




import argparse

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


logging.set_verbosity_info()







def 		_A(       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[int]				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    TaConfig.from_json_file(SCREAMING_SNAKE_CASE_				)
							print(F'Building PyTorch model from configuration: {config}'				)
							__lowercase      =    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__":
			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 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__				     = parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












import math







def 		_A(       )    -> None:
							'''simple docstring'''


							__lowercase      =    input('''Enter message: '''				)
							__lowercase      =    int(input(F'Enter key [2-{len(UpperCamelCase__				) - 1}]: '				)				)
							__lowercase      =    input('''Encryption/Decryption [e/d]: '''				)

							if mode.lower().startswith('''e'''				):
														__lowercase      =    encrypt_message(UpperCamelCase__				,       UpperCamelCase__				)
							elif mode.lower().startswith('''d'''				):
														__lowercase      =    decrypt_message(UpperCamelCase__				,       UpperCamelCase__				)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}'				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    [''''''] * key
							for col in range(UpperCamelCase__				):
														__lowercase      =    col
														while pointer < len(UpperCamelCase__				):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    math.ceil(len(UpperCamelCase__				) / key				)
							__lowercase      =    key
							__lowercase      =    (num_cols * num_rows) - len(UpperCamelCase__				)
							__lowercase      =    [''''''] * num_cols
							__lowercase      =    0
							__lowercase      =    0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__lowercase      =    0
																					row += 1

							return "".join(UpperCamelCase__				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()






