
"""simple docstring"""







from sklearn.metrics import mean_absolute_error

import datasets


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

lowerCamelCase__     =						"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCamelCase__     =						"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




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

		def 				__lowerCamelCase				( self					):
			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_absolute_error.html'
			    ]			,						)

		def 				__lowerCamelCase				( self					):
			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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE="uniform_average"					):
			__lowerCAmelCase     :		str		     = mean_absolute_error(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						sample_weight=_SCREAMING_SNAKE_CASE			,						multioutput=_SCREAMING_SNAKE_CASE					)

			return {"mae": mae_score}

"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_pt_objects import *  # noqa F403
else:
							from .consistency_models import ConsistencyModelPipeline
							from .dance_diffusion import DanceDiffusionPipeline
							from .ddim import DDIMPipeline
							from .ddpm import DDPMPipeline
							from .dit import DiTPipeline
							from .latent_diffusion import LDMSuperResolutionPipeline
							from .latent_diffusion_uncond import LDMPipeline
							from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
							from .pndm import PNDMPipeline
							from .repaint import RePaintPipeline
							from .score_sde_ve import ScoreSdeVePipeline
							from .stochastic_karras_ve import KarrasVePipeline

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 .audio_diffusion import AudioDiffusionPipeline, Mel

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 .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
							from .audioldm import AudioLDMPipeline
							from .controlnet import (
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							)
							from .deepfloyd_if import (
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							)
							from .kandinsky import (
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							)
							from .kandinskya_a import (
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							)
							from .latent_diffusion import LDMTextToImagePipeline
							from .paint_by_example import PaintByExamplePipeline
							from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
							from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
							from .stable_diffusion import (
							    CycleDiffusionPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							)
							from .stable_diffusion_safe import StableDiffusionPipelineSafe
							from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
							from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
							from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
							from .versatile_diffusion import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
							from .vq_diffusion import 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 .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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

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

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 .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_objects import *  # noqa F403
else:
							from .pipeline_flax_utils import FlaxDiffusionPipeline


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 .controlnet import FlaxStableDiffusionControlNetPipeline
							from .stable_diffusion import (
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)
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 .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline