



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


_UpperCamelCase							    =						logging.get_logger(__name__)

_UpperCamelCase							    =						{
    '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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 ="""reformer"""
  a_							 =["""past_buckets_states"""]
  a_							 ={}




  def __init__(			self				:     str      ,     _a				:     str=64      ,     _a				:     int=["local", "lsh", "local", "lsh", "local", "lsh"]      ,     _a				:     Optional[int]=1.0      ,     _a				:     int=True      ,     _a				:     Optional[Any]=[64, 64]      ,     _a				:     Union[str, Any]=[64, 192]      ,     _a				:     Optional[Any]=0      ,     _a				:     Tuple=2      ,     _a				:     Dict=512      ,     _a				:     Optional[Any]=None      ,     _a				:     Union[str, Any]="relu"      ,     _a				:     Optional[Any]=0.05      ,     _a				:     Optional[int]=256      ,     _a				:     Optional[int]=0.02      ,     _a				:     List[str]=False      ,     _a				:     List[Any]=1e-12      ,     _a				:     Any=1      ,     _a				:     Any=0      ,     _a				:     Any=0.05      ,     _a				:     Tuple=64      ,     _a				:     Any=64      ,     _a				:     Union[str, Any]=0.0      ,     _a				:     Any=1      ,     _a				:     List[str]=0      ,     _a				:     Optional[int]=4096      ,     _a				:     int=12      ,     _a				:     Union[str, Any]=None      ,     _a				:     List[str]=1      ,     _a				:     str=0      ,     _a				:     Union[str, Any]=320      ,     _a				:     Tuple=False      ,     _a				:     List[str]=True      ,     _a				:     List[str]=None      ,     **_a				:     Dict      ,     ) -> List[Any]:
    __lowerCamelCase	:	Union[str, Any]   	=			hash_seed
    __lowerCamelCase	:	Union[str, Any]   	=			vocab_size
    __lowerCamelCase	:	Optional[int]   	=			attention_head_size
    __lowerCamelCase	:	Any   	=			hidden_size
    __lowerCamelCase	:	Union[str, Any]   	=			num_attention_heads
    __lowerCamelCase	:	Dict   	=			num_hashes
    __lowerCamelCase	:	Tuple   	=			len(_a       )
    __lowerCamelCase	:	List[str]   	=			tuple(_a       ) if isinstance(_a      ,     _a       ) else num_buckets
    __lowerCamelCase	:	List[Any]   	=			lsh_attn_chunk_length
    __lowerCamelCase	:	str   	=			local_attn_chunk_length
    __lowerCamelCase	:	str   	=			lsh_num_chunks_after
    __lowerCamelCase	:	Union[str, Any]   	=			lsh_num_chunks_before
    __lowerCamelCase	:	List[Any]   	=			local_num_chunks_after
    __lowerCamelCase	:	str   	=			local_num_chunks_before
    __lowerCamelCase	:	Union[str, Any]   	=			hidden_act
    __lowerCamelCase	:	List[Any]   	=			feed_forward_size
    __lowerCamelCase	:	Optional[Any]   	=			hidden_dropout_prob
    __lowerCamelCase	:	Dict   	=			lsh_attention_probs_dropout_prob
    __lowerCamelCase	:	Union[str, Any]   	=			local_attention_probs_dropout_prob
    __lowerCamelCase	:	int   	=			max_position_embeddings
    __lowerCamelCase	:	Any   	=			initializer_range
    __lowerCamelCase	:	List[Any]   	=			layer_norm_eps
    __lowerCamelCase	:	int   	=			axial_pos_embds
    __lowerCamelCase	:	Dict   	=			tuple(_a       )
    __lowerCamelCase	:	Union[str, Any]   	=			tuple(_a       )
    __lowerCamelCase	:	List[str]   	=			axial_norm_std
    __lowerCamelCase	:	str   	=			chunk_size_lm_head
    __lowerCamelCase	:	Optional[Any]   	=			attn_layers
    __lowerCamelCase	:	List[Any]   	=			use_cache
    __lowerCamelCase	:	List[str]   	=			classifier_dropout
    super().__init__(
        pad_token_id=_a      ,     eos_token_id=_a      ,     is_decoder=_a      ,     tie_word_embeddings=_a      ,     **_a      ,     )











'''simple docstring'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






