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, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import OwlViTImageProcessor
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=7       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=18       ,  lowerCAmelCase_=30       ,  lowerCAmelCase_=400       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,  lowerCAmelCase_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,  lowerCAmelCase_=True       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =num_channels
		_lowercase   =image_size
		_lowercase   =min_resolution
		_lowercase   =max_resolution
		_lowercase   =do_resize
		_lowercase   =size if size is not None else {"height": 18, "width": 18}
		_lowercase   =do_center_crop
		_lowercase   =crop_size if crop_size is not None else {"height": 18, "width": 18}
		_lowercase   =do_normalize
		_lowercase   =image_mean
		_lowercase   =image_std
		_lowercase   =do_convert_rgb


	def __lowerCAmelCase      (   self       ):
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_convert_rgb": self.do_convert_rgb,
		}

@require_torch
@require_vision
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					OwlViTImageProcessor if is_vision_available() else None


	def __lowerCAmelCase      (   self       ):
		_lowercase   =OwlViTImageProcessingTester(self       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self.image_processor_tester.prepare_image_processor_dict()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_resize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "size"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_normalize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_mean"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_std"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_convert_rgb"       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class.from_dict(self.image_processor_dict       )
		self.assertEqual(image_processor.size       ,  {"height": 18, "width": 18}       )
		self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}       )

		_lowercase   =self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42       ,  crop_size=84       )
		self.assertEqual(image_processor.size       ,  {"height": 42, "width": 42}       )
		self.assertEqual(image_processor.crop_size       ,  {"height": 84, "width": 84}       )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PIL images
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       )

		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  Image.Image       )

		# Test not batched input
		_lowercase   =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.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =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.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random numpy tensors
		_lowercase   =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
		_lowercase   =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.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =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.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PyTorch tensors
		_lowercase   =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
		_lowercase   =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.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =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.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






