
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						["image_processor", "tokenizer"]
       __lowerCamelCase				       =						"AutoImageProcessor"
       __lowerCamelCase				       =						"AutoTokenizer"


       def __init__(    self						,       __A=None						,       __A=None						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                         ''' instead.'''						,       __A						,       )
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=kwargs.pop('''feature_extractor'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_processor if image_processor is not None else feature_extractor
              if image_processor is None:
                     raise ValueError('''You need to specify an `image_processor`.'''				)
              if tokenizer is None:
                     raise ValueError('''You need to specify a `tokenizer`.'''				)

              super().__init__(__A						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=False


       def __call__(    self						,       *__A						,       **__A				)						->    List[str]:
              # For backward compatibility
              if self._in_target_context_manager:
                     return self.current_processor(*__A						,       **__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=kwargs.pop('''images'''						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=kwargs.pop('''text'''						,       __A				)
              if len(__A				) > 0:
                     SCREAMING_SNAKE_CASE_							:				str		=args[0]
                     SCREAMING_SNAKE_CASE_							:				Any		=args[1:]

              if images is None and text is None:
                     raise ValueError('''You need to specify either an `images` or `text` input to process.'''				)

              if images is not None:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor(__A						,       *__A						,       **__A				)
              if text is not None:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=self.tokenizer(__A						,       **__A				)

              if text is None:
                     return inputs
              elif images is None:
                     return encodings
              else:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=encodings['''input_ids''']
                     return inputs


       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    Union[str, Any]:
              return self.tokenizer.batch_decode(*__A						,       **__A				)


       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    Any:
              return self.tokenizer.decode(*__A						,       **__A				)


       @contextmanager
       def 	_snake_case				(    self				)						->    Tuple:
              warnings.warn(
                  '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
                  '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
                  '''your images inputs, or in a separate call.'''				)
              SCREAMING_SNAKE_CASE_							:				Any		=True
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenizer
              yield
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processor
              SCREAMING_SNAKE_CASE_							:				List[str]		=False


       def 	_snake_case				(    self						,       __A						,       __A=False						,       __A=None				)						->    Optional[Any]:
              if added_vocab is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.tokenizer.get_added_vocab()

              SCREAMING_SNAKE_CASE_							:				Tuple		={}

              while tokens:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=re.search(R'''<s_(.*?)>'''						,       __A						,       re.IGNORECASE				)
                     if start_token is None:
                            break
                     SCREAMING_SNAKE_CASE_							:				Any		=start_token.group(1				)
                     SCREAMING_SNAKE_CASE_							:				Any		=re.search(RF'</s_{key}>'						,       __A						,       re.IGNORECASE				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=start_token.group()
                     if end_token is None:
                            SCREAMING_SNAKE_CASE_							:				int		=tokens.replace(__A						,       ''''''				)
                     else:
                            SCREAMING_SNAKE_CASE_							:				Any		=end_token.group()
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=re.escape(__A				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=re.escape(__A				)
                            SCREAMING_SNAKE_CASE_							:				str		=re.search(F'{start_token_escaped}(.*?){end_token_escaped}'						,       __A						,       re.IGNORECASE				)
                            if content is not None:
                                   SCREAMING_SNAKE_CASE_							:				int		=content.group(1				).strip()
                                   if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                          SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenajson(__A						,       is_inner_value=__A						,       added_vocab=__A				)
                                          if value:
                                                 if len(__A				) == 1:
                                                        SCREAMING_SNAKE_CASE_							:				Optional[Any]		=value[0]
                                                 SCREAMING_SNAKE_CASE_							:				List[Any]		=value
                                   else:  # leaf nodes
                                          SCREAMING_SNAKE_CASE_							:				Optional[int]		=[]
                                          for leaf in content.split(R'''<sep/>'''				):
                                                 SCREAMING_SNAKE_CASE_							:				Optional[Any]		=leaf.strip()
                                                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                        SCREAMING_SNAKE_CASE_							:				Any		=leaf[1:-2]  # for categorical special tokens
                                                 output[key].append(__A				)
                                          if len(output[key]				) == 1:
                                                 SCREAMING_SNAKE_CASE_							:				Dict		=output[key][0]

