
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

A__ :		Tuple					={
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}







class 			__A					(			snake_case_      ):
 lowerCamelCase												="reformer"
 lowerCamelCase												=["past_buckets_states"]
 lowerCamelCase												={}
 def __init__( self      :     List[Any]					, lowerCamelCase      :     List[str]=64					, lowerCamelCase      :     List[str]=["local", "lsh", "local", "lsh", "local", "lsh"]					, lowerCamelCase      :     List[str]=1.0					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     Optional[Any]=[64, 64]					, lowerCamelCase      :     Tuple=[64, 1_92]					, lowerCamelCase      :     Tuple=0					, lowerCamelCase      :     List[Any]=2					, lowerCamelCase      :     Tuple=5_12					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     Optional[int]="relu"					, lowerCamelCase      :     int=0.05					, lowerCamelCase      :     Union[str, Any]=2_56					, lowerCamelCase      :     Any=0.02					, lowerCamelCase      :     Optional[int]=False					, lowerCamelCase      :     Tuple=1e-1_2					, lowerCamelCase      :     int=1					, lowerCamelCase      :     str=0					, lowerCamelCase      :     Any=0.05					, lowerCamelCase      :     Optional[int]=64					, lowerCamelCase      :     int=64					, lowerCamelCase      :     List[str]=0.0					, lowerCamelCase      :     Tuple=1					, lowerCamelCase      :     int=0					, lowerCamelCase      :     int=40_96					, lowerCamelCase      :     int=12					, lowerCamelCase      :     int=None					, lowerCamelCase      :     List[Any]=1					, lowerCamelCase      :     Any=0					, lowerCamelCase      :     Optional[Any]=3_20					, lowerCamelCase      :     Dict=False					, lowerCamelCase      :     List[str]=True					, lowerCamelCase      :     int=None					, **lowerCamelCase      :     int					, ):



   """simple docstring"""





   __A		:  Tuple       				=		hash_seed
   __A		:  Optional[Any]       				=		vocab_size
   __A		:  Dict       				=		attention_head_size
   __A		:  Union[str, Any]       				=		hidden_size
   __A		:  Dict       				=		num_attention_heads
   __A		:  List[Any]       				=		num_hashes
   __A		:  str       				=		len(lowercase_							)
   __A		:  int       				=		tuple(lowercase_							) if isinstance(lowercase_					, lowercase_							) else num_buckets
   __A		:  str       				=		lsh_attn_chunk_length
   __A		:  List[str]       				=		local_attn_chunk_length
   __A		:  Dict       				=		lsh_num_chunks_after
   __A		:  int       				=		lsh_num_chunks_before
   __A		:  Union[str, Any]       				=		local_num_chunks_after
   __A		:  List[str]       				=		local_num_chunks_before
   __A		:  str       				=		hidden_act
   __A		:  Tuple       				=		feed_forward_size
   __A		:  Optional[int]       				=		hidden_dropout_prob
   __A		:  Any       				=		lsh_attention_probs_dropout_prob
   __A		:  Tuple       				=		local_attention_probs_dropout_prob
   __A		:  List[Any]       				=		max_position_embeddings
   __A		:  Tuple       				=		initializer_range
   __A		:  Any       				=		layer_norm_eps
   __A		:  Optional[Any]       				=		axial_pos_embds
   __A		:  Dict       				=		tuple(lowercase_							)
   __A		:  Dict       				=		tuple(lowercase_							)
   __A		:  int       				=		axial_norm_std
   __A		:  Any       				=		chunk_size_lm_head
   __A		:  str       				=		attn_layers
   __A		:  Optional[int]       				=		use_cache
   __A		:  Dict       				=		classifier_dropout
   super().__init__(
       pad_token_id=lowercase_					, eos_token_id=lowercase_					, is_decoder=lowercase_					, tie_word_embeddings=lowercase_					, **lowercase_					, )








import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									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:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

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

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

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

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									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 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

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


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

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

