"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for i, input_name in enumerate(node_proto.input       ):
								if input_name == name:
												node_proto.input.insert(lowerCamelCase					,			lowerCamelCase       )
												node_proto.input.pop(i + 1       )

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )
								_graph_replace_input_with(node_proto.attribute[1].g					,			lowerCamelCase					,			lowerCamelCase       )
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							list(model_without_ext.graph.initializer       )
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]       )

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

				UpperCAmelCase__             =							set()
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							[]

				UpperCAmelCase__             =							0

				for i in range(len(lowerCamelCase       )       ):
								if i in dup_set:
												continue

								for j in range(i + 1					,			len(lowerCamelCase       )       ):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]					,			inits[j]       ):
																dup_set.add(lowerCamelCase       )
																dup_set.add(lowerCamelCase       )

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							numpy.prod(inits[j].dims       )
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

																UpperCAmelCase__             =							inits[i].name
																UpperCAmelCase__             =							inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[name_j]
																ind_to_replace.append((j, i)       )

				print('total reduced size: '					,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'GB'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model






a__				=		'''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



