"""simple docstring"""







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


UpperCamelCase_							:							str				 =		logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCamelCase_							:							List[Any]				 =		direct_transformers_import(Path(__file__).parent)


UpperCamelCase_							:							Optional[Any]				 =		{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}


class       _lowercase		( __UpperCamelCase	):
				lowerCamelCase__							=							['feature_extractor', 'tokenizer']
				# Names need to be attr_class for attr in attributes
				lowerCamelCase__							=							None
				lowerCamelCase__							=							None
				lowerCamelCase__							=							None

				def __init__(  self							:     List[Any]							,     *_lowercase							:     Union[str, Any]							,     **_lowercase							:     str  ):


										'''simple docstring'''
										# Sanitize args and kwargs
										for key in kwargs:
																if key not in self.attributes:
																						raise TypeError(f'Unexpected keyword argument {key}.'  )
										for arg, attribute_name in zip(__lowercase							,     self.attributes  ):
																if attribute_name in kwargs:
																						raise TypeError(f'Got multiple values for argument {attribute_name}.'  )
																else:
																						UpperCAmelCase__       						=		arg

										if len(__lowercase  ) != len(self.attributes  ):
																raise ValueError(
																    f'This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '
																    f'{len(__lowercase  )} arguments instead.'  )

										# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
										for attribute_name, arg in kwargs.items():
																UpperCAmelCase__       						=		getattr(self							,     f'{attribute_name}_class'  )
																# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																UpperCAmelCase__       						=		AUTO_TO_BASE_CLASS_MAPPING.get(__lowercase							,     __lowercase  )
																if isinstance(__lowercase							,     __lowercase  ):
																						UpperCAmelCase__       						=		tuple(getattr(__lowercase							,     __lowercase  ) for n in class_name if n is not None  )
																else:
																						UpperCAmelCase__       						=		getattr(__lowercase							,     __lowercase  )

																if not isinstance(__lowercase							,     __lowercase  ):
																						raise ValueError(
																						    f'Received a {type(__lowercase  ).__name__} for argument {attribute_name}, but a {class_name} was expected.'  )

																setattr(self							,     __lowercase							,     __lowercase  )

				def __repr__(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		[f'- {name}: {repr(getattr(self							,     __lowercase  )  )}' for name in self.attributes]
										UpperCAmelCase__       						=		"""\n""".join(__lowercase  )
										return f'{self.__class__.__name__}:\n{attributes_repr}'

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     Dict							,     _lowercase							:     Optional[int] = False							,     **_lowercase							:     int  ):


										'''simple docstring'''
										os.makedirs(__lowercase							,     exist_ok=__lowercase  )

										if push_to_hub:
																UpperCAmelCase__       						=		kwargs.pop("""commit_message"""							,     __lowercase  )
																UpperCAmelCase__       						=		kwargs.pop("""repo_id"""							,     save_directory.split(os.path.sep  )[-1]  )
																UpperCAmelCase__       						=		self._create_repo(__lowercase							,     **__lowercase  )
																UpperCAmelCase__       						=		self._get_files_timestamps(__lowercase  )
										# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
										# loaded from the Hub.
										if self._auto_class is not None:
																UpperCAmelCase__       						=		[getattr(self							,     __lowercase  ) for attribute_name in self.attributes]
																UpperCAmelCase__       						=		[(a.init_kwargs if isinstance(__lowercase							,     __lowercase  ) else a) for a in attrs]
																custom_object_save(self							,     __lowercase							,     config=__lowercase  )

										for attribute_name in self.attributes:
																UpperCAmelCase__       						=		getattr(self							,     __lowercase  )
																# Include the processor class in the attribute config so this processor can then be reloaded with the
																# `AutoProcessor` API.
																if hasattr(__lowercase							,     """_set_processor_class"""  ):
																						attribute._set_processor_class(self.__class__.__name__  )
																attribute.save_pretrained(__lowercase  )

										if self._auto_class is not None:
																# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
																for attribute_name in self.attributes:
																						UpperCAmelCase__       						=		getattr(self							,     __lowercase  )
																						if isinstance(__lowercase							,     __lowercase  ):
																												del attribute.init_kwargs["auto_map"]

										if push_to_hub:
																self._upload_modified_files(
																    __lowercase							,     __lowercase							,     __lowercase							,     commit_message=__lowercase							,     token=kwargs.get("""use_auth_token"""  )							,     )

