
"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_     				=   {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_     				=   ['DetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_     				=   ['DetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_     				=   [
      'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DetrForObjectDetection',
      'DetrForSegmentation',
      'DetrModel',
      'DetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_detr import DetrFeatureExtractor
    from .image_processing_detr import DetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_detr import (
        DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetrForObjectDetection,
        DetrForSegmentation,
        DetrModel,
        DetrPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_     				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



"""simple docstring"""







import numpy as np






class     __a			:







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



          '''simple docstring'''

          _UpperCAmelCase   	=					depth
          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					min_leaf_size
          _UpperCAmelCase   	=					None







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

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

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







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          if x.ndim != 1:
             print("Error: Input data set must be one dimensional"		)
             return
          if len(lowerCamelCase_		) != len(lowerCamelCase_		):
             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(lowerCamelCase_		) < 2 * self.min_leaf_size:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
             return

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

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

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

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

          return







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''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(lowerCamelCase_		)
             else:
                return self.left.predict(lowerCamelCase_		)
          else:
             print("Error: Decision tree not yet trained"		)
             return None






class     __a			:







       @staticmethod
       def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					float(0		)
          for label in labels:
             squared_error_sum += (label - prediction) ** 2

          return float(squared_error_sum / labels.size		)



def 			__UpperCAmelCase		(		)     ->       Dict:
   _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
   _UpperCAmelCase   	=					np.sin(a_				)

   _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
   tree.train(a_    ,  a_				)

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

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


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

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

