

'''simple docstring'''






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__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				XGBClassifier()
 classifier.fit(_A  ,       _A   )
 return classifier


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				load_iris()
 __lowercase													=				data_handling(_A   )
 __lowercase													=				train_test_split(
     _A  ,       _A  ,       test_size=0.2_5   )

 __lowercase													=				iris["target_names"]

 # Create an XGBoost Classifier from the training data
 __lowercase													=				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()




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

