





'''simple docstring'''







import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SegformerImageProcessor


class        __a   (unittest.TestCase  ):

							def __init__(				self				:  List[Any]							,  __magic_name__				:  int							,  __magic_name__				:  Dict=7							,  __magic_name__				:  List[Any]=3							,  __magic_name__				:  int=30							,  __magic_name__				:  Dict=4_00							,  __magic_name__				:  Union[str, Any]=True							,  __magic_name__				:  List[str]=None							,  __magic_name__				:  str=True							,  __magic_name__				:  Dict=[0.5, 0.5, 0.5]							,  __magic_name__				:  Optional[Any]=[0.5, 0.5, 0.5]							,  __magic_name__				:  List[str]=False							,  )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	size if size is not None else {'''height''': 30, '''width''': 30}
													UpperCAmelCase_      :   List[Any]      =	parent
													UpperCAmelCase_      :   str      =	batch_size
													UpperCAmelCase_      :   int      =	num_channels
													UpperCAmelCase_      :   List[str]      =	min_resolution
													UpperCAmelCase_      :   Any      =	max_resolution
													UpperCAmelCase_      :   Any      =	do_resize
													UpperCAmelCase_      :   List[str]      =	size
													UpperCAmelCase_      :   Optional[Any]      =	do_normalize
													UpperCAmelCase_      :   List[str]      =	image_mean
													UpperCAmelCase_      :   Optional[Any]      =	image_std
													UpperCAmelCase_      :   Any      =	do_reduce_labels



							def      UpperCAmelCase__						(				self				:  int )       ->							str:







													"""simple docstring"""
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_reduce_labels": self.do_reduce_labels,
													}





def 							lowerCamelCase_   (	)			->						str:
						UpperCAmelCase_      :   str      =	load_dataset('''hf-internal-testing/fixtures_ade20k''',      split='''test''' )

						UpperCAmelCase_      :   int      =	Image.open(dataset[0]['''file'''] )
						UpperCAmelCase_      :   List[str]      =	Image.open(dataset[1]['''file'''] )

						return image, map





def 							lowerCamelCase_   (	)			->						Dict:
						UpperCAmelCase_      :   List[Any]      =	load_dataset('''hf-internal-testing/fixtures_ade20k''',      split='''test''' )

						UpperCAmelCase_      :   str      =	Image.open(dataset[0]['''file'''] )
						UpperCAmelCase_      :   List[str]      =	Image.open(dataset[1]['''file'''] )
						UpperCAmelCase_      :   Optional[Any]      =	Image.open(dataset[2]['''file'''] )
						UpperCAmelCase_      :   Optional[int]      =	Image.open(dataset[3]['''file'''] )

						return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : Dict          =							SegformerImageProcessor if is_vision_available() else None

							def      UpperCAmelCase__						(				self				:  int )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	SegformerImageProcessingTester(self )

							@property
							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							List[Any]:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	self.image_processing_class(**self.image_processor_dict )
													self.assertTrue(hasattr(__magic_name__							,  '''do_resize''' ) )
													self.assertTrue(hasattr(__magic_name__							,  '''size''' ) )
													self.assertTrue(hasattr(__magic_name__							,  '''do_normalize''' ) )
													self.assertTrue(hasattr(__magic_name__							,  '''image_mean''' ) )
													self.assertTrue(hasattr(__magic_name__							,  '''image_std''' ) )
													self.assertTrue(hasattr(__magic_name__							,  '''do_reduce_labels''' ) )

							def      UpperCAmelCase__						(				self				:  str )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	self.image_processing_class.from_dict(self.image_processor_dict )
													self.assertEqual(image_processor.size							,  {'''height''': 30, '''width''': 30} )
													self.assertEqual(image_processor.do_reduce_labels							,  __magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	self.image_processing_class.from_dict(self.image_processor_dict							,  size=42							,  reduce_labels=__magic_name__ )
													self.assertEqual(image_processor.size							,  {'''height''': 42, '''width''': 42} )
													self.assertEqual(image_processor.do_reduce_labels							,  __magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Any:







													"""simple docstring"""
													pass

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							Tuple:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_      :   Dict      =	self.image_processing_class(**self.image_processor_dict )
													# create random PIL images
													UpperCAmelCase_      :   List[str]      =	prepare_image_inputs(self.image_processor_tester							,  equal_resolution=__magic_name__ )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__							,  Image.Image )

													# Test not batched input
													UpperCAmelCase_      :   int      =	image_processing(image_inputs[0]							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

													# Test batched
													UpperCAmelCase_      :   Union[str, Any]      =	image_processing(__magic_name__							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_      :   List[Any]      =	self.image_processing_class(**self.image_processor_dict )
													# create random numpy tensors
													UpperCAmelCase_      :   Optional[int]      =	prepare_image_inputs(self.image_processor_tester							,  equal_resolution=__magic_name__							,  numpify=__magic_name__ )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__							,  np.ndarray )

