"""simple docstring"""







import numpy as np


class      _lowercase						:

				def __init__(       self    :      int    ,      _lowercase    :      Optional[Any]=5    ,      _lowercase    :      int=5       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						depth
										UpperCAmelCase__   		=						0
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						min_leaf_size
										UpperCAmelCase__   		=						None

				def     A	(       self    :      int    ,      _lowercase    :      Optional[Any]    ,      _lowercase    :      Union[str, Any]       ):


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

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

				def     A	(       self    :      Dict    ,      _lowercase    :      Optional[int]    ,      _lowercase    :      List[Any]       ):


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

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

										UpperCAmelCase__   		=						0
										UpperCAmelCase__   		=						self.mean_squared_error(_lowercase    ,      np.mean(_lowercase       )       ) * 2
										for i in range(len(_lowercase       )       ):
																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(_lowercase    ,      _lowercase       )
																self.right.train(_lowercase    ,      _lowercase       )
										else:
																UpperCAmelCase__   		=						np.mean(_lowercase       )

										return

				def     A	(       self    :      List[Any]    ,      _lowercase    :      int       ):


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


class      _lowercase						:

				@staticmethod
				def     A	(       _lowercase    :      int    ,      _lowercase    :      Optional[Any]       ):


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

										return float(squared_error_sum / labels.size       )


def 					a__ (						)->		List[Any]:
						UpperCAmelCase__   		=						np.arange(-1.0						,     1.0						,     0.0_05     )
						UpperCAmelCase__   		=						np.sin(UpperCAmelCase__     )

						UpperCAmelCase__   		=						DecisionTree(depth=1_0						,     min_leaf_size=1_0     )
						tree.train(UpperCAmelCase__						,     UpperCAmelCase__     )

						UpperCAmelCase__   		=						(np.random.rand(1_0     ) * 2) - 1
						UpperCAmelCase__   		=						np.array([tree.predict(UpperCAmelCase__     ) for x in test_cases]     )
						UpperCAmelCase__   		=						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)


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										if self.task == "multiple-choice":
																UpperCAmelCase__   		=						{0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]       )

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

