

import numpy as np





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self       ,    _A=5       ,    _A=5				):
												__lowerCAmelCase       =				depth
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				None
												__lowerCAmelCase       =				None
												__lowerCAmelCase       =				min_leaf_size
												__lowerCAmelCase       =				None




							def 							A__	(			self       ,    _A       ,    _A				):
												if labels.ndim != 1:
																	print('Error: Input labels must be one dimensional'				)

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




							def 							A__	(			self       ,    _A       ,    _A				):
												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:
																	__lowerCAmelCase       =				np.mean(_A				)
																	return

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

												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				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:
																						__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(_A       ,    _A				)
																	self.right.train(_A       ,    _A				)
												else:
																	__lowerCAmelCase       =				np.mean(_A				)

												return




							def 							A__	(			self       ,    _A				):
												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      snake_case_      :


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A				):
												__lowerCAmelCase       =				float(0				)
												for label in labels:
																	squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size				)


def 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				np.arange(-1.0		,       1.0		,       0.005   )
					__lowerCAmelCase       =				np.sin(UpperCAmelCase__   )

					__lowerCAmelCase       =				DecisionTree(depth=10		,       min_leaf_size=10   )
					tree.train(UpperCAmelCase__		,       UpperCAmelCase__   )

					__lowerCAmelCase       =				(np.random.rand(10   ) * 2) - 1
					__lowerCAmelCase       =				np.array([tree.predict(UpperCAmelCase__   ) for x in test_cases]   )
					__lowerCAmelCase       =				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)









import copy

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt_decoder"""




							def __init__(			self       ,    _A=0       ,    _A=0				):
												super().__init__()
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				bos_token_id





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fsmt"""
							__UpperCAmelCase      ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




							def __init__(			self       ,    _A=["en", "de"]       ,    _A=4_2_0_2_4       ,    _A=4_2_0_2_4       ,    _A="relu"       ,    _A=1_0_2_4       ,    _A=2_0_0       ,    _A=1_0_2_4       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=4_0_9_6       ,    _A=1_2       ,    _A=1_6       ,    _A=0.0       ,    _A=0.0       ,    _A=0.1       ,    _A=0.0       ,    _A=0.02       ,    _A=2       ,    _A=True       ,    _A=True       ,    _A=False       ,    _A=5       ,    _A=1.0       ,    _A=False       ,    _A=True       ,    _A=1       ,    _A=0       ,    _A=2       ,    _A=2       ,    **_A       ,    ):
												__lowerCAmelCase       =				langs
												__lowerCAmelCase       =				src_vocab_size
												__lowerCAmelCase       =				tgt_vocab_size
												__lowerCAmelCase       =				d_model  # encoder_embed_dim and decoder_embed_dim

												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				__lowerCAmelCase       =				encoder_layers
												__lowerCAmelCase       =				encoder_attention_heads
												__lowerCAmelCase       =				encoder_layerdrop
												__lowerCAmelCase       =				decoder_layerdrop
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				decoder_layers
												__lowerCAmelCase       =				decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												__lowerCAmelCase       =				DecoderConfig(vocab_size=_A       ,    bos_token_id=_A				)
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												__lowerCAmelCase       =				scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache
												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    decoder_start_token_id=_A       ,    is_encoder_decoder=_A       ,    tie_word_embeddings=_A       ,    forced_eos_token_id=_A       ,    max_length=_A       ,    num_beams=_A       ,    length_penalty=_A       ,    early_stopping=_A       ,    **_A       ,    )




							def 							A__	(			self				):
												__lowerCAmelCase       =				copy.deepcopy(self.__dict__				)
												__lowerCAmelCase       =				self.decoder.to_dict()
												__lowerCAmelCase       =				self.__class__.model_type
												return output