				@classmethod
				def 							A			(  cls							:     int							,     _lowercase							:     Tuple							,     _lowercase							:     int = None							,     _lowercase							:     int = False							,     _lowercase							:     Union[str, Any] = False							,     _lowercase							:     List[Any] = None							,     _lowercase							:     Dict = "main"							,     **_lowercase							:     Tuple							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		cache_dir
										UpperCAmelCase__       						=		force_download
										UpperCAmelCase__       						=		local_files_only
										UpperCAmelCase__       						=		revision

										UpperCAmelCase__       						=		kwargs.pop("""use_auth_token"""							,     __lowercase  )
										if use_auth_token is not None:
																warnings.warn(
																    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""							,     __lowercase  )
																if token is not None:
																						raise ValueError(
																						    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""  )
																UpperCAmelCase__       						=		use_auth_token

										if token is not None:
																# change to `token` in a follow-up PR
																UpperCAmelCase__       						=		token

										UpperCAmelCase__       						=		cls._get_arguments_from_pretrained(__lowercase							,     **__lowercase  )
										return cls(*__lowercase  )

				@classmethod
				def 							A			(  cls							:     Union[str, Any]							,     _lowercase							:     List[Any]="AutoProcessor"  ):


										'''simple docstring'''
										if not isinstance(__lowercase							,     __lowercase  ):
																UpperCAmelCase__       						=		auto_class.__name__

										import transformers.models.auto as auto_module

										if not hasattr(__lowercase							,     __lowercase  ):
																raise ValueError(f'{auto_class} is not a valid auto class.'  )

										UpperCAmelCase__       						=		auto_class

				@classmethod
				def 							A			(  cls							:     int							,     _lowercase							:     Any							,     **_lowercase							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		[]
										for attribute_name in cls.attributes:
																UpperCAmelCase__       						=		getattr(cls							,     f'{attribute_name}_class'  )
																if isinstance(__lowercase							,     __lowercase  ):
																						UpperCAmelCase__       						=		tuple(getattr(__lowercase							,     __lowercase  ) if n is not None else None for n in class_name  )
																						UpperCAmelCase__       						=		kwargs.get("""use_fast"""							,     __lowercase  )
																						if use_fast and classes[1] is not None:
																												UpperCAmelCase__       						=		classes[1]
																						else:
																												UpperCAmelCase__       						=		classes[0]
																else:
																						UpperCAmelCase__       						=		getattr(__lowercase							,     __lowercase  )

																args.append(attribute_class.from_pretrained(__lowercase							,     **__lowercase  )  )
										return args

				@property
				def 							A			(  self							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		getattr(self							,     self.attributes[0]  )
										return getattr(__lowercase							,     """model_input_names"""							,     __lowercase  )
UpperCamelCase_							:							List[Any]				 =		copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
						UpperCamelCase_							:							str				 =		ProcessorMixin.push_to_hub.__doc__.format(
						    object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
						)









"""simple docstring"""



from string import ascii_uppercase




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = ascii_uppercase			)    ->str:
 UpperCAmelCase__ 						=				keyword.upper()
 UpperCAmelCase__ 						=				plaintext.upper()
 UpperCAmelCase__ 						=				set(_SCREAMING_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
 UpperCAmelCase__ 						=				[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(_SCREAMING_SNAKE_CASE			)
    # the number of those unique characters will determine the number of rows
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

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

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

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

 if verbose:
  print(_SCREAMING_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'''))



