



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        __SCREAMING_SNAKE_CASE					(     UpperCamelCase_       ):
							snake_case     :      Optional[Any]						     =       ["""image_processor""", """tokenizer"""]
							snake_case     :      int						     =       """AutoImageProcessor"""
							snake_case     :      str						     =       """AutoTokenizer"""






							def __init__(						self  , __lowerCAmelCase=None  , __lowerCAmelCase=None  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			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  , )
																					UpperCamelCase__				     =			kwargs.pop("""feature_extractor"""  )

														UpperCamelCase__				     =			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  )
														UpperCamelCase__				     =			self.image_processor
														UpperCamelCase__				     =			False






							def __call__(						self  , *__lowerCAmelCase  , **__lowerCAmelCase  ):






														if self._in_target_context_manager:
																					return self.current_processor(*__a  , **__a  )

														UpperCamelCase__				     =			kwargs.pop("""images"""  , __a  )
														UpperCamelCase__				     =			kwargs.pop("""text"""  , __a  )
														if len(__a  ) > 0:
																					UpperCamelCase__				     =			args[0]
																					UpperCamelCase__				     =			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:
																					UpperCamelCase__				     =			self.image_processor(__a  , *__a  , **__a  )
														if text is not None:
																					UpperCamelCase__				     =			self.tokenizer(__a  , **__a  )

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					UpperCamelCase__				     =			encodings['input_ids']
																					return inputs






							def 				_lowerCamelCase		(						self  , *__lowerCAmelCase  , **__lowerCAmelCase  ):
														return self.tokenizer.batch_decode(*__a  , **__a  )






							def 				_lowerCamelCase		(						self  , *__lowerCAmelCase  , **__lowerCAmelCase  ):
														return self.tokenizer.decode(*__a  , **__a  )






							@contextmanager
							def 				_lowerCamelCase		(						self  ):
														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."""  )
														UpperCamelCase__				     =			True
														UpperCamelCase__				     =			self.tokenizer
														yield
														UpperCamelCase__				     =			self.image_processor
														UpperCamelCase__				     =			False






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase=False  , __lowerCAmelCase=None  ):






														if added_vocab is None:
																					UpperCamelCase__				     =			self.tokenizer.get_added_vocab()

														UpperCamelCase__				     =			{}

														while tokens:
																					UpperCamelCase__				     =			re.search(r"""<s_(.*?)>"""  , __a  , re.IGNORECASE  )
																					if start_token is None:
																												break
																					UpperCamelCase__				     =			start_token.group(1  )
																					UpperCamelCase__				     =			re.search(rf"""</s_{key}>"""  , __a  , re.IGNORECASE  )
																					UpperCamelCase__				     =			start_token.group()
																					if end_token is None:
																												UpperCamelCase__				     =			tokens.replace(__a  , """"""  )
																					else:
																												UpperCamelCase__				     =			end_token.group()
																												UpperCamelCase__				     =			re.escape(__a  )
																												UpperCamelCase__				     =			re.escape(__a  )
																												UpperCamelCase__				     =			re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""  , __a  , re.IGNORECASE  )
																												if content is not None:
																																			UpperCamelCase__				     =			content.group(1  ).strip()
																																			if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																										UpperCamelCase__				     =			self.tokenajson(__a  , is_inner_value=__a  , added_vocab=__a  )
																																										if value:
																																																	if len(__a  ) == 1:
																																																								UpperCamelCase__				     =			value[0]
																																																	UpperCamelCase__				     =			value
																																			else:  # leaf nodes
																																										UpperCamelCase__				     =			[]
																																										for leaf in content.split(r"""<sep/>"""  ):
																																																	UpperCamelCase__				     =			leaf.strip()
																																																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																								UpperCamelCase__				     =			leaf[1:-2]  # for categorical special tokens
																																																	output[key].append(__a  )
																																										if len(output[key]  ) == 1:
																																																	UpperCamelCase__				     =			output[key][0]

																												UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  ):
														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  , __a  , )
														return self.image_processor











UpperCamelCase__											= "ABCDEFGHIJKLMNOPQRSTUVWXYZ"





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			input("""Enter message: """       )
							UpperCamelCase__				     =			input("""Enter key [alphanumeric]: """       )
							UpperCamelCase__				     =			input("""Encrypt/Decrypt [e/d]: """       )

							if mode.lower().startswith("""e"""       ):
														UpperCamelCase__				     =			"""encrypt"""
														UpperCamelCase__				     =			encrypt_message(a__   ,			a__       )
							elif mode.lower().startswith("""d"""       ):
														UpperCamelCase__				     =			"""decrypt"""
														UpperCamelCase__				     =			decrypt_message(a__   ,			a__       )

							print(f"""\n{mode.title()}ed message:"""       )
							print(a__       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""encrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""decrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			key.upper()

							for symbol in message:
														UpperCamelCase__				     =			LETTERS.find(symbol.upper()       )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]       )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]       )

																					num %= len(a__       )

																					if symbol.isupper():
																												translated.append(LETTERS[num]       )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()       )

																					key_index += 1
																					if key_index == len(a__       ):
																												UpperCamelCase__				     =			0
														else:
																					translated.append(a__       )
							return "".join(a__       )


if __name__ == "__main__":
					main()




