


'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


a_       :     Dict 							=     get_logger(__name__)






def 						_A					(lowerCAmelCase__	:Dict		) ->	Dict:




     '''simple docstring'''



     if version.parse(lowerCAmelCase__		) < version.parse(lowerCAmelCase__		):
          if "dev" in min_version:
               _a         =				(
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               _a         =				f'This example requires a minimum version of {min_version},'
          error_message += f' but the version found is {__version__}.\n'
          raise ImportError(lowerCAmelCase__		)





'''simple docstring'''






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if a < b:
          return gcd(lowerCAmelCase__					,    lowerCAmelCase__		)
     if a % b == 0:
          return b
     return gcd(lowerCAmelCase__					,    a % b		)






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if y == 0:
          return 1
     _a         =				power(lowerCAmelCase__					,    y // 2					,    lowerCAmelCase__		) % mod
     _a         =				(temp * temp) % mod
     if y % 2 == 1:
          _a         =				(temp * x) % mod
     return temp






def 						_A					(lowerCAmelCase__	:int		) ->	bool:




     '''simple docstring'''



     _a         =				2
     while b < n:
          if gcd(lowerCAmelCase__					,    lowerCAmelCase__		) == 1 and power(lowerCAmelCase__					,    n - 1					,    lowerCAmelCase__		) != 1:
               return False
          b += 1
     return True


if __name__ == "__main__":
 a_       :     Any 							=     int(input("Enter number: ").strip())
 if is_carmichael_number(number):
  print(f'''{number} is a Carmichael Number.''')
 else:
  print(f'''{number} is not a Carmichael Number.''')

