
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 						__magic_name__				(  unittest.TestCase      ):





		def __init__(							self      :		Optional[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Union[str, Any]=7  ,					lowerCamelCase__      :		Optional[int]=3  ,					lowerCamelCase__      :		Dict=3_0  ,					lowerCamelCase__      :		Optional[int]=4_0_0  ,					lowerCamelCase__      :		Tuple=True  ,					lowerCamelCase__      :		Union[str, Any]=None  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		List[Any]=[0.5, 0.5, 0.5]  ,					lowerCamelCase__      :		Optional[int]=[0.5, 0.5, 0.5]  ,					lowerCamelCase__      :		Any=False  ,					):
							lowerCAmelCase						:   Optional[int]							=       size if size is not None else {"height": 3_0, "width": 3_0}
							lowerCAmelCase						:   Optional[Any]							=       parent
							lowerCAmelCase						:   Optional[Any]							=       batch_size
							lowerCAmelCase						:   List[str]							=       num_channels
							lowerCAmelCase						:   Optional[int]							=       min_resolution
							lowerCAmelCase						:   str							=       max_resolution
							lowerCAmelCase						:   Any							=       do_resize
							lowerCAmelCase						:   int							=       size
							lowerCAmelCase						:   Tuple							=       do_normalize
							lowerCAmelCase						:   Dict							=       image_mean
							lowerCAmelCase						:   Union[str, Any]							=       image_std
							lowerCAmelCase						:   Optional[int]							=       do_reduce_labels





		def  _A							(							self      :		str			):
							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       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       load_dataset('''hf-internal-testing/fixtures_ade20k'''							,					split='''test'''					)

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

					return image, map







def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   str							=       load_dataset('''hf-internal-testing/fixtures_ade20k'''							,					split='''test'''					)

					lowerCAmelCase						:   List[Any]							=       Image.open(dataset[0]['''file''']					)
					lowerCAmelCase						:   Optional[Any]							=       Image.open(dataset[1]['''file''']					)
					lowerCAmelCase						:   Tuple							=       Image.open(dataset[2]['''file''']					)
					lowerCAmelCase						:   Tuple							=       Image.open(dataset[3]['''file''']					)

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


@require_torch
@require_vision
class 						__magic_name__				(  __A,			unittest.TestCase      ):
		_lowerCAmelCase						=		SegformerImageProcessor if is_vision_available() else None





		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[str]							=       SegformerImageProcessingTester(self			)





		@property
		def  _A							(							self      :		int			):
							return self.image_processor_tester.prepare_image_processor_dict()





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Optional[int]							=       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''do_resize'''			)			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''size'''			)			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''do_normalize'''			)			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''image_mean'''			)			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''image_std'''			)			)
							self.assertTrue(hasattr(lowerCamelCase__  ,					'''do_reduce_labels'''			)			)





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   str							=       self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size  ,					{'''height''': 3_0, '''width''': 3_0}			)
							self.assertEqual(image_processor.do_reduce_labels  ,					lowerCamelCase__			)

							lowerCAmelCase						:   List[str]							=       self.image_processing_class.from_dict(self.image_processor_dict  ,					size=4_2  ,					reduce_labels=lowerCamelCase__			)
							self.assertEqual(image_processor.size  ,					{'''height''': 4_2, '''width''': 4_2}			)
							self.assertEqual(image_processor.do_reduce_labels  ,					lowerCamelCase__			)





		def  _A							(							self      :		Union[str, Any]			):
							pass





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Union[str, Any]							=       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							lowerCAmelCase						:   Optional[Any]							=       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=lowerCamelCase__			)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__  ,					Image.Image			)

							# Test not batched input
							lowerCAmelCase						:   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
							lowerCAmelCase						:   Tuple							=       image_processing(lowerCamelCase__  ,					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  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   List[Any]							=       self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							lowerCAmelCase						:   List[str]							=       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=lowerCamelCase__  ,					numpify=lowerCamelCase__			)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__  ,					np.ndarray			)

							# Test not batched input
							lowerCAmelCase						:   Any							=       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
							lowerCAmelCase						:   str							=       image_processing(lowerCamelCase__  ,					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  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   List[Any]							=       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							lowerCAmelCase						:   Tuple							=       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=lowerCamelCase__  ,					torchify=lowerCamelCase__			)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__  ,					torch.Tensor			)

							# Test not batched input
							lowerCAmelCase						:   Dict							=       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
							lowerCAmelCase						:   Tuple							=       image_processing(lowerCamelCase__  ,					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  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   Optional[int]							=       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							lowerCAmelCase						:   Tuple							=       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=lowerCamelCase__  ,					torchify=lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       []
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__  ,					torch.Tensor			)
												maps.append(torch.zeros(image.shape[-2:]			).long()			)

