
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 __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Tuple=       RePaintPipeline
    _a	:		int=       IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
    _a	:		List[str]=       PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    _a	:		List[Any]=       IMAGE_INPAINTING_BATCH_PARAMS
    _a	:		str=       False
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           torch.manual_seed(0   )
           lowercase							:  Dict 			=   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""")	,)
           lowercase							:  Dict 			=   RePaintScheduler()
           lowercase							:  int 			=   {"""unet""": unet, """scheduler""": scheduler}
           return components
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case=0   ):



           '''simple docstring'''







           if str(snake_case   ).startswith("""mps"""   ):
                  lowercase							:  Dict 			=   torch.manual_seed(snake_case   )
           else:
                  lowercase							:  Any 			=   torch.Generator(device=snake_case   ).manual_seed(snake_case   )
           lowercase							:  Optional[int] 			=   np.random.RandomState(snake_case   ).standard_normal((1, 3, 32, 32)   )
           lowercase							:  List[str] 			=   torch.from_numpy(snake_case   ).to(device=snake_case	,dtype=torch.floataa   )
           lowercase							:  Union[str, Any] 			=   (image > 0).to(device=snake_case	,dtype=torch.floataa   )
           lowercase							:  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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  str 			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowercase							:  Union[str, Any] 			=   self.get_dummy_components()
           lowercase							:  int 			=   RePaintPipeline(**snake_case   )
           lowercase							:  List[str] 			=   sd_pipe.to(snake_case   )
           sd_pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  List[str] 			=   self.get_dummy_inputs(snake_case   )
           lowercase							:  List[Any] 			=   sd_pipe(**snake_case   ).images
           lowercase							:  str 			=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase							:  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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_save_load_local()
    @unittest.skip("""non-deterministic pipeline"""   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_inference_batch_single_identical()
    @skip_mps
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_save_load_optional_components()
    @skip_mps
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class __snake_case	(   unittest.TestCase  ):
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256.png"""   )
           lowercase							:  Dict 			=   load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""   )
           lowercase							:  Tuple 			=   load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256_result.npy"""   )

           lowercase							:  List[Any] 			=   """google/ddpm-ema-celebahq-256"""
           lowercase							:  str 			=   UNetaDModel.from_pretrained(snake_case   )
           lowercase							:  List[str] 			=   RePaintScheduler.from_pretrained(snake_case   )

           lowercase							:  Dict 			=   RePaintPipeline(unet=snake_case	,scheduler=snake_case   ).to(snake_case   )
           repaint.set_progress_bar_config(disable=snake_case   )
           repaint.enable_attention_slicing()

           lowercase							:  List[str] 			=   torch.manual_seed(0   )
           lowercase							:  Tuple 			=   repaint(
               snake_case	,snake_case	,num_inference_steps=250	,eta=0.0	,jump_length=10	,jump_n_sample=10	,generator=snake_case	,output_type="""np"""	,)
           lowercase							:  int 			=   output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image   ).mean() < 1e-2


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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(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
           lowercase							:  Union[str, Any] 			=   image_processing(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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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

           # Test not batched input
           lowercase							:  int 			=   image_processing(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
           lowercase							:  Optional[int] 			=   image_processing(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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(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
           lowercase							:  Union[str, Any] 			=   image_processing(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"""],
               )	,)
