


from __future__ import annotations

from typing import Any
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     pass




class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:int     ,		_a:Any   ):
       snake_case__						= data
       snake_case__						= None

     def __iter__(					self:Any   ):
       snake_case__						= self
       snake_case__						= []
       while node:
         if node in visited:
           raise ContainsLoopError
         visited.append(_a   )
         yield node.data
         snake_case__						= node.next_node







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       try:
         list(self   )
         return False
       except ContainsLoopError:
         return True


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   Node(1)
   lowerCamelCase__							:						Union[str, Any]							     =   Node(2)
   lowerCamelCase__							:						Union[str, Any]							     =   Node(3)
   lowerCamelCase__							:						Dict							     =   Node(4)
   print(root_node.has_loop)  # False
   lowerCamelCase__							:						List[str]							     =   root_node.next_node
   print(root_node.has_loop)  # True

   lowerCamelCase__							:						int							     =   Node(5)
   lowerCamelCase__							:						List[Any]							     =   Node(6)
   lowerCamelCase__							:						Dict							     =   Node(5)
   lowerCamelCase__							:						Optional[int]							     =   Node(6)
   print(root_node.has_loop)  # False

   lowerCamelCase__							:						Optional[int]							     =   Node(1)
   print(root_node.has_loop)  # False





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

