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


lowerCamelCase__        =						logging.get_logger(__name__)

lowerCamelCase__        =						{
    """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__				(	__magic_name__      ):
				lowercase            =    'reformer'
				lowercase            =    ['past_buckets_states']
				lowercase            =    {}






				def __init__(		self   : Optional[Any]       ,	a   : List[Any]=64       ,	a   : List[Any]=["local", "lsh", "local", "lsh", "local", "lsh"]       ,	a   : Any=1.0       ,	a   : Any=True       ,	a   : Optional[int]=[64, 64]       ,	a   : Dict=[64, 192]       ,	a   : str=0       ,	a   : Union[str, Any]=2       ,	a   : Union[str, Any]=512       ,	a   : List[str]=None       ,	a   : Union[str, Any]="relu"       ,	a   : int=0.0_5       ,	a   : List[Any]=256       ,	a   : List[str]=0.0_2       ,	a   : Tuple=False       ,	a   : Optional[Any]=1E-12       ,	a   : Optional[int]=1       ,	a   : List[str]=0       ,	a   : Optional[Any]=0.0_5       ,	a   : Tuple=64       ,	a   : List[str]=64       ,	a   : int=0.0       ,	a   : Union[str, Any]=1       ,	a   : int=0       ,	a   : int=4_096       ,	a   : Optional[int]=12       ,	a   : Tuple=None       ,	a   : Union[str, Any]=1       ,	a   : Optional[int]=0       ,	a   : Optional[int]=320       ,	a   : Optional[Any]=False       ,	a   : Tuple=True       ,	a   : str=None       ,	**a   : Union[str, Any]       ,	):






							'''simple docstring'''







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

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

# Load iris file
lowerCamelCase__        =						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
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

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

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

lowerCamelCase__        =						knn.predict(X_new)

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