

'''simple docstring'''





import argparse

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


logging.set_verbosity_info()







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Optional[Any]:
					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__":
				A_	= 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."
				)
				A_	= parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










from math import sqrt
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5				,	int(sqrt(SCREAMING_SNAKE_CASE_						) + 1						)				,	6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 1_00_01						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   1
   while count != nth and number < 3:
      number += 1
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   while count != nth:
      number += 2
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   return number


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