





"""simple docstring"""




import numpy as np




class  _a						:






				def __init__( self		:    Any, lowerCAmelCase__		:    Dict=5, lowerCAmelCase__		:    Tuple=5							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    depth
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					Optional[Any]     =    None
										_UpperCamelCase			:					Dict     =    None
										_UpperCamelCase			:					str     =    min_leaf_size
										_UpperCamelCase			:					List[str]     =    None






				def 				snake_case	( self		:    int, lowerCAmelCase__		:    str, lowerCAmelCase__		:    List[str]							)	->      Optional[Any]:




										'''simple docstring'''



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

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






				def 				snake_case	( self		:    int, lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Any							)	->      Any:




										'''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			:					Any     =    np.mean(lowerCAmelCase__							)
																return

										if self.depth == 1:
																_UpperCamelCase			:					Optional[int]     =    np.mean(lowerCAmelCase__							)
																return

										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    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			:					List[str]     =    self.mean_squared_error(x[:i], np.mean(y[:i]							)							)
																						_UpperCamelCase			:					Tuple     =    self.mean_squared_error(x[i:], np.mean(y[i:]							)							)
																						_UpperCamelCase			:					Any     =    error_left + error_right
																						if error < min_error:
																												_UpperCamelCase			:					Union[str, Any]     =    i
																												_UpperCamelCase			:					str     =    error

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

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

										return






				def 				snake_case	( self		:    Tuple, lowerCAmelCase__		:    Union[str, Any]							)	->      Optional[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(lowerCAmelCase__							)
																else:
																						return self.left.predict(lowerCAmelCase__							)
										else:
																print('''Error: Decision tree not yet trained'''							)
																return None




class  _a						:






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    Dict							)	->      str:




										'''simple docstring'''



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

										return float(squared_error_sum / labels.size							)





def        a_				(							):
						_UpperCamelCase			:					Optional[int]     =    np.arange(-1.0       ,						1.0       ,						0.005   )
						_UpperCamelCase			:					List[str]     =    np.sin(_lowercase   )

						_UpperCamelCase			:					Union[str, Any]     =    DecisionTree(depth=10       ,						min_leaf_size=10   )
						tree.train(_lowercase       ,						_lowercase   )

						_UpperCamelCase			:					Optional[Any]     =    (np.random.rand(10   ) * 2) - 1
						_UpperCamelCase			:					Tuple     =    np.array([tree.predict(_lowercase   ) for x in test_cases]   )
						_UpperCamelCase			:					int     =    np.mean((predictions - test_cases) ** 2   )

						print('''Test values: ''' + str(_lowercase   )   )
						print('''Predictions: ''' + str(_lowercase   )   )
						print('''Average error: ''' + str(_lowercase   )   )


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

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










"""simple docstring"""




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)




class  _a						(      unittest.TestCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Tuple, lowerCAmelCase__		:    List[str]=1_3, lowerCAmelCase__		:    Optional[int]=7, lowerCAmelCase__		:    Optional[Any]=True, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    Any=True, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Any=9_9, lowerCAmelCase__		:    Union[str, Any]=2_4, lowerCAmelCase__		:    Optional[Any]=3_2, lowerCAmelCase__		:    Tuple=5, lowerCAmelCase__		:    int=4, lowerCAmelCase__		:    List[Any]=3_7, lowerCAmelCase__		:    Dict="gelu", lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    str=5_1_2, lowerCAmelCase__		:    Tuple=1_6, lowerCAmelCase__		:    int=2, lowerCAmelCase__		:    int=0.02, lowerCAmelCase__		:    Optional[int]=4, )	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					str     =    seq_length
										_UpperCamelCase			:					Any     =    is_training
										_UpperCamelCase			:					Union[str, Any]     =    use_attention_mask
										_UpperCamelCase			:					List[Any]     =    use_token_type_ids
										_UpperCamelCase			:					str     =    use_labels
										_UpperCamelCase			:					Union[str, Any]     =    vocab_size
										_UpperCamelCase			:					int     =    hidden_size
										_UpperCamelCase			:					Optional[int]     =    embedding_size
										_UpperCamelCase			:					int     =    num_hidden_layers
										_UpperCamelCase			:					int     =    num_attention_heads
										_UpperCamelCase			:					Tuple     =    intermediate_size
										_UpperCamelCase			:					Optional[int]     =    hidden_act
										_UpperCamelCase			:					Any     =    hidden_dropout_prob
										_UpperCamelCase			:					Union[str, Any]     =    attention_probs_dropout_prob
										_UpperCamelCase			:					Any     =    max_position_embeddings
										_UpperCamelCase			:					List[str]     =    type_vocab_size
										_UpperCamelCase			:					Union[str, Any]     =    type_sequence_label_size
										_UpperCamelCase			:					str     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    num_choices






				def 				snake_case	( self		:    Optional[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    ids_tensor([self.batch_size, self.seq_length], self.vocab_size							)

										_UpperCamelCase			:					Dict     =    None
										if self.use_attention_mask:
																_UpperCamelCase			:					Union[str, Any]     =    random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase			:					Optional[Any]     =    None
										if self.use_token_type_ids:
																_UpperCamelCase			:					Union[str, Any]     =    ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size							)

										_UpperCamelCase			:					Optional[Any]     =    ElectraConfig(
										    vocab_size=self.vocab_size, hidden_size=self.hidden_size, embedding_size=self.embedding_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, )

										return config, input_ids, token_type_ids, attention_mask






				def 				snake_case	( self		:    Any							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.prepare_config_and_inputs()
										_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    config_and_inputs
										_UpperCamelCase			:					List[Any]     =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




@require_flax
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    True

				UpperCamelCase								=    (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    FlaxElectraModelTester(self							)






				@slow
				def 				snake_case	( self		:    Optional[Any]							)	->      str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						_UpperCamelCase			:					int     =    model_class_name.from_pretrained('''google/electra-small-generator'''							)
																else:
																						_UpperCamelCase			:					Any     =    model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
																_UpperCamelCase			:					List[str]     =    model(np.ones((1, 1)							)							)
																self.assertIsNotNone(lowerCAmelCase__							)



