


'''simple docstring'''







A				:					Optional[int]      						=			'''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'''







import operator as op


A				:					List[str]    						=			'''scaler.pt'''
A				:					Optional[int]    						=			'''pytorch_model'''
A				:					Union[str, Any]    						=			'''random_states'''
A				:					List[Any]    						=			'''optimizer'''
A				:					Optional[Any]    						=			'''scheduler'''
A				:					Optional[int]    						=			'''pytorch_model.bin'''
A				:					List[str]    						=			'''pytorch_model.bin.index.json'''
A				:					List[Any]    						=			'''model.safetensors'''
A				:					int    						=			'''model.safetensors.index.json'''
A				:					List[str]    						=			'''1.10.2'''
A				:					str    						=			'''py38'''
A				:					int    						=			'''4.17.0'''
A				:					Tuple    						=			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A				:					Union[str, Any]    						=			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A				:					Optional[Any]    						=			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A				:					Dict    						=			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A				:					Any    						=			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A				:					Tuple    						=			'''2.0.1'''
A				:					List[str]    						=			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A				:					Union[str, Any]    						=			['''default''', '''reduce-overhead''', '''max-autotune''']

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






