



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 snake_case__  (			__SCREAMING_SNAKE_CASE )		->							tuple:
		return (data["data"], data["target"])







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							XGBClassifier:
		UpperCAmelCase_						=				XGBClassifier()
		classifier.fit(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		return classifier







def snake_case__  (			)		->							None:
		UpperCAmelCase_						=				load_iris()
		UpperCAmelCase_					,						UpperCAmelCase_						=				data_handling(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				train_test_split(
		    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     test_size=0.25 )

		UpperCAmelCase_						=				iris["target_names"]

		# Create an XGBoost Classifier from the training data
		UpperCAmelCase_						=				xgboost(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

