"""simple docstring"""






from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:int							=				c_float






    @property
    def _snake_case    (     self							):
       return sizeof(self.value							)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = 0				)						->						int:
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowercase__:     List[str]      =   fixed_dimension

   dimension -= num_token_to_add
   return dimension



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						int:
   return num_parameters * dtype.size




def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(__UpperCAmelCase				)
   except (ValueError, OSError, KeyError):
      lowercase__,				lowercase__:     Any      =   None, None
      try:
         lowercase__:     Dict      =   AutoTokenizer.from_pretrained(__UpperCAmelCase				)
      except (OSError, KeyError):
         pass
      try:
         lowercase__:     Optional[Any]      =   AutoFeatureExtractor.from_pretrained(__UpperCAmelCase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor

"""simple docstring"""






class 					UpperCAmelCase		:



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase=None							):
       lowercase__:     Optional[int]      =   data
       lowercase__:     Dict      =   None






    def __repr__(     self							):
       lowercase__:     int      =   ''''''
       lowercase__:     Any      =   self
       while temp:
          string_rep += F"""<{temp.data}> ---> """
          lowercase__:     Any      =   temp.next
       string_rep += "<END>"
       return string_rep



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:

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

   # Set first element as Head
   lowercase__:     Union[str, Any]      =   Node(elements_list[0]				)
   lowercase__:     List[str]      =   head
   # Loop through elements from position 1
   for data in elements_list[1:]:
      lowercase__:     Tuple      =   Node(__UpperCAmelCase				)
      lowercase__:     Tuple      =   current.next
   return head


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




print(linked_list)
