
'''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__ :						str    							=					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__ :						Union[str, Any]    							=					train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

UpperCamelCase__ :						str    							=					KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase__ :						Union[str, Any]    							=					[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase__ :						Dict    							=					knn.predict(X_new)

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








'''simple docstring'''



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


UpperCamelCase__ :						int    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'cpmant'






       def __init__(							self    ,							A__ = 3_07_20    ,							A__ = 40_96    ,							A__ = 32    ,							A__ = 1_28    ,							A__ = 1_02_40    ,							A__ = 48    ,							A__ = 0.0    ,							A__ = 5_12    ,							A__ = 20_48    ,							A__ = 1E-6    ,							A__ = 1.0    ,							A__ = 32    ,							A__ = 32    ,							A__ = 32    ,							A__ = True    ,							**A__    ,							)      ->     List[Any]:
          super().__init__(**A__ )
          _SCREAMING_SNAKE_CASE       	=			prompt_types
          _SCREAMING_SNAKE_CASE       	=			prompt_length
          _SCREAMING_SNAKE_CASE       	=			segment_types
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dim_head
          _SCREAMING_SNAKE_CASE       	=			dim_ff
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			position_bias_num_buckets
          _SCREAMING_SNAKE_CASE       	=			position_bias_max_distance
          _SCREAMING_SNAKE_CASE       	=			dropout_p
          _SCREAMING_SNAKE_CASE       	=			eps
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			init_std






