
'''simple docstring'''





import numpy as np

class     a :




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=5,     SCREAMING_SNAKE_CASE_						:					Dict=5   ):
										snake_case							:  Optional[Any]			=     depth
										snake_case							:  int			=     0
										snake_case							:  List[str]			=     None
										snake_case							:  Tuple			=     None
										snake_case							:  List[Any]			=     min_leaf_size
										snake_case							:  List[Any]			=     None




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if labels.ndim != 1:
																print('''Error: Input labels must be one dimensional'''   )

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




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if x.ndim != 1:
																print('''Error: Input data set must be one dimensional'''   )
																return
										if len(SCREAMING_SNAKE_CASE_   ) != len(SCREAMING_SNAKE_CASE_   ):
																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(SCREAMING_SNAKE_CASE_   ) < 2 * self.min_leaf_size:
																snake_case							:  Tuple			=     np.mean(SCREAMING_SNAKE_CASE_   )
																return

										if self.depth == 1:
																snake_case							:  Dict			=     np.mean(SCREAMING_SNAKE_CASE_   )
																return

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

										if best_split != 0:
																snake_case							:  Union[str, Any]			=     x[:best_split]
																snake_case							:  Union[str, Any]			=     y[:best_split]
																snake_case							:  Any			=     x[best_split:]
																snake_case							:  List[str]			=     y[best_split:]

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

										return




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										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(SCREAMING_SNAKE_CASE_   )
																else:
																						return self.left.predict(SCREAMING_SNAKE_CASE_   )
										else:
																print('''Error: Decision tree not yet trained'''   )
																return None




class     a :




				@staticmethod
				def 						__snake_case				(				SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  Optional[Any]			=     float(0   )
										for label in labels:
																squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size   )




def   A			(							):
						snake_case							:  Union[str, Any]			=     np.arange(-1.0     ,				1.0     ,				0.005							)
						snake_case							:  Optional[Any]			=     np.sin(A_							)

						snake_case							:  List[Any]			=     DecisionTree(depth=10     ,				min_leaf_size=10							)
						tree.train(A_     ,				A_							)

						snake_case							:  Dict			=     (np.random.rand(10							) * 2) - 1
						snake_case							:  Optional[Any]			=     np.array([tree.predict(A_							) for x in test_cases]							)
						snake_case							:  Union[str, Any]			=     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)



'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

