


'''simple docstring'''



import os
import sys

A__		:					Any												=  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,
)


A__		:					Tuple												=  [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]



@add_start_docstrings(AutoConfig.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				Union[str, Any]      ,    **UpperCAmelCase_    :				Union[str, Any]							)     ->		Optional[int]:
     return AutoConfig.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				Optional[int]      ,    **UpperCAmelCase_    :				Any							)     ->		Optional[int]:
     return AutoTokenizer.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoModel.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				Dict      ,    **UpperCAmelCase_    :				Any							)     ->		Optional[int]:
     return AutoModel.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				str      ,    **UpperCAmelCase_    :				Tuple							)     ->		List[Any]:
     return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				List[str]      ,    **UpperCAmelCase_    :				Optional[int]							)     ->		Optional[int]:
     return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				Optional[int]      ,    **UpperCAmelCase_    :				Dict							)     ->		Optional[Any]:
     return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def 			UpperCAmelCase__					(	*UpperCAmelCase_    :				str      ,    **UpperCAmelCase_    :				Optional[Any]							)     ->		Tuple:
     return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase_      ,    **UpperCAmelCase_							)










'''simple docstring'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
      print(f'''{solution() = }''')






