






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	: Union[str, Any]      =				get_logger(__name__)






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










class 					a					:



      """simple docstring"""


      def __init__(   self      :    int	,   __lowercase      :    int      )							->   None:
        __UpperCAmelCase							:			str     =						data
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None






def        lowerCamelCase__   (					__lowerCamelCase    :  BinaryTreeNode | None						,	__lowerCamelCase    :  int	):
  if node is None:
    __UpperCAmelCase							:			Optional[int]     =						BinaryTreeNode(__lowerCamelCase	)
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase							:			int     =						insert(node.left_child						,	__lowerCamelCase	)
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase							:			Dict     =						insert(node.right_child						,	__lowerCamelCase	)
  return node






def        lowerCamelCase__   (					__lowerCamelCase    :  None | BinaryTreeNode	):  # if node is None,return
  if node:
    __UpperCAmelCase							:			Optional[Any]     =						inorder(node.left_child	)
    __UpperCAmelCase							:			str     =						[*inorder_array, node.data]
    __UpperCAmelCase							:			Optional[Any]     =						inorder_array + inorder(node.right_child	)
  else:
    __UpperCAmelCase							:			Tuple     =						[]
  return inorder_array






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Any     =						insert(__lowerCamelCase						,	15	)
  insert(__lowerCamelCase						,	10	)
  insert(__lowerCamelCase						,	25	)
  insert(__lowerCamelCase						,	6	)
  insert(__lowerCamelCase						,	14	)
  insert(__lowerCamelCase						,	20	)
  insert(__lowerCamelCase						,	60	)
  return root






def        lowerCamelCase__   (					):
  # main function
  __UpperCAmelCase							:			int     =						make_tree()
  print("""Printing values of binary search tree in Inorder Traversal."""	)
  inorder(__lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


