


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 		__a				(    UpperCAmelCase      ):
		_a     :  str           =     ['image_processor', 'tokenizer']
		_a     :  Union[str, Any]           =     'BlipImageProcessor'
		_a     :  str           =     ('BertTokenizer', 'BertTokenizerFast')
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      False
									super().__init__(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.image_processor
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 0      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	BatchEncoding:




									"""simple docstring"""





									if images is None and text is None:
																raise ValueError('You have to specify either images or text.'						)

									# Get only text
									if images is None:
																_UpperCAmelCase					=      self.tokenizer
																_UpperCAmelCase					=      self.tokenizer(
																    text=_SCREAMING_SNAKE_CASE      ,   add_special_tokens=_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   truncation=_SCREAMING_SNAKE_CASE      ,   max_length=_SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,   return_attention_mask=_SCREAMING_SNAKE_CASE      ,   return_overflowing_tokens=_SCREAMING_SNAKE_CASE      ,   return_special_tokens_mask=_SCREAMING_SNAKE_CASE      ,   return_offsets_mapping=_SCREAMING_SNAKE_CASE      ,   return_token_type_ids=_SCREAMING_SNAKE_CASE      ,   return_length=_SCREAMING_SNAKE_CASE      ,   verbose=_SCREAMING_SNAKE_CASE      ,   return_tensors=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
																return text_encoding

									# add pixel_values
									_UpperCAmelCase					=      self.image_processor(_SCREAMING_SNAKE_CASE      ,   return_tensors=_SCREAMING_SNAKE_CASE						)

									if text is not None:
																_UpperCAmelCase					=      self.tokenizer(
																    text=_SCREAMING_SNAKE_CASE      ,   add_special_tokens=_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   truncation=_SCREAMING_SNAKE_CASE      ,   max_length=_SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,   return_attention_mask=_SCREAMING_SNAKE_CASE      ,   return_overflowing_tokens=_SCREAMING_SNAKE_CASE      ,   return_special_tokens_mask=_SCREAMING_SNAKE_CASE      ,   return_offsets_mapping=_SCREAMING_SNAKE_CASE      ,   return_token_type_ids=_SCREAMING_SNAKE_CASE      ,   return_length=_SCREAMING_SNAKE_CASE      ,   verbose=_SCREAMING_SNAKE_CASE      ,   return_tensors=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
									else:
																_UpperCAmelCase					=      None

									if text_encoding is not None:
																encoding_image_processor.update(_SCREAMING_SNAKE_CASE						)

									return encoding_image_processor
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





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






		@property
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.tokenizer.model_input_names
									_UpperCAmelCase					=      self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




from __future__ import annotations

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

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
