from ... import PretrainedConfig


a_    					=       {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
 lowercase				     =   'nezha'

 def __init__( self	:							Optional[Any]   ,      snake_case	:							List[Any]=2_1_1_2_8   ,      snake_case	:							List[str]=7_6_8   ,      snake_case	:							Any=1_2   ,      snake_case	:							List[Any]=1_2   ,      snake_case	:							Any=3_0_7_2   ,      snake_case	:							Union[str, Any]="gelu"   ,      snake_case	:							Optional[int]=0.1   ,      snake_case	:							Optional[int]=0.1   ,      snake_case	:							List[Any]=5_1_2   ,      snake_case	:							int=6_4   ,      snake_case	:							Optional[Any]=2   ,      snake_case	:							int=0.02   ,      snake_case	:							Optional[Any]=1e-12   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							Dict=0   ,      snake_case	:							Optional[int]=2   ,      snake_case	:							Optional[Any]=3   ,      snake_case	:							Optional[Any]=True   ,      **snake_case	:							List[Any]   ,      )   ->						List[str]:

  """simple docstring"""





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

  UpperCamelCase_					:						Optional[int] =					vocab_size
  UpperCamelCase_					:						Optional[Any] =					hidden_size
  UpperCamelCase_					:						List[Any] =					num_hidden_layers
  UpperCamelCase_					:						str =					num_attention_heads
  UpperCamelCase_					:						List[Any] =					hidden_act
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						str =					hidden_dropout_prob
  UpperCamelCase_					:						int =					attention_probs_dropout_prob
  UpperCamelCase_					:						List[Any] =					max_position_embeddings
  UpperCamelCase_					:						Union[str, Any] =					max_relative_position
  UpperCamelCase_					:						Any =					type_vocab_size
  UpperCamelCase_					:						Tuple =					initializer_range
  UpperCamelCase_					:						Any =					layer_norm_eps
  UpperCamelCase_					:						int =					classifier_dropout
  UpperCamelCase_					:						Optional[Any] =					use_cache








import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





					return (data["data"], data["target"])






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    A ,     A ,     A ,     display_labels=A ,     cmap="Blues" ,     normalize="true" ,     )
					plt.title("Normalized Confusion Matrix - IRIS Dataset"       )
					plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
