =				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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								snake_case_           =				schur_complement(a			,     a			,     a   )

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

								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )

								self.assertAlmostEqual(a			,     det_a * det_s   )




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

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






	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1, 3], [6, 3, 5]]   )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()





