


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


UpperCAmelCase__            =							get_logger(__name__)
def     UpperCAmelCase_ (							__snake_case						)					->      Tuple:







 """simple docstring"""


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





from math import sqrt
def     UpperCAmelCase_ (							__snake_case						)					->      list[int]:







 """simple docstring"""


 _lowercase											=[1]
 for i in range(2					,						int(sqrt(__snake_case						)						) + 1					,						1						):
  if number % i == 0:
   values.append(__snake_case						)
   if int(number // i						) != i:
    values.append(int(number // i						)						)
 return sorted(__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return sum(factors(__snake_case						)						) > n
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 _lowercase											=factors(__snake_case						)
 _lowercase											=len(__snake_case						)
 _lowercase											=[[0 for i in range(number + 1						)] for j in range(r + 1						)]
 for i in range(r + 1						):
  _lowercase											=True

 for i in range(1					,						number + 1						):
  _lowercase											=False

 for i in range(1					,						r + 1						):
  for j in range(1					,						number + 1						):
   if j < values[i - 1]:
    _lowercase											=subset[i - 1][j]
   else:
    _lowercase											=subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

 return subset[r][number] != 0
def     UpperCAmelCase_ (							__snake_case						)					->      bool:







 """simple docstring"""


 return abundant(__snake_case						) and not semi_perfect(__snake_case						)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  for number in (69, 70, 71):
    print(f'''{number} is {'' if weird(number) else 'not '}weird.''')

