



'''simple docstring'''






import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


A_				:					Union[str, Any]							=      logging.get_logger(__name__)



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """hybrid-clip"""
 UpperCAmelCase		      =    True







 def __init__(				self    ,a_=512    ,**a_				)						->  int:
        super().__init__(**a_				)

        if "text_config" not in kwargs:
               raise ValueError("""`text_config` can not be `None`."""				)

        if "vision_config" not in kwargs:
               raise ValueError("""`vision_config` can not be `None`."""				)

        _UpperCAmelCase    :		int     	=					kwargs.pop("""text_config"""				)
        _UpperCAmelCase    :		Dict     	=					kwargs.pop("""vision_config"""				)

        _UpperCAmelCase    :		Optional[Any]     	=					text_config.pop("""model_type"""				)
        _UpperCAmelCase    :		Optional[int]     	=					vision_config.pop("""model_type"""				)

        from transformers import AutoConfig

        _UpperCAmelCase    :		Tuple     	=					AutoConfig.for_model(a_    ,**a_				)

        if vision_model_type == "clip":
               _UpperCAmelCase    :		Union[str, Any]     	=					AutoConfig.for_model(a_    ,**a_				).vision_config
        elif vision_model_type == "clip_vision_model":
               from transformers import CLIPVisionConfig

               _UpperCAmelCase    :		Optional[int]     	=					CLIPVisionConfig(**a_				)
        else:
               _UpperCAmelCase    :		Union[str, Any]     	=					AutoConfig.for_model(a_    ,**a_				)

        _UpperCAmelCase    :		int     	=					projection_dim
        _UpperCAmelCase    :		str     	=					1.0







 @classmethod
 def _snake_case					(				cls    ,a_    ,a_    ,**a_				)						->  Dict:
        return cls(text_config=text_config.to_dict()    ,vision_config=vision_config.to_dict()    ,**a_				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		int     	=					copy.deepcopy(self.__dict__				)
        _UpperCAmelCase    :		Optional[int]     	=					self.text_config.to_dict()
        _UpperCAmelCase    :		Any     	=					self.vision_config.to_dict()
        _UpperCAmelCase    :		Union[str, Any]     	=					self.__class__.model_type
        return output








'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

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







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               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(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

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

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

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[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(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

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

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


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

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