                            SCREAMING_SNAKE_CASE_							:				List[str]		=tokens[tokens.find(__A				) + len(__A				) :].strip()
                            if tokens[:6] == r"<sep/>":  # non-leaf nodes
                                   return [output] + self.tokenajson(tokens[6:]						,       is_inner_value=__A						,       added_vocab=__A				)

              if len(__A				):
                     return [output] if is_inner_value else output
              else:
                     return [] if is_inner_value else {"text_sequence": tokens}


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              warnings.warn(
                  '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,       __A						,       )
              return self.image_processor_class





       @property
       def 	_snake_case				(    self				)						->    Optional[Any]:
              warnings.warn(
                  '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,       __A						,       )
              return self.image_processor


import string

import numpy
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int:
       return b if a == 0 else greatest_common_divisor(b % a			,							UpperCAmelCase_				)


class 			lowercase_		:
       __lowerCamelCase				       =						string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       __lowerCamelCase				       =						numpy.vectorize(lambda A       : x % 3_6       )

       __lowerCamelCase				       =						numpy.vectorize(A       )


       def __init__(    self						,       __A				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.modulus(__A				)  # mod36 calc's on the encrypt key
              self.check_determinant()  # validate the determinant of the encryption key
              SCREAMING_SNAKE_CASE_							:				Dict		=encrypt_key.shape[0]


       def 	_snake_case				(    self						,       __A				)						->    int:
              return self.key_string.index(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              return self.key_string[round(__A				)]


       def 	_snake_case				(    self				)						->    None:
              SCREAMING_SNAKE_CASE_							:				List[str]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=det % len(self.key_string				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=len(self.key_string				)
              if greatest_common_divisor(__A						,       len(self.key_string				)				) != 1:
                     SCREAMING_SNAKE_CASE_							:				Any		=(
                         F'determinant modular {req_l} of encryption key({det}) '
                         F'is not co prime w.r.t {req_l}.\nTry another key.'
                     )
                     raise ValueError(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[char for char in text.upper() if char in self.key_string]

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=chars[-1]
              while len(__A				) % self.break_key != 0:
                     chars.append(__A				)

              return "".join(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				Any		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self.modulus(self.encrypt_key.dot(__A				)				).T.tolist()[
                         0
                     ]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_encrypted				)
                     encrypted += encrypted_batch

              return encrypted


       def 	_snake_case				(    self				)						->    numpy.ndarray:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=det % len(self.key_string				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=None
              for i in range(len(self.key_string				)				):
                     if (det * i) % len(self.key_string				) == 1:
                            SCREAMING_SNAKE_CASE_							:				List[str]		=i
                            break

              SCREAMING_SNAKE_CASE_							:				List[str]		=(
                  det_inv
                  * numpy.linalg.det(self.encrypt_key				)
                  * numpy.linalg.inv(self.encrypt_key				)
              )

              return self.to_int(self.modulus(__A				)				)





       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				int		=self.make_decrypt_key()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Any		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				str		=self.modulus(decrypt_key.dot(__A				)				).T.tolist()[0]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_decrypted				)
                     decrypted += decrypted_batch

              return decrypted






def 							SCREAMING_SNAKE_CASE_	(					)			->						None:
       SCREAMING_SNAKE_CASE_							:				List[str]		=int(input('''Enter the order of the encryption key: '''				)				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[]

       print('''Enter each row of the encryption key with space separated integers'''				)
       for _ in range(UpperCAmelCase_				):
              SCREAMING_SNAKE_CASE_							:				Tuple		=[int(UpperCAmelCase_				) for x in input().split()]
              hill_matrix.append(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=HillCipher(numpy.array(UpperCAmelCase_				)				)

       print('''Would you like to encrypt or decrypt some text? (1 or 2)'''				)
       SCREAMING_SNAKE_CASE_							:				Dict		=input('''\n1. Encrypt\n2. Decrypt\n'''				)
       if option == "1":
              SCREAMING_SNAKE_CASE_							:				List[Any]		=input('''What text would you like to encrypt?: '''				)
              print('''Your encrypted text is:'''				)
              print(hc.encrypt(UpperCAmelCase_				)				)
       elif option == "2":
              SCREAMING_SNAKE_CASE_							:				Any		=input('''What text would you like to decrypt?: '''				)
              print('''Your decrypted text is:'''				)
              print(hc.decrypt(UpperCAmelCase_				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
