




'''simple docstring'''







class   _snake_case:






 def __init__(self					:     Any						,		a					:     Dict=None						)					->      List[Any]:






    """simple docstring"""


    A__         =    data
    A__         =    None






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






    """simple docstring"""


    A__         =    ''
    A__         =    self
    while temp:
       string_rep += f"""<{temp.data}> ---> """
       A__         =    temp.next
    string_rep += "<END>"
    return string_rep







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if not elements_list:
      raise Exception('The Elements List is empty'	)

   # Set first element as Head
   A__         =    Node(elements_list[0]	)
   A__         =    head
   # Loop through elements from position 1
   for data in elements_list[1:]:
      A__         =    Node(UpperCAmelCase	)
      A__         =    current.next
   return head


lowerCAmelCase_    	=							[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(f'''List: {list_data}''')
print('''Creating Linked List from List.''')
lowerCAmelCase_    	=							make_linked_list(list_data)
print('''Linked List:''')







print(linked_list)








'''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


lowerCAmelCase_    	=							logging.get_logger(__name__)

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


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

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''feature_extractor''', '''tokenizer''']
 # Names need to be attr_class for attr in attributes
 __snake_case:       Optional[Any]									=				None
 __snake_case:       List[str]									=				None
 __snake_case:       Any									=				None






 def __init__(self					:     List[Any]						,		*a					:     Union[str, Any]						,		**a					:     Optional[int]						)					->      Any:






    """simple docstring"""


    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(f"""Unexpected keyword argument {key}."""						)
    for arg, attribute_name in zip(a						,		self.attributes						):
       if attribute_name in kwargs:
          raise TypeError(f"""Got multiple values for argument {attribute_name}."""						)
       else:
          A__         =    arg

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

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

       setattr(self						,		a						,		a						)






 def __repr__(self					:     Optional[int]						)					->      Any:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     bool = False						,		**a					:     int						)					->      List[str]:






    """simple docstring"""


    os.makedirs(a						,		exist_ok=a						)

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

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

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

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






 @classmethod
 def 	_UpperCamelCase   (cls					:     int						,		a					:     Union[str, os.PathLike]						,		a					:     Optional[Union[str, os.PathLike]] = None						,		a					:     bool = False						,		a					:     bool = False						,		a					:     Optional[Union[str, bool]] = None						,		a					:     str = "main"						,		**a					:     Union[str, Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    cache_dir
    A__         =    force_download
    A__         =    local_files_only
    A__         =    revision

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

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

    A__         =    cls._get_arguments_from_pretrained(a						,		**a						)
    return cls(*a						)






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[int]						,		a					:     Union[str, Any]="AutoProcessor"						)					->      Any:






    """simple docstring"""


    if not isinstance(a						,		a						):
       A__         =    auto_class.__name__

    import transformers.models.auto as auto_module

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

    A__         =    auto_class






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[Any]						,		a					:     Optional[Any]						,		**a					:     Tuple						)					->      int:






    """simple docstring"""


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

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






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      List[str]:






    """simple docstring"""


    A__         =    getattr(self						,		self.attributes[0]						)
    return getattr(a						,		'model_input_names'						,		a						)




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


