


'''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 ConvNextImageProcessor







class A__	(						unittest.TestCase				):






      def __init__(			self      :Any					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :Tuple=7					,						SCREAMING_SNAKE_CASE      :List[Any]=3					,						SCREAMING_SNAKE_CASE      :str=1_8					,						SCREAMING_SNAKE_CASE      :Dict=3_0					,						SCREAMING_SNAKE_CASE      :Optional[int]=4_0_0					,						SCREAMING_SNAKE_CASE      :Dict=True					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						SCREAMING_SNAKE_CASE      :Optional[int]=0.875					,						SCREAMING_SNAKE_CASE      :List[str]=True					,						SCREAMING_SNAKE_CASE      :Dict=[0.5, 0.5, 0.5]					,						SCREAMING_SNAKE_CASE      :List[str]=[0.5, 0.5, 0.5]					,						)	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			Any					 =size if size is not None else {"""shortest_edge""": 2_0}
             _a   :			Optional[int]					 =parent
             _a   :			Optional[int]					 =batch_size
             _a   :			Tuple					 =num_channels
             _a   :			Optional[int]					 =image_size
             _a   :			int					 =min_resolution
             _a   :			Dict					 =max_resolution
             _a   :			Optional[int]					 =do_resize
             _a   :			Optional[int]					 =size
             _a   :			Tuple					 =crop_pct
             _a   :			Any					 =do_normalize
             _a   :			int					 =image_mean
             _a   :			int					 =image_std






      def __UpperCAmelCase	(			self      :List[str]     )	-> str:




             '''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,
                 "crop_pct": self.crop_pct,
             }







@require_torch
@require_vision
class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  Any     	=							ConvNextImageProcessor if is_vision_available() else None






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




             '''simple docstring'''
             _a   :			List[Any]					 =ConvNextImageProcessingTester(self     )






      @property
      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




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






      def __UpperCAmelCase	(			self      :Any     )	-> Dict:




             '''simple docstring'''
             _a   :			Dict					 =self.image_processing_class(**self.image_processor_dict     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""do_resize"""     )     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""size"""     )     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""crop_pct"""     )     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""do_normalize"""     )     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""image_mean"""     )     )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""image_std"""     )     )






      def __UpperCAmelCase	(			self      :int     )	-> List[str]:




             '''simple docstring'''
             _a   :			Dict					 =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size					,						{"""shortest_edge""": 2_0}     )

             _a   :			Optional[int]					 =self.image_processing_class.from_dict(self.image_processor_dict					,						size=4_2     )
             self.assertEqual(image_processor.size					,						{"""shortest_edge""": 4_2}     )






      def __UpperCAmelCase	(			self      :Dict     )	-> List[Any]:




             '''simple docstring'''
             pass






      def __UpperCAmelCase	(			self      :str     )	-> Optional[int]:




             '''simple docstring'''
             _a   :			Any					 =self.image_processing_class(**self.image_processor_dict     )
             # create random PIL images
             _a   :			str					 =prepare_image_inputs(self.image_processor_tester					,						equal_resolution=SCREAMING_SNAKE_CASE     )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE					,						Image.Image     )

             # Test not batched input
             _a   :			Optional[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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)

             # Test batched
             _a   :			int					 =image_processing(SCREAMING_SNAKE_CASE					,						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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)






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




             '''simple docstring'''
             _a   :			List[str]					 =self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             _a   :			Union[str, Any]					 =prepare_image_inputs(self.image_processor_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
             _a   :			List[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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)

             # Test batched
             _a   :			Optional[Any]					 =image_processing(SCREAMING_SNAKE_CASE					,						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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)






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




             '''simple docstring'''
             _a   :			List[Any]					 =self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             _a   :			Optional[int]					 =prepare_image_inputs(self.image_processor_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
             _a   :			Union[str, 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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)

             # Test batched
             _a   :			str					 =image_processing(SCREAMING_SNAKE_CASE					,						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["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )					,						)






'''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 A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






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




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =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""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =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   :			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 __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =self.get_dummy_inputs(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =sd_pipe(**SCREAMING_SNAKE_CASE     ).images
             _a   :			Any					 =image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =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 __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




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






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




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






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




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






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




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






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




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







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






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




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






      def __UpperCAmelCase	(			self      :str     )	-> int:




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

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

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

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


