






"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


_UpperCamelCase   :						Optional[Any]     						= logging.get_logger(__name__)

_UpperCamelCase   :						Optional[Any]     						= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase   :						List[str]     						= {
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCamelCase   :						Optional[int]     						= {
    """openai/clip-vit-base-patch32""": 77,
}






class __a   (   snake_case_				):
  __lowercase    :    Any						=					VOCAB_FILES_NAMES
  __lowercase    :    Any						=					PRETRAINED_VOCAB_FILES_MAP
  __lowercase    :    Dict						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase    :    List[str]						=					['input_ids', 'attention_mask']
  __lowercase    :    Union[str, Any]						=					CLIPTokenizer



  def __init__(self  :		Union[str, Any]				,			lowercase_  :		Union[str, Any]=None				,			lowercase_  :		Tuple=None				,			lowercase_  :		List[str]=None				,			lowercase_  :		List[str]="<|endoftext|>"				,			lowercase_  :		int="<|startoftext|>"				,			lowercase_  :		Dict="<|endoftext|>"				,			lowercase_  :		Optional[Any]="<|endoftext|>"				,			**lowercase_  :		str				,			)		->							List[Any]:
   super().__init__(
       lowercase_				,			lowercase_				,			tokenizer_file=lowercase_				,			unk_token=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			pad_token=lowercase_				,			**lowercase_				,			)

   if not isinstance(self.backend_tokenizer.pre_tokenizer				,			pre_tokenizers.Sequence   ):
    raise ValueError(
        'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
        ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
        ' to be compatible with this version.The easiest way to do so is'
        ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
        ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
        ' transformers.'   )

   self._wrap_decode_method_backend_tokenizer()



  def 						A_				(self  :		Any   )		->							Optional[int]:
   __A	:       List[Any]       =	self.backend_tokenizer.decode

   def new_decode_method(*lowercase_  :		Optional[Any]				,			**lowercase_  :		Union[str, Any]   ):
    __A	:       List[Any]       =	orig_decode_method(*lowercase_				,			**lowercase_   )
    __A	:       List[Any]       =	text.replace(self.backend_tokenizer.model.end_of_word_suffix				,			' '   ).strip()
    return text

   __A	:       List[Any]       =	new_decode_method



  def 						A_				(self  :		str				,			lowercase_  :		List[int]				,			lowercase_  :		Optional[List[int]] = None   )		->							List[int]:
   __A	:       List[str]       =	[self.bos_token_id]
   __A	:       Optional[int]       =	[self.eos_token_id]

   if token_ids_a is None:
    return bos_token + token_ids_a + eos_token
   return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token



  def 						A_				(self  :		Any				,			lowercase_  :		List[int]				,			lowercase_  :		Optional[List[int]] = None   )		->							List[int]:
   __A	:       Union[str, Any]       =	[self.bos_token_id]
   __A	:       str       =	[self.eos_token_id]

   if token_ids_a is None:
    return len(bos_token + token_ids_a + eos_token   ) * [0]
   return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]



  def 						A_				(self  :		Optional[int]				,			lowercase_  :		str				,			lowercase_  :		Optional[str] = None   )		->							Tuple[str]:
   __A	:       Optional[int]       =	self._tokenizer.model.save(lowercase_				,			name=lowercase_   )
   return tuple(lowercase_   )













"""simple docstring"""


from string import ascii_uppercase




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				bool = False     ,	UpperCamelCase_							:				str = ascii_uppercase		)  ->		str:
 __A	:       List[str]       =	keyword.upper()
 __A	:       str       =	plaintext.upper()
 __A	:       int       =	set(UpperCamelCase_		)

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

 # create a shifted version of the alphabet
 __A	:       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
 __A	:       int       =	[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0     ,	2_6     ,	UpperCamelCase_		)
 ]

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

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

 if verbose:
  print(UpperCamelCase_		)
 # 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"""))





