


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__            =							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCAmelCase__            =							direct_transformers_import(Path(__file__).parent)


UpperCAmelCase__            =							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}







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





     def __init__(self		,					*UpperCAmelCase		,					**UpperCAmelCase	)					->       Dict:
      # 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(UpperCAmelCase		,					self.attributes	):
       if attribute_name in kwargs:
        raise TypeError(f"Got multiple values for argument {attribute_name}."	)
       else:
        _lowercase											=arg

      if len(UpperCAmelCase	) != len(self.attributes	):
       raise ValueError(
           f"This processor requires {len(self.attributes	)} arguments: {', '.join(self.attributes	)}. Got "
           f"{len(UpperCAmelCase	)} 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():
       _lowercase											=getattr(self		,					f"{attribute_name}_class"	)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       _lowercase											=AUTO_TO_BASE_CLASS_MAPPING.get(UpperCAmelCase		,					UpperCAmelCase	)
       if isinstance(UpperCAmelCase		,					UpperCAmelCase	):
        _lowercase											=tuple(getattr(UpperCAmelCase		,					UpperCAmelCase	) for n in class_name if n is not None	)
       else:
        _lowercase											=getattr(UpperCAmelCase		,					UpperCAmelCase	)

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

       setattr(self		,					UpperCAmelCase		,					UpperCAmelCase	)





     def __repr__(self	)					->       Dict:
      _lowercase											=[f"- {name}: {repr(getattr(self		,					UpperCAmelCase	)	)}" for name in self.attributes]
      _lowercase											='''\n'''.join(UpperCAmelCase	)
      return f"{self.__class__.__name__}:\n{attributes_repr}"





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = False		,					**UpperCAmelCase	)					->       Optional[int]:
      os.makedirs(UpperCAmelCase		,					exist_ok=UpperCAmelCase	)

      if push_to_hub:
       _lowercase											=kwargs.pop('''commit_message'''		,					UpperCAmelCase	)
       _lowercase											=kwargs.pop('''repo_id'''		,					save_directory.split(os.path.sep	)[-1]	)
       _lowercase											=self._create_repo(UpperCAmelCase		,					**UpperCAmelCase	)
       _lowercase											=self._get_files_timestamps(UpperCAmelCase	)
      # 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:
       _lowercase											=[getattr(self		,					UpperCAmelCase	) for attribute_name in self.attributes]
       _lowercase											=[(a.init_kwargs if isinstance(UpperCAmelCase		,					UpperCAmelCase	) else a) for a in attrs]
       custom_object_save(self		,					UpperCAmelCase		,					config=UpperCAmelCase	)

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

      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:
        _lowercase											=getattr(self		,					UpperCAmelCase	)
        if isinstance(UpperCAmelCase		,					UpperCAmelCase	):
         del attribute.init_kwargs["auto_map"]

      if push_to_hub:
       self._upload_modified_files(
           UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					commit_message=UpperCAmelCase		,					token=kwargs.get('''use_auth_token'''	)		,					)





     @classmethod
     def 					__A (cls		,					UpperCAmelCase		,					UpperCAmelCase = None		,					UpperCAmelCase = False		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = "main"		,					**UpperCAmelCase		,					)					->       Tuple:
      _lowercase											=cache_dir
      _lowercase											=force_download
      _lowercase											=local_files_only
      _lowercase											=revision

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

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

      _lowercase											=cls._get_arguments_from_pretrained(UpperCAmelCase		,					**UpperCAmelCase	)
      return cls(*UpperCAmelCase	)





     @classmethod
     def 					__A (cls		,					UpperCAmelCase="AutoProcessor"	)					->       Dict:
      if not isinstance(UpperCAmelCase		,					UpperCAmelCase	):
       _lowercase											=auto_class.__name__

      import transformers.models.auto as auto_module

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

      _lowercase											=auto_class





     @classmethod
     def 					__A (cls		,					UpperCAmelCase		,					**UpperCAmelCase	)					->       Optional[int]:
      _lowercase											=[]
      for attribute_name in cls.attributes:
       _lowercase											=getattr(cls		,					f"{attribute_name}_class"	)
       if isinstance(UpperCAmelCase		,					UpperCAmelCase	):
        _lowercase											=tuple(getattr(UpperCAmelCase		,					UpperCAmelCase	) if n is not None else None for n in class_name	)
        _lowercase											=kwargs.get('''use_fast'''		,					UpperCAmelCase	)
        if use_fast and classes[1] is not None:
         _lowercase											=classes[1]
        else:
         _lowercase											=classes[0]
       else:
        _lowercase											=getattr(UpperCAmelCase		,					UpperCAmelCase	)

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





     @property
     def 					__A (self	)					->       Optional[int]:
      _lowercase											=getattr(self		,					self.attributes[0]	)
      return getattr(UpperCAmelCase		,					'''model_input_names'''		,					UpperCAmelCase	)



UpperCAmelCase__            =							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  UpperCAmelCase__            =							ProcessorMixin.push_to_hub.__doc__.format(
      object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
  )





UpperCAmelCase__            =							[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      bool:







 """simple docstring"""


 return 0 <= row < rows and 0 <= col < cols
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=len(__snake_case						)
 _lowercase											=len(mat[0]						)
 if is_safe(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						) and (row, col) not in seen and mat[row][col] == 1:
  seen.add((row, col)						)
  return (
      1
      + depth_first_search(row + 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(row - 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col + 1					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col - 1					,						__snake_case					,						__snake_case						)
  )
 else:
  return 0
def     UpperCAmelCase_ (							__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=set()

 _lowercase											=0
 for row, line in enumerate(__snake_case						):
  for col, item in enumerate(__snake_case						):
   if item == 1 and (row, col) not in seen:
    # Maximizing the area
    _lowercase											=max(__snake_case					,						depth_first_search(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)						)
 return max_area


if __name__ == "__main__":
  import doctest

  print(find_max_area(matrix))  # Output -> 6
  doctest.testmod()

