


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


snake_case__     							= logging.get_logger(__name__)

snake_case__     							= {
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''yoso'''
				def __init__(  self     ,      _UpperCAmelCase=5_0265     ,      _UpperCAmelCase=768     ,      _UpperCAmelCase=12     ,      _UpperCAmelCase=12     ,      _UpperCAmelCase=3072     ,      _UpperCAmelCase="gelu"     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=4096     ,      _UpperCAmelCase=1     ,      _UpperCAmelCase=0.02     ,      _UpperCAmelCase=1E-1_2     ,      _UpperCAmelCase="absolute"     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=9     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=1     ,      _UpperCAmelCase=0     ,      _UpperCAmelCase=2     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(pad_token_id=_UpperCAmelCase     ,      bos_token_id=_UpperCAmelCase     ,      eos_token_id=_UpperCAmelCase     ,      **_UpperCAmelCase      )

										__magic_name__				:							Tuple				=      vocab_size
										__magic_name__				:							List[str]				=      max_position_embeddings
										__magic_name__				:							Union[str, Any]				=      hidden_size
										__magic_name__				:							Union[str, Any]				=      num_hidden_layers
										__magic_name__				:							List[Any]				=      num_attention_heads
										__magic_name__				:							Optional[Any]				=      intermediate_size
										__magic_name__				:							List[str]				=      hidden_act
										__magic_name__				:							List[str]				=      hidden_dropout_prob
										__magic_name__				:							Optional[int]				=      attention_probs_dropout_prob
										__magic_name__				:							int				=      initializer_range
										__magic_name__				:							Optional[Any]				=      type_vocab_size
										__magic_name__				:							Dict				=      layer_norm_eps
										__magic_name__				:							List[str]				=      position_embedding_type
										__magic_name__				:							Union[str, Any]				=      use_expectation
										__magic_name__				:							List[str]				=      hash_code_len
										__magic_name__				:							Dict				=      num_hash
										__magic_name__				:							List[str]				=      conv_window
										__magic_name__				:							Optional[Any]				=      use_fast_hash
										__magic_name__				:							Dict				=      lsh_backward






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

# Load iris file
snake_case__     							= 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
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

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

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

snake_case__     							= knn.predict(X_new)

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


