
'''simple docstring'''

UpperCamelCase_        =							"""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 sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION )
class 					snake_case			(datasets.Metric ):



       def 				_a   (					self   )	->				Optional[int]:
           return datasets.MetricInfo(
               description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types()   )  ,reference_urls=[
                   "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"   )   ),
                   "references": datasets.Sequence(datasets.Value("float"   )   ),
               }
           else:
               return {
                   "predictions": datasets.Value("float"   ),
                   "references": datasets.Value("float"   ),
               }







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}




