


"""simple docstring"""







lowerCamelCase_   :   List[Any]									=  """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_pt_objects import *  # noqa F403
else:
       from .models import (
           AutoencoderKL,
           ControlNetModel,
           ModelMixin,
           PriorTransformer,
           TaFilmDecoder,
           TransformeraDModel,
           UNetaDModel,
           UNetaDConditionModel,
           UNetaDModel,
           UNetaDConditionModel,
           VQModel,
       )
       from .optimization import (
           get_constant_schedule,
           get_constant_schedule_with_warmup,
           get_cosine_schedule_with_warmup,
           get_cosine_with_hard_restarts_schedule_with_warmup,
           get_linear_schedule_with_warmup,
           get_polynomial_decay_schedule_with_warmup,
           get_scheduler,
       )
       from .pipelines import (
           AudioPipelineOutput,
           ConsistencyModelPipeline,
           DanceDiffusionPipeline,
           DDIMPipeline,
           DDPMPipeline,
           DiffusionPipeline,
           DiTPipeline,
           ImagePipelineOutput,
           KarrasVePipeline,
           LDMPipeline,
           LDMSuperResolutionPipeline,
           PNDMPipeline,
           RePaintPipeline,
           ScoreSdeVePipeline,
       )
       from .schedulers import (
           CMStochasticIterativeScheduler,
           DDIMInverseScheduler,
           DDIMParallelScheduler,
           DDIMScheduler,
           DDPMParallelScheduler,
           DDPMScheduler,
           DEISMultistepScheduler,
           DPMSolverMultistepInverseScheduler,
           DPMSolverMultistepScheduler,
           DPMSolverSinglestepScheduler,
           EulerAncestralDiscreteScheduler,
           EulerDiscreteScheduler,
           HeunDiscreteScheduler,
           IPNDMScheduler,
           KarrasVeScheduler,
           KDPMaAncestralDiscreteScheduler,
           KDPMaDiscreteScheduler,
           PNDMScheduler,
           RePaintScheduler,
           SchedulerMixin,
           ScoreSdeVeScheduler,
           UnCLIPScheduler,
           UniPCMultistepScheduler,
           VQDiffusionScheduler,
       )
       from .training_utils import EMAModel

try:
       if not (is_torch_available() and is_scipy_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
       from .schedulers import LMSDiscreteScheduler

try:
       if not (is_torch_available() and is_torchsde_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
       from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
           AltDiffusionImgaImgPipeline,
           AltDiffusionPipeline,
           AudioLDMPipeline,
           CycleDiffusionPipeline,
           IFImgaImgPipeline,
           IFImgaImgSuperResolutionPipeline,
           IFInpaintingPipeline,
           IFInpaintingSuperResolutionPipeline,
           IFPipeline,
           IFSuperResolutionPipeline,
           ImageTextPipelineOutput,
           KandinskyImgaImgPipeline,
           KandinskyInpaintPipeline,
           KandinskyPipeline,
           KandinskyPriorPipeline,
           KandinskyVaaControlnetImgaImgPipeline,
           KandinskyVaaControlnetPipeline,
           KandinskyVaaImgaImgPipeline,
           KandinskyVaaInpaintPipeline,
           KandinskyVaaPipeline,
           KandinskyVaaPriorEmbaEmbPipeline,
           KandinskyVaaPriorPipeline,
           LDMTextToImagePipeline,
           PaintByExamplePipeline,
           SemanticStableDiffusionPipeline,
           ShapEImgaImgPipeline,
           ShapEPipeline,
           StableDiffusionAttendAndExcitePipeline,
           StableDiffusionControlNetImgaImgPipeline,
           StableDiffusionControlNetInpaintPipeline,
           StableDiffusionControlNetPipeline,
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionImageVariationPipeline,
           StableDiffusionImgaImgPipeline,
           StableDiffusionInpaintPipeline,
           StableDiffusionInpaintPipelineLegacy,
           StableDiffusionInstructPixaPixPipeline,
           StableDiffusionLatentUpscalePipeline,
           StableDiffusionLDMaDPipeline,
           StableDiffusionModelEditingPipeline,
           StableDiffusionPanoramaPipeline,
           StableDiffusionParadigmsPipeline,
           StableDiffusionPipeline,
           StableDiffusionPipelineSafe,
           StableDiffusionPixaPixZeroPipeline,
           StableDiffusionSAGPipeline,
           StableDiffusionUpscalePipeline,
           StableUnCLIPImgaImgPipeline,
           StableUnCLIPPipeline,
           TextToVideoSDPipeline,
           TextToVideoZeroPipeline,
           UnCLIPImageVariationPipeline,
           UnCLIPPipeline,
           UniDiffuserModel,
           UniDiffuserPipeline,
           UniDiffuserTextDecoder,
           VersatileDiffusionDualGuidedPipeline,
           VersatileDiffusionImageVariationPipeline,
           VersatileDiffusionPipeline,
           VersatileDiffusionTextToImagePipeline,
           VideoToVideoSDPipeline,
           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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
           OnnxStableDiffusionImgaImgPipeline,
           OnnxStableDiffusionInpaintPipeline,
           OnnxStableDiffusionInpaintPipelineLegacy,
           OnnxStableDiffusionPipeline,
           OnnxStableDiffusionUpscalePipeline,
           StableDiffusionOnnxPipeline,
       )

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_flax_objects import *  # noqa F403
else:
       from .models.controlnet_flax import FlaxControlNetModel
       from .models.modeling_flax_utils import FlaxModelMixin
       from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
       from .models.vae_flax import FlaxAutoencoderKL
       from .pipelines import FlaxDiffusionPipeline
       from .schedulers import (
           FlaxDDIMScheduler,
           FlaxDDPMScheduler,
           FlaxDPMSolverMultistepScheduler,
           FlaxKarrasVeScheduler,
           FlaxLMSDiscreteScheduler,
           FlaxPNDMScheduler,
           FlaxSchedulerMixin,
           FlaxScoreSdeVeScheduler,
       )


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 .pipelines import (
           FlaxStableDiffusionControlNetPipeline,
           FlaxStableDiffusionImgaImgPipeline,
           FlaxStableDiffusionInpaintPipeline,
           FlaxStableDiffusionPipeline,
       )

try:
       if not (is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_note_seq_objects import *  # noqa F403
else:
       from .pipelines import MidiProcessor

'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



