
'''simple docstring'''

from __future__ import annotations

from collections import Counter







def   A     (      UpperCamelCase_					:     Optional[int]      )					->       list[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							[True] * n
			lowerCAmelCase__								=							False
			lowerCAmelCase__								=							False
			lowerCAmelCase__								=							True

			for i in range(3						,	int(n**0.5 + 1      )						,	2      ):
						lowerCAmelCase__								=							i * 2
						while index < n:
									lowerCAmelCase__								=							False
									lowerCAmelCase__								=							index + i

			lowerCAmelCase__								=							[2]

			for i in range(3						,	snake_case__						,	2      ):
						if is_prime[i]:
									primes.append(snake_case__      )

			return primes







def   A     (      UpperCamelCase_					:     List[Any]      )					->       list[list[int]]:







			'''simple docstring'''



			lowerCAmelCase__								=							str(snake_case__      )
			lowerCAmelCase__								=							[]
			lowerCAmelCase__								=							["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]

			for duplicate in Counter(snake_case__      ) - Counter(set(snake_case__      )      ):
						lowerCAmelCase__								=							[int(number_str.replace(snake_case__						,	snake_case__      )      ) for digit in digits]
						replacements.append(snake_case__      )

			return replacements







def   A     (      UpperCamelCase_					:     str = 8      )					->       int:







			'''simple docstring'''



			lowerCAmelCase__								=							set()

			# Filter primes with less than 3 replaceable digits
			lowerCAmelCase__								=							{
			    x for x in set(prime_sieve(1_00_00_00      )      ) if len(str(snake_case__      )      ) - len(set(str(snake_case__      )      )      ) >= 3
			}

			for prime in primes:
						if prime in numbers_checked:
									continue

						lowerCAmelCase__								=							digit_replacements(snake_case__      )

						for family in replacements:
									numbers_checked.update(snake_case__      )
									lowerCAmelCase__								=							primes.intersection(snake_case__      )

									if len(snake_case__      ) != family_length:
												continue

									return min(snake_case__      )

			return -1


if __name__ == "__main__":
		print(solution())







'''simple docstring'''

import argparse

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


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

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

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
			model.save_pretrained(UpperCamelCase_      )


if __name__ == "__main__":
		UpperCAmelCase__   : List[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."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