													# Test not batched input
													UpperCAmelCase_      :   str      =	image_processing(image_inputs[0]							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

													# Test batched
													UpperCAmelCase_      :   Dict      =	image_processing(__magic_name__							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

							def      UpperCAmelCase__						(				self				:  Any )       ->							List[Any]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_      :   Any      =	self.image_processing_class(**self.image_processor_dict )
													# create random PyTorch tensors
													UpperCAmelCase_      :   str      =	prepare_image_inputs(self.image_processor_tester							,  equal_resolution=__magic_name__							,  torchify=__magic_name__ )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__							,  torch.Tensor )

													# Test not batched input
													UpperCAmelCase_      :   int      =	image_processing(image_inputs[0]							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

													# Test batched
													UpperCAmelCase_      :   Any      =	image_processing(__magic_name__							,  return_tensors='''pt''' ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,  (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )

							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Optional[Any]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_      :   int      =	self.image_processing_class(**self.image_processor_dict )
													# create random PyTorch tensors
													UpperCAmelCase_      :   int      =	prepare_image_inputs(self.image_processor_tester							,  equal_resolution=__magic_name__							,  torchify=__magic_name__ )
													UpperCAmelCase_      :   Tuple      =	[]
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__							,  torch.Tensor )
																			maps.append(torch.zeros(image.shape[-2:] ).long() )

													# Test not batched input
													UpperCAmelCase_      :   List[str]      =	image_processing(image_inputs[0]							,  maps[0]							,  return_tensors='''pt''' )
													self.assertEqual(
													    encoding['''pixel_values'''].shape							,  (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(
													    encoding['''labels'''].shape							,  (
													        1,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(encoding['''labels'''].dtype							,  torch.long )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

													# Test batched
													UpperCAmelCase_      :   int      =	image_processing(__magic_name__							,  __magic_name__							,  return_tensors='''pt''' )
													self.assertEqual(
													    encoding['''pixel_values'''].shape							,  (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(
													    encoding['''labels'''].shape							,  (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(encoding['''labels'''].dtype							,  torch.long )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

													# Test not batched input (PIL images)
													UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_semantic_single_inputs()

													UpperCAmelCase_      :   Tuple      =	image_processing(__magic_name__							,  __magic_name__							,  return_tensors='''pt''' )
													self.assertEqual(
													    encoding['''pixel_values'''].shape							,  (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(
													    encoding['''labels'''].shape							,  (
													        1,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(encoding['''labels'''].dtype							,  torch.long )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

													# Test batched input (PIL images)
													UpperCAmelCase_    ,							UpperCAmelCase_      :   str      =	prepare_semantic_batch_inputs()

													UpperCAmelCase_      :   Any      =	image_processing(__magic_name__							,  __magic_name__							,  return_tensors='''pt''' )
													self.assertEqual(
													    encoding['''pixel_values'''].shape							,  (
													        2,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(
													    encoding['''labels'''].shape							,  (
													        2,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )							,  )
													self.assertEqual(encoding['''labels'''].dtype							,  torch.long )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )



							def      UpperCAmelCase__						(				self				:  Any )       ->							Tuple:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_      :   Any      =	self.image_processing_class(**self.image_processor_dict )

													# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
													UpperCAmelCase_    ,							UpperCAmelCase_      :   Dict      =	prepare_semantic_single_inputs()
													UpperCAmelCase_      :   Tuple      =	image_processing(__magic_name__							,  __magic_name__							,  return_tensors='''pt''' )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 1_50 )

													UpperCAmelCase_      :   Any      =	True
													UpperCAmelCase_      :   str      =	image_processing(__magic_name__							,  __magic_name__							,  return_tensors='''pt''' )
													self.assertTrue(encoding['''labels'''].min().item() >= 0 )
													self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )









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 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= UNetaDModel(
           block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')							, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')							, )
       UpperCAmelCase_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   List[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        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= 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        _lowerCamelCase			(						self					: Optional[int]						):





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





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





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





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





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





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





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





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





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





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






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





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





    def        _lowerCamelCase			(						self					: List[str]						):





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

       UpperCAmelCase_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

       UpperCAmelCase_				:   Optional[int]							= torch.manual_seed(0						)
       UpperCAmelCase_				:   List[str]							= repaint(
           __snake_case							, __snake_case							, num_inference_steps=250							, eta=0.0							, jump_length=10							, jump_n_sample=10							, generator=__snake_case							, output_type='''np'''							, )
       UpperCAmelCase_				:   Tuple							= output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image						).mean() < 1E-2