"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						["""pixel_values"""]

				def __init__(				self		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = PILImageResampling.BILINEAR		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = True		,    lowerCAmelCase = 1 / 2_55		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )
										snake_case								=			size if size is not None else {'shortest_edge': 2_56}
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )
										snake_case								=			crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
										snake_case								=			get_size_dict(lowerCAmelCase    )
										snake_case								=			do_resize
										snake_case								=			size
										snake_case								=			resample
										snake_case								=			do_center_crop
										snake_case								=			crop_size
										snake_case								=			do_rescale
										snake_case								=			rescale_factor
										snake_case								=			do_normalize
										snake_case								=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case								=			image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = PILImageResampling.BICUBIC		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )
										if "shortest_edge" not in size:
																raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
										snake_case								=			get_resize_output_image_size(lowerCAmelCase		,    size=size['shortest_edge']		,    default_to_square=lowerCAmelCase    )
										return resize(lowerCAmelCase		,    size=lowerCAmelCase		,    resample=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			get_size_dict(lowerCAmelCase    )
										return center_crop(lowerCAmelCase		,    size=(size['height'], size['width'])		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return rescale(lowerCAmelCase		,    scale=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										return normalize(lowerCAmelCase		,    mean=lowerCAmelCase		,    std=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = ChannelDimension.FIRST		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			do_resize if do_resize is not None else self.do_resize
										snake_case								=			size if size is not None else self.size
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )
										snake_case								=			resample if resample is not None else self.resample
										snake_case								=			do_center_crop if do_center_crop is not None else self.do_center_crop
										snake_case								=			crop_size if crop_size is not None else self.crop_size
										snake_case								=			get_size_dict(lowerCAmelCase    )
										snake_case								=			do_rescale if do_rescale is not None else self.do_rescale
										snake_case								=			rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case								=			do_normalize if do_normalize is not None else self.do_normalize
										snake_case								=			image_mean if image_mean is not None else self.image_mean
										snake_case								=			image_std if image_std is not None else self.image_std

										snake_case								=			make_list_of_images(lowerCAmelCase    )

										if not valid_images(lowerCAmelCase    ):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

										if do_resize and size is None:
																raise ValueError('Size must be specified if do_resize is True.'    )

										if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'    )

										if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

										# All transformations expect numpy arrays.
										snake_case								=			[to_numpy_array(lowerCAmelCase    ) for image in images]

										if do_resize:
																snake_case								=			[self.resize(image=lowerCAmelCase		,    size=lowerCAmelCase		,    resample=lowerCAmelCase    ) for image in images]

										if do_center_crop:
																snake_case								=			[self.center_crop(image=lowerCAmelCase		,    size=lowerCAmelCase    ) for image in images]

										if do_rescale:
																snake_case								=			[self.rescale(image=lowerCAmelCase		,    scale=lowerCAmelCase    ) for image in images]

										if do_normalize:
																snake_case								=			[self.normalize(image=lowerCAmelCase		,    mean=lowerCAmelCase		,    std=lowerCAmelCase    ) for image in images]

										snake_case								=			[to_channel_dimension_format(lowerCAmelCase		,    lowerCAmelCase    ) for image in images]

										snake_case								=			{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase		,    tensor_type=lowerCAmelCase    )



"""simple docstring"""
SCREAMING_SNAKE_CASE__      						=  "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


