
"""simple docstring"""





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 			lowercase__  (	__lowerCamelCase,	__lowerCamelCase						):
   # Initialize PyTorch model
   UpperCAmelCase_      :      Dict      =					CanineConfig()
   UpperCAmelCase_      :      Optional[int]      =					CanineModel(__lowerCamelCase						)
   model.eval()

   print(f"""Building PyTorch model from configuration: {config}"""						)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)

   # Save pytorch-model (weights and configuration)
   print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
   model.save_pretrained(__lowerCamelCase						)

   # Save tokenizer files
   UpperCAmelCase_      :      List[str]      =					CanineTokenizer()
   print(f"""Save tokenizer files to {pytorch_dump_path}"""						)
   tokenizer.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
    _a	 =							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
    )
    parser.add_argument(
        '--pytorch_dump_path',
        default=None,
        type=str,
        required=True,
        help='Path to a folder where the PyTorch model will be placed.',
    )
    _a	 =							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)




"""simple docstring"""





from string import ascii_uppercase







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
   UpperCAmelCase_      :      List[str]      =					keyword.upper()
   UpperCAmelCase_      :      str      =					plaintext.upper()
   UpperCAmelCase_      :      Tuple      =					set(__lowerCamelCase						)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   UpperCAmelCase_      :      Tuple      =					[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(__lowerCamelCase						)
    # the number of those unique characters will determine the number of rows
   UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)

   # create a shifted version of the alphabet
   UpperCAmelCase_      :      Tuple      =					unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   UpperCAmelCase_      :      List[str]      =					[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0,	26,	__lowerCamelCase						)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   UpperCAmelCase_      :      Tuple      =					{}
   UpperCAmelCase_      :      Dict      =					0
   for column in range(__lowerCamelCase						):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(__lowerCamelCase						) <= column:
            break

         # map current letter to letter in modified alphabet
         UpperCAmelCase_      :      Tuple      =					row[column]
         letter_index += 1

   if verbose:
      print(__lowerCamelCase						)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext						)


if __name__ == "__main__":
    # example use
    print(mixed_keyword('college', 'UNIVERSITY'))


