




'''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):
       __lowercase  :      Union[str, Any]        =			['''image_processor''', '''tokenizer''']
       __lowercase  :      Any        =			'''OneFormerImageProcessor'''
       __lowercase  :      int        =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE = 77    ,  __SCREAMING_SNAKE_CASE = 77    ,  **__SCREAMING_SNAKE_CASE  )      ->  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`.'''  )

            __snake_case    					=  max_seq_length
            __snake_case    					=  task_seq_length

            super().__init__(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=77  )      ->  Union[str, Any]:



            '''simple docstring'''




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

            __snake_case    					=  self.tokenizer(__SCREAMING_SNAKE_CASE    ,  padding='''max_length'''    ,  max_length=__SCREAMING_SNAKE_CASE    ,  truncation=__SCREAMING_SNAKE_CASE  )

            __snake_case					,    __snake_case    					=  tokens['''attention_mask'''], tokens['''input_ids''']

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

            __snake_case    					=  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  )      ->  List[Any]:



            '''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.'''  )

            __snake_case    					=  self.image_processor(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )

            if isinstance(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  ):
                 __snake_case    					=  [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  ):
                 __snake_case    					=  []
                 for task in task_inputs:
                      __snake_case    					=  F'''the task is {task}'''
                      task_token_inputs.append(__SCREAMING_SNAKE_CASE  )
                 __snake_case    					=  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'''  ):
                 __snake_case    					=  encoded_inputs.text_inputs

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

                 __snake_case    					=  torch.cat(__SCREAMING_SNAKE_CASE    ,  dim=0  )

            return encoded_inputs


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  **__SCREAMING_SNAKE_CASE  )      ->  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.'''  )

            __snake_case    					=  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  ):
                 __snake_case    					=  [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  ):
                 __snake_case    					=  []
                 for task in task_inputs:
                      __snake_case    					=  F'''the task is {task}'''
                      task_token_inputs.append(__SCREAMING_SNAKE_CASE  )
                 __snake_case    					=  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'''  ):
                 __snake_case    					=  encoded_inputs.text_inputs

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

                 __snake_case    					=  torch.cat(__SCREAMING_SNAKE_CASE    ,  dim=0  )

            return encoded_inputs


       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




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




       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  int:



            '''simple docstring'''




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












'''simple docstring'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






