


'''simple docstring'''

from typing import List

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


if is_torch_available():
    import torch





class 					snake_case   (   lowerCamelCase__      ):





  """simple docstring"""




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






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







   """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__(lowercase_      ,  lowercase_					)






  def   snake_case		(   self      ,  UpperCamelCase=None      ,  UpperCamelCase=77					):







   """simple docstring"""






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

   lowerCamelCase_								=  self.tokenizer(lowercase_      ,  padding="max_length"      ,  max_length=lowercase_      ,  truncation=lowercase_					)

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

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

   lowerCamelCase_								=  torch.cat(lowercase_      ,  dim=0					)
   return token_inputs






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







   """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(lowercase_      ,  lowercase_      ,  lowercase_      ,  **lowercase_					)

   if isinstance(lowercase_      ,  lowercase_					):
    lowerCamelCase_								=  [task_inputs]

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

   if hasattr(lowercase_      ,  "text_inputs"					):
    lowerCamelCase_								=  encoded_inputs.text_inputs

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

    lowerCamelCase_								=  torch.cat(lowercase_      ,  dim=0					)

   return encoded_inputs






  def   snake_case		(   self      ,  UpperCamelCase=None      ,  UpperCamelCase=None      ,  UpperCamelCase=None      ,  **UpperCamelCase					):







   """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(lowercase_      ,  lowercase_      ,  lowercase_      ,  **lowercase_					)

   if isinstance(lowercase_      ,  lowercase_					):
    lowerCamelCase_								=  [task_inputs]

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

   if hasattr(lowercase_      ,  "text_inputs"					):
    lowerCamelCase_								=  encoded_inputs.text_inputs

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

    lowerCamelCase_								=  torch.cat(lowercase_      ,  dim=0					)

   return encoded_inputs






  def   snake_case		(   self      ,  *UpperCamelCase      ,  **UpperCamelCase					):







   """simple docstring"""
   return self.image_processor.post_process_semantic_segmentation(*lowercase_      ,  **lowercase_					)






  def   snake_case		(   self      ,  *UpperCamelCase      ,  **UpperCamelCase					):







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







  def   snake_case		(   self      ,  *UpperCamelCase      ,  **UpperCamelCase					):







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







'''simple docstring'''

import numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



