



'''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__     (     UpperCAmelCase_				):






	'''simple docstring'''

	A__		=	['''image_processor''', '''tokenizer''']
	A__		=	'''OneFormerImageProcessor'''
	A__		=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')




	def __init__(  self		:  List[str]   ,		__A		:  List[str]=None   ,		__A		:  int=None   ,		__A		:  Dict = 77   ,		__A		:  Tuple = 77   ,		**__A		:  str					)     ->			Optional[Any]:




								'''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`."""					)

								lowerCAmelCase__  =   max_seq_length
								lowerCAmelCase__  =   task_seq_length

								super().__init__(__UpperCamelCase   ,		__UpperCamelCase					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  Any=None   ,		__A		:  Dict=77					)     ->			Optional[int]:




								'''simple docstring'''





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

								lowerCAmelCase__  =   self.tokenizer(__UpperCamelCase   ,		padding="""max_length"""   ,		max_length=__UpperCamelCase   ,		truncation=__UpperCamelCase					)

								lowerCAmelCase__  ,lowerCAmelCase__  =   tokens["""attention_mask"""], tokens["""input_ids"""]

								lowerCAmelCase__  =   []
								for attn_mask, input_id in zip(__UpperCamelCase   ,		__UpperCamelCase					):
															lowerCAmelCase__  =   torch.tensor(__UpperCamelCase					) * torch.tensor(__UpperCamelCase					)
															token_inputs.append(token.unsqueeze(0					)					)

								lowerCAmelCase__  =   torch.cat(__UpperCamelCase   ,		dim=0					)
								return token_inputs




	def __call__(  self		:  Dict   ,		__A		:  Optional[int]=None   ,		__A		:  Union[str, Any]=None   ,		__A		:  Union[str, Any]=None   ,		**__A		:  str					)     ->			int:




								'''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."""					)

								lowerCAmelCase__  =   self.image_processor(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		**__UpperCamelCase					)

								if isinstance(__UpperCamelCase   ,		__UpperCamelCase					):
															lowerCAmelCase__  =   [task_inputs]

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

								if hasattr(__UpperCamelCase   ,		"""text_inputs"""					):
															lowerCAmelCase__  =   encoded_inputs.text_inputs

															lowerCAmelCase__  =   []
															for texts in texts_list:
																						lowerCAmelCase__  =   self._preprocess_text(__UpperCamelCase   ,		max_length=self.max_seq_length					)
																						text_inputs.append(text_input_list.unsqueeze(0					)					)

															lowerCAmelCase__  =   torch.cat(__UpperCamelCase   ,		dim=0					)

								return encoded_inputs




	def      lowercase__							(  self		:  Dict   ,		__A		:  Dict=None   ,		__A		:  Any=None   ,		__A		:  List[str]=None   ,		**__A		:  Union[str, Any]					)     ->			str:




								'''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."""					)

								lowerCAmelCase__  =   self.image_processor.encode_inputs(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		**__UpperCamelCase					)

								if isinstance(__UpperCamelCase   ,		__UpperCamelCase					):
															lowerCAmelCase__  =   [task_inputs]

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

								if hasattr(__UpperCamelCase   ,		"""text_inputs"""					):
															lowerCAmelCase__  =   encoded_inputs.text_inputs

															lowerCAmelCase__  =   []
															for texts in texts_list:
																						lowerCAmelCase__  =   self._preprocess_text(__UpperCamelCase   ,		max_length=self.max_seq_length					)
																						text_inputs.append(text_input_list.unsqueeze(0					)					)

															lowerCAmelCase__  =   torch.cat(__UpperCamelCase   ,		dim=0					)

								return encoded_inputs




	def      lowercase__							(  self		:  Any   ,		*__A		:  Tuple   ,		**__A		:  List[Any]					)     ->			str:




								'''simple docstring'''





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




	def      lowercase__							(  self		:  str   ,		*__A		:  Tuple   ,		**__A		:  Union[str, Any]					)     ->			List[Any]:




								'''simple docstring'''





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




	def      lowercase__							(  self		:  Optional[int]   ,		*__A		:  Tuple   ,		**__A		:  List[str]					)     ->			List[str]:




								'''simple docstring'''





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











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





