





import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()
def 			__UpperCamelCase				(			lowercase__	:       Optional[int]		,    lowercase__	:       List[str]		,    lowercase__	:       int		)	->      Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_					:	List[str]						=   json.load(open(lowercase__		,    """r"""		)		)
    lowerCAmelCase_					:	Optional[int]						=   GPTNeoConfig(
        hidden_size=config_json["""n_embd"""]		,    num_layers=config_json["""n_layer"""]		,    num_heads=config_json["""n_head"""]		,    attention_types=config_json["""attention_types"""]		,    max_position_embeddings=config_json["""n_positions"""]		,    resid_dropout=config_json["""res_dropout"""]		,    embed_dropout=config_json["""embed_dropout"""]		,    attention_dropout=config_json["""attn_dropout"""]		,    )
    print(f'Building PyTorch model from configuration: {config}'		)
    lowerCAmelCase_					:	Union[str, Any]						=   GPTNeoForCausalLM(lowercase__		)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(lowercase__		,    lowercase__		,    lowercase__		)

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


if __name__ == "__main__":
  __UpperCAmelCase						=      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 mesh-tf 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.'
  )
  __UpperCAmelCase						=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









from __future__ import annotations

import math
from itertools import permutations
def 			__UpperCamelCase				(			lowercase__	:       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(math.sqrt(lowercase__		) + 1		)		,    6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True
def 			__UpperCamelCase				(			lowercase__	:       int = 7		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   """""".join(str(lowercase__		) for i in range(1		,    n + 1		)		)
    lowerCAmelCase_					:	Any						=   [int("""""".join(lowercase__		)		) for i in permutations(lowercase__		,    lowercase__		)]
    lowerCAmelCase_					:	List[Any]						=   [num for num in perm_list if is_prime(lowercase__		)]
    return max(lowercase__		) if pandigitals else 0


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


