





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       :				Dict           =			get_logger(__name__)
def  __UpperCamelCase    (						snake_case      )    ->		List[Any]:


   '''simple docstring'''







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









'''simple docstring'''





from __future__ import annotations

from queue import Queue
class 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     lowerCamelCase__	=  {}

    def  a__					(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     for i in self.vertices:
      print(__lowerCamelCase					,  " : "					,  " -> ".join([str(__lowerCamelCase  ) for j in self.vertices[i]]  )  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  int  )				->				None:

     '''simple docstring'''




     if from_vertex in self.vertices:
      self.vertices[from_vertex].append(__lowerCamelCase  )
     else:
      lowerCamelCase__	=  [to_vertex]




    def  a__					(       self  :  Optional[int]					,  __lowerCamelCase  :  int  )				->				set[int]:

     '''simple docstring'''




     lowerCamelCase__	=  set()

     # create a first in first out queue to store all the vertices for BFS
     lowerCamelCase__	=  Queue()

     # mark the source node as visited and enqueue it
     visited.add(__lowerCamelCase  )
     queue.put(__lowerCamelCase  )

     while not queue.empty():
      lowerCamelCase__	=  queue.get()

      # loop through all adjacent vertex and enqueue it if not yet visited
      for adjacent_vertex in self.vertices[vertex]:
       if adjacent_vertex not in visited:
        queue.put(__lowerCamelCase  )
        visited.add(__lowerCamelCase  )
     return visited


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)

 __A				:				Optional[Any]					=		Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 # 0  :  1 -> 2
 # 1  :  2
 # 2  :  0 -> 3
 # 3  :  3

 assert sorted(g.bfs(2)) == [0, 1, 2, 3]

