



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   __lowerCamelCase				(					__magic_name__		:     List[Any]  ):

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









UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


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