


'''simple docstring'''







from sklearn.metrics import accuracy_score

import datasets


A__:   List[str]    =	'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


A__:   Tuple    =	'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


A__:   Tuple    =	'''
@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}
}
'''







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






      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Dict:




             '''simple docstring'''
             return datasets.MetricInfo(
                 description=_DESCRIPTION					,						citation=_CITATION					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(
                     {
                         """predictions""": datasets.Sequence(datasets.Value("""int32"""     )     ),
                         """references""": datasets.Sequence(datasets.Value("""int32"""     )     ),
                     }
                     if self.config_name == """multilabel"""
                     else {
                         """predictions""": datasets.Value("""int32"""     ),
                         """references""": datasets.Value("""int32"""     ),
                     }     )					,						reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""]					,						)






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Optional[Any]=True					,						SCREAMING_SNAKE_CASE      :str=None     )	-> Optional[int]:




             '''simple docstring'''
             return {
                 "accuracy": float(
                     accuracy_score(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						normalize=SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE     )     )
             }






'''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


