

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_pt_objects import *  # noqa F403
else:
       from .consistency_models import ConsistencyModelPipeline
       from .dance_diffusion import DanceDiffusionPipeline
       from .ddim import DDIMPipeline
       from .ddpm import DDPMPipeline
       from .dit import DiTPipeline
       from .latent_diffusion import LDMSuperResolutionPipeline
       from .latent_diffusion_uncond import LDMPipeline
       from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
       from .pndm import PNDMPipeline
       from .repaint import RePaintPipeline
       from .score_sde_ve import ScoreSdeVePipeline
       from .stochastic_karras_ve import KarrasVePipeline

try:
       if not (is_torch_available() and is_librosa_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
       from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
       if not (is_torch_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
       from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
       from .audioldm import AudioLDMPipeline
       from .controlnet import (
           StableDiffusionControlNetImgaImgPipeline,
           StableDiffusionControlNetInpaintPipeline,
           StableDiffusionControlNetPipeline,
       )
       from .deepfloyd_if import (
           IFImgaImgPipeline,
           IFImgaImgSuperResolutionPipeline,
           IFInpaintingPipeline,
           IFInpaintingSuperResolutionPipeline,
           IFPipeline,
           IFSuperResolutionPipeline,
       )
       from .kandinsky import (
           KandinskyImgaImgPipeline,
           KandinskyInpaintPipeline,
           KandinskyPipeline,
           KandinskyPriorPipeline,
       )
       from .kandinskya_a import (
           KandinskyVaaControlnetImgaImgPipeline,
           KandinskyVaaControlnetPipeline,
           KandinskyVaaImgaImgPipeline,
           KandinskyVaaInpaintPipeline,
           KandinskyVaaPipeline,
           KandinskyVaaPriorEmbaEmbPipeline,
           KandinskyVaaPriorPipeline,
       )
       from .latent_diffusion import LDMTextToImagePipeline
       from .paint_by_example import PaintByExamplePipeline
       from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
       from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
       from .stable_diffusion import (
           CycleDiffusionPipeline,
           StableDiffusionAttendAndExcitePipeline,
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionImageVariationPipeline,
           StableDiffusionImgaImgPipeline,
           StableDiffusionInpaintPipeline,
           StableDiffusionInpaintPipelineLegacy,
           StableDiffusionInstructPixaPixPipeline,
           StableDiffusionLatentUpscalePipeline,
           StableDiffusionLDMaDPipeline,
           StableDiffusionModelEditingPipeline,
           StableDiffusionPanoramaPipeline,
           StableDiffusionParadigmsPipeline,
           StableDiffusionPipeline,
           StableDiffusionPixaPixZeroPipeline,
           StableDiffusionSAGPipeline,
           StableDiffusionUpscalePipeline,
           StableUnCLIPImgaImgPipeline,
           StableUnCLIPPipeline,
       )
       from .stable_diffusion_safe import StableDiffusionPipelineSafe
       from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
       from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
       from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
       from .versatile_diffusion import (
           VersatileDiffusionDualGuidedPipeline,
           VersatileDiffusionImageVariationPipeline,
           VersatileDiffusionPipeline,
           VersatileDiffusionTextToImagePipeline,
       )
       from .vq_diffusion import VQDiffusionPipeline


try:
       if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
       from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
       if not is_onnx_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_onnx_objects import *  # noqa F403
else:
       from .onnx_utils import OnnxRuntimeModel

try:
       if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
       from .stable_diffusion import (
           OnnxStableDiffusionImgaImgPipeline,
           OnnxStableDiffusionInpaintPipeline,
           OnnxStableDiffusionInpaintPipelineLegacy,
           OnnxStableDiffusionPipeline,
           OnnxStableDiffusionUpscalePipeline,
           StableDiffusionOnnxPipeline,
       )

try:
       if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
       from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_objects import *  # noqa F403
else:
       from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
       if not (is_flax_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
       from .controlnet import FlaxStableDiffusionControlNetPipeline
       from .stable_diffusion import (
           FlaxStableDiffusionImgaImgPipeline,
           FlaxStableDiffusionInpaintPipeline,
           FlaxStableDiffusionPipeline,
       )
try:
       if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
       from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




