

def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
