

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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   XGBClassifier()
							classifier.fit(UpperCamelCase__       ,							UpperCamelCase__				)
							return classifier





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   load_iris()
							snake_case_    ,   snake_case_          =   data_handling(UpperCamelCase__				)
							snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   train_test_split(
							    UpperCamelCase__       ,							UpperCamelCase__       ,							test_size=0.25				)

							snake_case_          =   iris['target_names']

							# Create an XGBoost Classifier from the training data
							snake_case_          =   xgboost(UpperCamelCase__       ,							UpperCamelCase__				)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()







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


_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       int						    =	{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			int	      =      '''canine'''




				def __init__( self				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1_6384				,       snake_case=16				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=0xE000				,       snake_case=0xE001				,       snake_case=4				,       snake_case=4				,       snake_case=8				,       snake_case=1_6384				,       snake_case=128				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       **snake_case	)

											snake_case_          =   max_position_embeddings
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   initializer_range
											snake_case_          =   type_vocab_size
											snake_case_          =   layer_norm_eps

											# Character config:
											snake_case_          =   downsampling_rate
											snake_case_          =   upsampling_kernel_size
											snake_case_          =   num_hash_functions
											snake_case_          =   num_hash_buckets
											snake_case_          =   local_transformer_stride




