




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    snake_case   (				A		,  A		,  A     )				->				Dict:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				json.load(open(A		,  'r'     )     )
	lowerCamelCase_				:						str				  =				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_				:						Dict				  =				GPTNeoForCausalLM(A     )

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

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


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)




"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

    a											={char for login in split_logins for char in login}

    for permutation in itertools.permutations(lowercase						):
        a											=True
        for login in logins:
            if not (
                permutation.index(login[0]						)
                < permutation.index(login[1]						)
                < permutation.index(login[2]						)
            ):
                a											=False
                break

        if satisfied:
            return int(''''''.join(lowercase						)						)

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


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