



from __future__ import annotations

import typing
from collections import Counter







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				Counter()
		for base in range(1			,     max_perimeter + 1 ):
				for perpendicular in range(_lowerCAmelCase			,     max_perimeter + 1 ):
						UpperCAmelCase_						=				(base * base + perpendicular * perpendicular) ** 0.5
						if hypotenuse == int(_lowerCAmelCase ):
								UpperCAmelCase_						=				int(base + perpendicular + hypotenuse )
								if perimeter > max_perimeter:
										continue
								triplets[perimeter] += 1
		return triplets







def snake_case__  (			__SCREAMING_SNAKE_CASE = 1000 )		->							str:
		UpperCAmelCase_						=				pythagorean_triple(_lowerCAmelCase )
		return triplets.most_common(1 )[0][0]


if __name__ == "__main__":
			print(f'''Perimeter {solution()} has maximum solutions''')






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)

