
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       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			json.load(open(__magic_name__		,						"r"	)	)
				lowercase						    =			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}'''	)
				lowercase						    =			GPTNeoForCausalLM(__magic_name__	)

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

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


if __name__ == "__main__":
						_snake_case			:       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 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."
						)
						_snake_case			:       str      =						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


_snake_case			:       List[Any]      =						{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				return [alphabet[char] for char in key.upper()]

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			""
				lowercase						    =			0
				lowercase						    =			generate_table(__magic_name__	)
				for char in words.upper():
								cipher += get_opponent(table[count]		,						__magic_name__	)
								lowercase						    =			(count + 1) % len(__magic_name__	)
				return cipher

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return encrypt(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			0 if char in table[0] else 1
				lowercase						    =			table[row].index(__magic_name__	)
				return row, col

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase    ,  lowercase						    =			get_position(__magic_name__		,						char.upper()	)
				if row == 1:
								return table[0][col]
				else:
								return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						_snake_case			:       List[Any]      =						input("Enter key: ").strip()
						_snake_case			:       Optional[int]      =						input("Enter text to encrypt: ").strip()
						_snake_case			:       Optional[Any]      =						encrypt(key, text)

						print(F"Encrypted: {cipher_text}")
						print(F"Decrypted with key: {decrypt(key, cipher_text)}")
