'''simple docstring'''




import os
import sys

_A								=   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								=   [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]

@add_start_docstrings(AutoConfig.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

@add_start_docstrings(AutoTokenizer.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

@add_start_docstrings(AutoModel.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def _UpperCamelCase   (       *SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							):
     return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)

'''simple docstring'''




import math

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5  ,       int(math.sqrt(SCREAMING_SNAKE_CASE_							) + 1							)  ,       6							):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_ = 10_001							):

     try:
          lowercase_					:       List[Any]     				=					int(SCREAMING_SNAKE_CASE_							)
     except (TypeError, ValueError):
          raise TypeError('Parameter nth must be int or castable to int.'							) from None
     if nth <= 0:
          raise ValueError('Parameter nth must be greater than or equal to one.'							)
     lowercase_					:       list[int]     				=					[]
     lowercase_					:       Optional[int]     				=					2
     while len(SCREAMING_SNAKE_CASE_							) < nth:
          if is_prime(SCREAMING_SNAKE_CASE_							):
               primes.append(SCREAMING_SNAKE_CASE_							)
               num += 1
          else:
               num += 1
     return primes[len(SCREAMING_SNAKE_CASE_							) - 1]


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




