
import unittest

import numpy as np

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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor




def 						A_			(				snake_case				: List[str]			,   snake_case				: Tuple=10			,   snake_case				: Tuple=10			,   snake_case				: Union[str, Any]=False			,   snake_case				: int=False			)   ->     Tuple:



						'''simple docstring'''

						__UpperCamelCase  	=      []
						for i in range(image_processor_tester.num_frames			):
												video.append(np.random.randint(255			,   size=(image_processor_tester.num_channels, width, height)			,   dtype=np.uinta			)			)

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												__UpperCamelCase  	=      [Image.fromarray(np.moveaxis(snake_case			,   0			,   -1			)			) for frame in video]

						if torchify:
												__UpperCamelCase  	=      [torch.from_numpy(snake_case			) for frame in video]

						return video




def 						A_			(				snake_case				: List[Any]			,   snake_case				: int=False			,   snake_case				: Optional[Any]=False			,   snake_case				: Any=False			)   ->     Any:



						'''simple docstring'''

						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						__UpperCamelCase  	=      []
						for i in range(image_processor_tester.batch_size			):
												if equal_resolution:
																		__UpperCamelCase  	=      __UpperCamelCase  	=      image_processor_tester.max_resolution
												else:
																		__UpperCamelCase	,				__UpperCamelCase  	=      np.random.choice(
																		    np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution			)			,   2			)
																		__UpperCamelCase  	=      prepare_video(
																		    image_processor_tester=snake_case			,   width=snake_case			,   height=snake_case			,   numpify=snake_case			,   torchify=snake_case			,   )
												video_inputs.append(snake_case			)

						return video_inputs






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=30  ,						SCREAMING_SNAKE_CASE_=400  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      size if size is not None else {'''shortest_edge''': 18}
													__UpperCamelCase  	=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      num_frames
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      min_resolution
													__UpperCamelCase  	=      max_resolution
													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean
													__UpperCamelCase  	=      image_std
													__UpperCamelCase  	=      do_center_crop
													__UpperCamelCase  	=      crop_size



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													}






@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	TvltImageProcessor if is_vision_available() else None



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      TvltImageProcessorTester(self      )



							@property
							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													return self.image_processor_tester.prepare_image_processor_dict()



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_mean'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_std'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_normalize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_resize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''size'''      )      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random PIL videos
													__UpperCamelCase  	=      prepare_video_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_      )
													for video in video_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(video[0]  ,						Image.Image      )

													# Test not batched input
													__UpperCamelCase  	=      image_processor(video_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													__UpperCamelCase  	=      prepare_video_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_  ,						numpify=SCREAMING_SNAKE_CASE_      )
													for video in video_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(video[0]  ,						np.ndarray      )

													# Test not batched input
													__UpperCamelCase  	=      image_processor(video_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



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





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													__UpperCamelCase  	=      prepare_video_inputs(self.image_processor_tester  ,						equal_resolution=SCREAMING_SNAKE_CASE_  ,						torchify=SCREAMING_SNAKE_CASE_      )
													for video in video_inputs:
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(video[0]  ,						torch.Tensor      )

													# Test not batched input
													__UpperCamelCase  	=      image_processor(video_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        1,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_videos.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_frames,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)










'''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 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Any										=				RePaintPipeline
  UpperCamelCase_			:      int										=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  UpperCamelCase_			:      List[str]										=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  UpperCamelCase_			:      List[str]										=				IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase_			:      str										=				False



  def 					_snake_case     ( self				:							Dict	)			->		Optional[Any]:







       '''simple docstring'''



       torch.manual_seed(0	)
       torch.manual_seed(0	)
       A:		Optional[Any]          =		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''')					,  )
       A:		Optional[int]          =		RePaintScheduler()
       A:		Optional[int]          =		{'''unet''': unet, '''scheduler''': scheduler}
       return components



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Tuple=0	)			->		Optional[int]:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		str          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		str          =		np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 3, 32, 32)	)
       A:		Tuple          =		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:		Tuple          =		{
           '''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 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		int          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		List[str]          =		self.get_dummy_components()
       A:		int          =		RePaintPipeline(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
       A:		Optional[Any]          =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       A:		List[Any]          =		np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]	)

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



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







       '''simple docstring'''



       return super().test_save_load_local()



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







       '''simple docstring'''



       return super().test_inference_batch_single_identical()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_save_load_optional_components()




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







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()






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







  '''simple docstring'''

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







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



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

       A:		Dict          =		'''google/ddpm-ema-celebahq-256'''
       A:		Tuple          =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		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:		str          =		torch.manual_seed(0	)
       A:		str          =		repaint(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  num_inference_steps=2_50					,  eta=0.0					,  jump_length=10					,  jump_n_sample=10					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )
       A:		Optional[Any]          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image	).mean() < 1E-2






