


import argparse

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


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

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

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

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

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							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		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






from string import ascii_uppercase



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     str    ,   snake_case						:     bool = False    ,   snake_case						:     str = ascii_uppercase			)							->				str:
   __lowerCamelCase:					List[str]	 =						keyword.upper()
   __lowerCamelCase:					Tuple	 =						plaintext.upper()
   __lowerCamelCase:					Optional[Any]	 =						set(snake_case			)

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

   # create a shifted version of the alphabet
   __lowerCamelCase:					List[str]	 =						unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

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

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

         # map current letter to letter in modified alphabet
         __lowerCamelCase:					Union[str, Any]	 =						row[column]
         letter_index += 1

   if verbose:
      print(snake_case			)
   # 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'''))


