






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()













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__)





