



'''simple docstring'''



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 ViTImageProcessor
class 				UpperCAmelCase__     (			unittest.TestCase):



 def __init__(     self						,		lowercase						,		lowercase=1_3						,		lowercase=3						,		lowercase=2_2_4						,		lowercase=3_0						,		lowercase=4_0_0						,		lowercase=True						,		lowercase=None						,		lowercase=True						,		lowercase=[0.5, 0.5, 0.5]						,		lowercase=[0.5, 0.5, 0.5]						,		)     ->							Tuple:
  __UpperCamelCase 					=     size if size is not None else {"""height""": 1_8, """width""": 1_8}
  __UpperCamelCase 					=     parent
  __UpperCamelCase 					=     batch_size
  __UpperCamelCase 					=     num_channels
  __UpperCamelCase 					=     image_size
  __UpperCamelCase 					=     min_resolution
  __UpperCamelCase 					=     max_resolution
  __UpperCamelCase 					=     do_resize
  __UpperCamelCase 					=     size
  __UpperCamelCase 					=     do_normalize
  __UpperCamelCase 					=     image_mean
  __UpperCamelCase 					=     image_std




 def       __lowerCamelCase (     self	)     ->							int:
  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
  }


@require_torch
@require_vision
class 				UpperCAmelCase__     (			UpperCAmelCase_							,	unittest.TestCase):
 __SCREAMING_SNAKE_CASE    					= ViTImageProcessor if is_vision_available() else None



 def       __lowerCamelCase (     self	)     ->							str:
  __UpperCamelCase 					=     EfficientFormerImageProcessorTester(self	)



 @property
 def       __lowerCamelCase (     self	)     ->							List[str]:
  return self.image_proc_tester.prepare_image_processor_dict()



 def       __lowerCamelCase (     self	)     ->							Tuple:
  __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						,		"""size"""	)	)



 def       __lowerCamelCase (     self	)     ->							str:
  pass



 def       __lowerCamelCase (     self	)     ->							Union[str, Any]:
  __UpperCamelCase 					=     self.image_processing_class(**self.image_processor_dict	)
  # create random PIL images
  __UpperCamelCase 					=     prepare_image_inputs(self.image_proc_tester						,		equal_resolution=__SCREAMING_SNAKE_CASE	)
  for image in image_inputs:
   self.assertIsInstance(__SCREAMING_SNAKE_CASE						,		Image.Image	)

  # Test not batched input
  __UpperCamelCase 					=     image_processor(image_inputs[0]						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          1,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)

  # Test batched
  __UpperCamelCase 					=     image_processor(__SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          self.image_proc_tester.batch_size,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)



 def       __lowerCamelCase (     self	)     ->							Union[str, Any]:
  __UpperCamelCase 					=     self.image_processing_class(**self.image_processor_dict	)
  # create random numpy tensors
  __UpperCamelCase 					=     prepare_image_inputs(self.image_proc_tester						,		equal_resolution=__SCREAMING_SNAKE_CASE						,		numpify=__SCREAMING_SNAKE_CASE	)
  for image in image_inputs:
   self.assertIsInstance(__SCREAMING_SNAKE_CASE						,		np.ndarray	)

  # Test not batched input
  __UpperCamelCase 					=     image_processor(image_inputs[0]						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          1,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)

  # Test batched
  __UpperCamelCase 					=     image_processor(__SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          self.image_proc_tester.batch_size,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)




 def       __lowerCamelCase (     self	)     ->							Optional[Any]:
  __UpperCamelCase 					=     self.image_processing_class(**self.image_processor_dict	)
  # create random PyTorch tensors
  __UpperCamelCase 					=     prepare_image_inputs(self.image_proc_tester						,		equal_resolution=__SCREAMING_SNAKE_CASE						,		torchify=__SCREAMING_SNAKE_CASE	)
  for image in image_inputs:
   self.assertIsInstance(__SCREAMING_SNAKE_CASE						,		torch.Tensor	)

  # Test not batched input
  __UpperCamelCase 					=     image_processor(image_inputs[0]						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          1,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)

  # Test batched
  __UpperCamelCase 					=     image_processor(__SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""	).pixel_values
  self.assertEqual(
      encoded_images.shape						,		(
          self.image_proc_tester.batch_size,
          self.image_proc_tester.num_channels,
          self.image_proc_tester.size["""height"""],
          self.image_proc_tester.size["""width"""],
      )						,		)








'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDConditionModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=9					,							out_channels=4					,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,							cross_attention_dim=32					,							attention_head_dim=(2, 4)					,							use_linear_projection=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      AutoencoderKL(
          block_out_channels=[32, 64]					,							in_channels=3					,							out_channels=3					,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,							latent_channels=4					,							sample_size=128					,							)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      CLIPTextConfig(
          bos_token_id=0					,							eos_token_id=2					,							hidden_size=32					,							intermediate_size=37					,							layer_norm_eps=1E-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							pad_token_id=1					,							vocab_size=1_000					,							hidden_act="""gelu"""					,							projection_dim=512					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": init_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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



 """simple docstring"""



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


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 5E-1


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






