

'''simple docstring'''




from typing import List

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


if is_torch_available():
  import torch



class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: int      =   ["image_processor", "tokenizer"]
  __lowerCAmelCase					: str      =   "OneFormerImageProcessor"
  __lowerCAmelCase					: Tuple      =   ("CLIPTokenizer", "CLIPTokenizerFast")
  def __init__(     self   , a_=None   , a_=None   , a_ = 7_7   , a_ = 7_7   , **a_     )							->				Optional[int]:


    """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`.'     )

    UpperCAmelCase													=	max_seq_length
    UpperCAmelCase													=	task_seq_length

    super().__init__(a_   , a_     )
  def 						snake_case_			(     self   , a_=None   , a_=7_7     )							->				Optional[int]:


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

    UpperCAmelCase													=	self.tokenizer(a_   , padding='max_length'   , max_length=a_   , truncation=a_     )

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

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

    UpperCAmelCase													=	torch.cat(a_   , dim=0     )
    return token_inputs
  def __call__(     self   , a_=None   , a_=None   , a_=None   , **a_     )							->				Dict:


    """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.'     )

    UpperCAmelCase													=	self.image_processor(a_   , a_   , a_   , **a_     )

    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	[task_inputs]

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

    if hasattr(a_   , 'text_inputs'     ):
      UpperCAmelCase													=	encoded_inputs.text_inputs

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

      UpperCAmelCase													=	torch.cat(a_   , dim=0     )

    return encoded_inputs
  def 						snake_case_			(     self   , a_=None   , a_=None   , a_=None   , **a_     )							->				List[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.'     )

    UpperCAmelCase													=	self.image_processor.encode_inputs(a_   , a_   , a_   , **a_     )

    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	[task_inputs]

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

    if hasattr(a_   , 'text_inputs'     ):
      UpperCAmelCase													=	encoded_inputs.text_inputs

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

      UpperCAmelCase													=	torch.cat(a_   , dim=0     )

    return encoded_inputs
  def 						snake_case_			(     self   , *a_   , **a_     )							->				List[str]:


    """simple docstring"""
    return self.image_processor.post_process_semantic_segmentation(*a_   , **a_     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Optional[Any]:


    """simple docstring"""
    return self.image_processor.post_process_instance_segmentation(*a_   , **a_     )





  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


    """simple docstring"""
    return self.image_processor.post_process_panoptic_segmentation(*a_   , **a_     )





'''simple docstring'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   list[list[int]]						):
  if len(SCREAMING_SNAKE_CASE						) == 0 or not all(len(SCREAMING_SNAKE_CASE						) == 2 for pair in coordinates						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  if len({tuple(SCREAMING_SNAKE_CASE						) for pair in coordinates}						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	{x for x, _ in coordinates}
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	0
  UpperCAmelCase													=	[]
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    UpperCAmelCase													=	0
    UpperCAmelCase													=	coordinates[count_of_line][0]
    UpperCAmelCase													=	[]
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))						)
      count_in_line += 1
    matrix.append(SCREAMING_SNAKE_CASE						)
    count_of_line += 1

  UpperCAmelCase													=	0
  # put the y values into a vector
  UpperCAmelCase													=	[]
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]						)
    count_of_line += 1

  UpperCAmelCase													=	0

  while count < x:
    UpperCAmelCase													=	0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      UpperCAmelCase													=	matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]						):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  UpperCAmelCase													=	0
  # make solutions
  UpperCAmelCase													=	[]
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]						)						)
    count += 1

  UpperCAmelCase													=	0
  UpperCAmelCase													=	'f(x)='

  while count < x:
    UpperCAmelCase													=	solution[count].split('E'						)
    if len(SCREAMING_SNAKE_CASE						) > 1:
      UpperCAmelCase													=	f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
  print(points_to_polynomial([]))
  print(points_to_polynomial([[]]))
  print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
  print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
  print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


