from __future__ import annotations

import typing
from collections import Counter




def 				UpperCamelCase						(       __lowercase						: Optional[Any]	):
		'''simple docstring'''




		A_		:				typing.Counter[int]     =      Counter()
		for base in range(1						,max_perimeter + 1	):
				for perpendicular in range(_SCREAMING_SNAKE_CASE						,max_perimeter + 1	):
						A_		:				Tuple     =      (base * base + perpendicular * perpendicular) ** 0.5
						if hypotenuse == int(_SCREAMING_SNAKE_CASE	):
								A_		:				Tuple     =      int(base + perpendicular + hypotenuse	)
								if perimeter > max_perimeter:
										continue
								triplets[perimeter] += 1
		return triplets




def 				UpperCamelCase						(       __lowercase						: Optional[Any] = 10_00	):
		'''simple docstring'''




		A_		:				Dict     =      pythagorean_triple(_SCREAMING_SNAKE_CASE	)
		return triplets.most_common(1	)[0][0]


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












import argparse

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


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

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

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   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.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






