


'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






      def __UpperCAmelCase	(			self      :List[Any]     )	-> List[str]:




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			List[Any]					 =(image > 0).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			str					 ={
                 """image""": image,
                 """mask_image""": mask,
                 """generator""": generator,
                 """num_inference_steps""": 5,
                 """eta""": 0.0,
                 """jump_length""": 2,
                 """jump_n_sample""": 2,
                 """output_type""": """numpy""",
             }
             return inputs






      def __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =self.get_dummy_inputs(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =sd_pipe(**SCREAMING_SNAKE_CASE     ).images
             _a   :			Any					 =image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






      @skip_mps
      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             return super().test_save_load_local()






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




             '''simple docstring'''
             return super().test_inference_batch_single_identical()






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




             '''simple docstring'''
             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def __UpperCAmelCase	(			self      :str     )	-> Optional[Any]:




             '''simple docstring'''
             return super().test_save_load_optional_components()






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class A__	(						unittest.TestCase				):






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




             '''simple docstring'''
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Optional[int]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256.png"""     )
             _a   :			Optional[Any]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""     )
             _a   :			Union[str, Any]					 =load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256_result.npy"""     )

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

             _a   :			str					 =RePaintPipeline(unet=SCREAMING_SNAKE_CASE					,						scheduler=SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )
             repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )
             repaint.enable_attention_slicing()

             _a   :			Dict					 =torch.manual_seed(0     )
             _a   :			Any					 =repaint(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						num_inference_steps=2_5_0					,						eta=0.0					,						jump_length=1_0					,						jump_n_sample=1_0					,						generator=SCREAMING_SNAKE_CASE					,						output_type="""np"""					,						)
             _a   :			Any					 =output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image     ).mean() < 1e-2









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


A__:   Dict    =	'''\
@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}
}
'''

A__:   Union[str, Any]    =	'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


A__:   str    =	'''
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 __UpperCAmelCase	(			self      :List[Any]     )	-> Dict:




             '''simple docstring'''
             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 __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             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 __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Any="uniform_average"     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =mean_absolute_error(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE					,						multioutput=SCREAMING_SNAKE_CASE     )

             return {"mae": mae_score}


