




"""simple docstring"""







import numpy as np






class  SCREAMING_SNAKE_CASE__   :


						def __init__(							self			:     Optional[int]  ,  lowerCAmelCase			:     Dict=5  ,  lowerCAmelCase			:     int=5   ):
										lowerCAmelCase		=			depth
										lowerCAmelCase		=			0
										lowerCAmelCase		=			None
										lowerCAmelCase		=			None
										lowerCAmelCase		=			min_leaf_size
										lowerCAmelCase		=			None


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     Any   ):
										if labels.ndim != 1:
														print("""Error: Input labels must be one dimensional"""   )

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


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Union[str, Any]  ,  lowerCAmelCase			:     Dict   ):
										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:
														lowerCAmelCase		=			np.mean(lowerCAmelCase   )
														return

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

										lowerCAmelCase		=			0
										lowerCAmelCase		=			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:
																		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(lowerCAmelCase  ,  lowerCAmelCase   )
														self.right.train(lowerCAmelCase  ,  lowerCAmelCase   )
										else:
														lowerCAmelCase		=			np.mean(lowerCAmelCase   )

										return




						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     Any   ):
										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  SCREAMING_SNAKE_CASE__   :


						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Any   ):
										lowerCAmelCase		=			float(0   )
										for label in labels:
														squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size   )





def 							lowercase   ()       ->					Tuple:


				'''simple docstring'''






				lowerCAmelCase		=			np.arange(-1.0 ,    1.0 ,    0.005	)
				lowerCAmelCase		=			np.sin(snake_case__	)

				lowerCAmelCase		=			DecisionTree(depth=10 ,    min_leaf_size=10	)
				tree.train(snake_case__ ,    snake_case__	)

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

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


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

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






"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""  ,  lowerCAmelCase  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			image_processor if image_processor is not None else feature_extractor
										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`."""   )

										super().__init__(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     str=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     int=None  ,  **lowerCAmelCase			:     str   ):

										if text is None and videos is None:
														raise ValueError("""You have to specify either text or videos. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and videos is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor_class




						@property
						def 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





