

from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class      lowerCamelCase_ :
 _lowerCAmelCase									=	42
 _lowerCAmelCase									=	None
 _lowerCAmelCase									=	None





def  _snake_case		()							->		TreeNode:
  return TreeNode(3    ,       TreeNode(9  )    ,       TreeNode(20    ,       TreeNode(15  )    ,       TreeNode(7  )  )  )





def  _snake_case		(snake_case_					:     List[Any]  )							->		list[int]:

  def depth_first_search(
      snake_case_					:     Tuple    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     str  ) -> None:
    if not root:
      return

    if depth == len(a_  ):
      right_view.append(root.val  )

    depth_first_search(root.right    ,       depth + 1    ,       a_  )
    depth_first_search(root.left    ,       depth + 1    ,       a_  )

  __UpperCAmelCase	:  list		=     []
  if not root:
    return right_view

  depth_first_search(a_    ,       0    ,       a_  )
  return right_view





def  _snake_case		(snake_case_					:     Tuple  )							->		list[int]:

  def depth_first_search(
      snake_case_					:     Optional[Any]    ,       snake_case_					:     List[str]    ,       snake_case_					:     Optional[Any]  ) -> None:
    if not root:
      return

    if depth == len(a_  ):
      left_view.append(root.val  )

    depth_first_search(root.left    ,       depth + 1    ,       a_  )
    depth_first_search(root.right    ,       depth + 1    ,       a_  )

  __UpperCAmelCase	:  list		=     []
  if not root:
    return left_view

  depth_first_search(a_    ,       0    ,       a_  )
  return left_view





def  _snake_case		(snake_case_					:     Optional[Any]  )							->		list[int]:

  def breadth_first_search(snake_case_					:     Dict    ,       snake_case_					:     Optional[int]  ) -> None:
    __UpperCAmelCase	:  Dict		=     [(root, 0)]
    __UpperCAmelCase	:  Union[str, Any]		=     defaultdict(a_  )

    while queue:
      __UpperCAmelCase	:  List[str]		=     queue.pop(0  )
      __UpperCAmelCase	:  int		=     first

      lookup[hd].append(node.val  )

      if node.left:
        queue.append((node.left, hd - 1)  )
      if node.right:
        queue.append((node.right, hd + 1)  )

    for pair in sorted(lookup.items()    ,       key=lambda snake_case_  : each[0]  ):
      top_view.append(pair[1][0]  )

  __UpperCAmelCase	:  list		=     []
  if not root:
    return top_view

  breadth_first_search(a_    ,       a_  )
  return top_view





def  _snake_case		(snake_case_					:     str  )							->		list[int]:
  from collections import defaultdict

  def breadth_first_search(snake_case_					:     Union[str, Any]    ,       snake_case_					:     int  ) -> None:
    __UpperCAmelCase	:  Optional[Any]		=     [(root, 0)]
    __UpperCAmelCase	:  List[str]		=     defaultdict(a_  )

    while queue:
      __UpperCAmelCase	:  str		=     queue.pop(0  )
      __UpperCAmelCase	:  Union[str, Any]		=     first
      lookup[hd].append(node.val  )

      if node.left:
        queue.append((node.left, hd - 1)  )
      if node.right:
        queue.append((node.right, hd + 1)  )

    for pair in sorted(lookup.items()    ,       key=lambda snake_case_  : each[0]  ):
      bottom_view.append(pair[1][-1]  )

  __UpperCAmelCase	:  list		=     []
  if not root:
    return bottom_view

  breadth_first_search(a_    ,       a_  )
  return bottom_view


if __name__ == "__main__":
      import doctest

      doctest.testmod()













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_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										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 :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

