






"""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			=      get_logger(__name__)






def     A(  snake_case_		):
       """simple docstring"""





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














"""simple docstring"""





class 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_=None)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    str        =      data
          lowercase__:    Any        =      None





   def __repr__(   self)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      ""
          lowercase__:    Dict        =      self
          while temp:
                 string_rep += F"""<{temp.data}> ---> """
                 lowercase__:    Tuple        =      temp.next
          string_rep += "<END>"
          return string_rep







def     A(  snake_case_		):
       """simple docstring"""





       if not elements_list:
              raise Exception("The Elements List is empty"		)

       # Set first element as Head
       lowercase__:    Any        =      Node(elements_list[0]		)
       lowercase__:    Optional[int]        =      head
       # Loop through elements from position 1
       for data in elements_list[1:]:
              lowercase__:    Dict        =      Node(snake_case_		)
              lowercase__:    Dict        =      current.next
       return head


UpperCamelCase			=      [1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
UpperCamelCase			=      make_linked_list(list_data)
print("""Linked List:""")







print(linked_list)