							# Test not batched input
							lowerCAmelCase						:   Tuple							=       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_5_5			)

							# Test batched
							lowerCAmelCase						:   Tuple							=       image_processing(lowerCamelCase__  ,					lowerCamelCase__  ,					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_5_5			)

							# Test not batched input (PIL images)
							lowerCAmelCase						:   Optional[Any]							=       prepare_semantic_single_inputs()

							lowerCAmelCase						:   List[str]							=       image_processing(lowerCamelCase__  ,					lowerCamelCase__  ,					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_5_5			)

							# Test batched input (PIL images)
							lowerCAmelCase						:   List[Any]							=       prepare_semantic_batch_inputs()

							lowerCAmelCase						:   str							=       image_processing(lowerCamelCase__  ,					lowerCamelCase__  ,					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_5_5			)





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Union[str, 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
							lowerCAmelCase						:   List[Any]							=       prepare_semantic_single_inputs()
							lowerCAmelCase						:   Tuple							=       image_processing(lowerCamelCase__  ,					lowerCamelCase__  ,					return_tensors='''pt'''			)
							self.assertTrue(encoding['''labels'''].min().item() >= 0			)
							self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0			)

							lowerCAmelCase						:   List[str]							=       True
							lowerCAmelCase						:   Optional[int]							=       image_processing(lowerCamelCase__  ,					lowerCamelCase__  ,					return_tensors='''pt'''			)
							self.assertTrue(encoding['''labels'''].min().item() >= 0			)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)


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 						__magic_name__				(  snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		RePaintPipeline
		_lowerCAmelCase						=		IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
		_lowerCAmelCase						=		PipelineTesterMixin.required_optional_params - {
		    "latents",
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		_lowerCAmelCase						=		IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase						=		False





		def  _A							(							self      :		Optional[int]			):
							torch.manual_seed(0			)
							torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       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''')  ,					)
							lowerCAmelCase						:   List[Any]							=       RePaintScheduler()
							lowerCAmelCase						:   Tuple							=       {'''unet''': unet, '''scheduler''': scheduler}
							return components





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		int=0			):
							if str(lowerCamelCase__			).startswith('''mps'''			):
												lowerCAmelCase						:   Union[str, Any]							=       torch.manual_seed(lowerCamelCase__			)
							else:
												lowerCAmelCase						:   Optional[Any]							=       torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       np.random.RandomState(lowerCamelCase__			).standard_normal((1, 3, 3_2, 3_2)			)
							lowerCAmelCase						:   Tuple							=       torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   Tuple							=       (image > 0).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   List[Any]							=       {
							    '''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  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase						:   Dict							=       self.get_dummy_components()
							lowerCAmelCase						:   str							=       RePaintPipeline(**lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							lowerCAmelCase						:   List[Any]							=       self.get_dummy_inputs(lowerCamelCase__			)
							lowerCAmelCase						:   int							=       sd_pipe(**lowerCamelCase__			).images
							lowerCAmelCase						:   Dict							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							lowerCAmelCase						:   List[str]							=       np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]			)

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





		@skip_mps
		def  _A							(							self      :		Optional[Any]			):
							return super().test_save_load_local()





		@unittest.skip('''non-deterministic pipeline'''			)
		def  _A							(							self      :		Any			):
							return super().test_inference_batch_single_identical()





		@skip_mps
		def  _A							(							self      :		List[Any]			):
							return super().test_dict_tuple_outputs_equivalent()





		@skip_mps
		def  _A							(							self      :		Dict			):
							return super().test_save_load_optional_components()





		@skip_mps
		def  _A							(							self      :		Tuple			):
							return super().test_attention_slicing_forward_pass()



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





		def  _A							(							self      :		Optional[Any]			):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   str							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256.png'''			)
							lowerCAmelCase						:   Tuple							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
							lowerCAmelCase						:   int							=       load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256_result.npy'''			)

							lowerCAmelCase						:   List[str]							=       '''google/ddpm-ema-celebahq-256'''
							lowerCAmelCase						:   List[str]							=       UNetaDModel.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   str							=       RePaintScheduler.from_pretrained(lowerCamelCase__			)

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

							lowerCAmelCase						:   str							=       torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       repaint(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					num_inference_steps=2_5_0  ,					eta=0.0  ,					jump_length=1_0  ,					jump_n_sample=1_0  ,					generator=lowerCamelCase__  ,					output_type='''np'''  ,					)
							lowerCAmelCase						:   str							=       output.images[0]

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