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


_a   :      str       =				logging.get_logger(__name__)

_a   :      int       =				{
    """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   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Dict	=       """yoso"""
    def __init__(     self    ,     snake_case_=5_02_65    ,     snake_case_=7_68    ,     snake_case_=12    ,     snake_case_=12    ,     snake_case_=30_72    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=40_96    ,     snake_case_=1    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_="absolute"    ,     snake_case_=True    ,     snake_case_=9    ,     snake_case_=64    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     **snake_case_  )

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


"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

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

# KNN

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

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

lowercase__   			=      knn.predict(X_new)

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


