






"""simple docstring"""





import argparse

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


logging.set_verbosity_info()







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Dict )      ->				List[str]:
  lowerCamelCase_         =						TaConfig.from_json_file(_SCREAMING_SNAKE_CASE )
  print(F'''Building PyTorch model from configuration: {config}''' )
  lowerCamelCase_         =						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					:				int					    =						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					:				Union[str, Any]					    =						parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)











from collections import defaultdict
from math import gcd



def    __lowercase   (      _SCREAMING_SNAKE_CASE = 1_50_00_00	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						defaultdict(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						2
      while 2 * euclid_m * (euclid_m + 1) <= limit:
            for euclid_n in range((euclid_m % 2) + 1 ,						_SCREAMING_SNAKE_CASE ,						2	):
                  if gcd(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) > 1:
                        continue
                  SCREAMING_SNAKE_CASE		=						2 * euclid_m * (euclid_m + euclid_n)
                  for perimeter in range(_SCREAMING_SNAKE_CASE ,						limit + 1 ,						_SCREAMING_SNAKE_CASE	):
                        frequencies[perimeter] += 1
            euclid_m += 1
      return sum(1 for frequency in frequencies.values() if frequency == 1	)


if __name__ == "__main__":
      print(F'''{solution() = }''')


