






"""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


UpperCamelCase_						:					Any      				=			get_logger(__name__)



def      __lowercase       (     a			:					str			)       ->       List[str]:


      if version.parse(lowerCAmelCase_			) < version.parse(lowerCAmelCase_			):
            if "dev" in min_version:
                  __snake_case   :     Any	      =(
                      """This example requires a source install from HuggingFace diffusers (see """
                      """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
                  )
            else:
                  __snake_case   :     Dict	      =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'''






from __future__ import annotations

A_				:					Tuple							=      """Alexander Joslin"""




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


       if number <= 0:
              _UpperCAmelCase    :		Union[str, Any]     	=					F'''n must be greater than 0. Got n = {number}'''
              raise ValueError(lowerCAmelCase_						)

       _UpperCAmelCase    :		List[str]     	=					[number + 1] * (number + 1)

       # starting position
       _UpperCAmelCase    :		Dict     	=					0
       for i in range(1			,  lowerCAmelCase_						):
              _UpperCAmelCase    :		int     	=					min(table[i + 1]			,  table[i] + 1						)
              # check if out of bounds
              if i * 2 <= number:
                     _UpperCAmelCase    :		Dict     	=					min(table[i * 2]			,  table[i] + 1						)
              # check if out of bounds
              if i * 3 <= number:
                     _UpperCAmelCase    :		Tuple     	=					min(table[i * 3]			,  table[i] + 1						)
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



