


from __future__ import annotations

UpperCAmelCase__   	=							[True] * 100_0001
UpperCAmelCase__   	=							2
while i * i <= 100_0000:
  if seive[i]:
    for j in range(i * i, 100_0001, i):
      UpperCAmelCase__   	=							False
  i += 1
def 				UpperCAmelCase_					(							__snake_case						)     ->			bool:







 """simple docstring"""


 return seive[n]
def 				UpperCAmelCase_					(							__snake_case						)     ->			bool:







 """simple docstring"""


 return any(digit in '''02468''' for digit in str(__snake_case						)						)
def 				UpperCAmelCase_					(							__snake_case = 1000000						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[2]  # result already includes the number 2.
 for num in range(3     ,						limit + 1     ,						2						):
  if is_prime(__snake_case						) and not contains_an_even_digit(__snake_case						):
   _lowercase							      =str(__snake_case						)
   _lowercase							      =[int(str_num[j:] + str_num[:j]						) for j in range(len(__snake_case						)						)]
   if all(is_prime(__snake_case						) for i in list_nums						):
    result.append(__snake_case						)
 return result
def 				UpperCAmelCase_					(							)     ->			int:







 """simple docstring"""


 return len(find_circular_primes()						)


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

