
'''simple docstring'''







import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				NDArray[floataa]   ,   __SCREAMING_SNAKE_CASE      :				NDArray[floataa]							):



						"""simple docstring"""





						lowercase_		,				lowercase_			:  Any									= np.shape(__SCREAMING_SNAKE_CASE							)

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

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

						return x
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				NDArray[floataa]   ,   __SCREAMING_SNAKE_CASE      :				NDArray[floataa]							):



						"""simple docstring"""





						lowercase_		,				lowercase_			:  List[Any]									= np.shape(__SCREAMING_SNAKE_CASE							)
						if rows != columns:
												return np.array(()   ,   dtype=__SCREAMING_SNAKE_CASE							)

						# augmented matrix
						lowercase_			:  NDArray[floataa]									= np.concatenate((coefficients, vector)   ,   axis=1							)
						lowercase_			:  Tuple									= augmented_mat.astype('''float64'''							)

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

						lowercase_			:  Dict									= retroactive_resolution(
						    augmented_mat[:, 0:columns]   ,   augmented_mat[:, columns : columns + 1]							)

						return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											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`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



