code: 
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Tuple										=     [True for i in range(n + 1 )]
      UpperCamelCase :List[Any]										=     [i - 1 for i in range(n + 1 )]
      UpperCamelCase :Union[str, Any]										=     []
      for i in range(2  ,						n + 1 ):
          if is_prime[i]:
              primes.append(SCREAMING_SNAKE_CASE__ )
          for j in range(0  ,						len(SCREAMING_SNAKE_CASE__ ) ):
              if i * primes[j] >= n:
                  break
              UpperCamelCase :Dict										=     False
  
              if i % primes[j] == 0:
                  UpperCamelCase :str										=     totients[i] * primes[j]
                  break
  
              UpperCamelCase :str										=     totients[i] * (primes[j] - 1)
  
      return totients
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  