

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=data




  def __iter__(							self   )    ->					int:
        '''simple docstring'''






        for element in self.data:
              yield element
def 	a (     A__      :  Any=True	)       -> List[Any]:





      """simple docstring"""



      _lowercase      			=Accelerator(even_batches=A__	)
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator
def 	a (     A__      :  Accelerator ,  A__      :  int ,  A__      :  int ,  A__      :  bool = False	)       -> Optional[int]:





      """simple docstring"""



      if iterable:
            _lowercase      			=DummyIterableDataset(torch.as_tensor(range(A__	)	)	)
      else:
            _lowercase      			=TensorDataset(torch.as_tensor(range(A__	)	)	)

      _lowercase      			=DataLoader(A__ ,  batch_size=A__	)
      _lowercase      			=accelerator.prepare(A__	)

      return dl
def 	a (     A__      :  Accelerator ,  A__      :  int ,  A__      :  int ,  A__      :  List[int] ,  A__      :  List[int] ,  )       -> List[Any]:





      """simple docstring"""



      _lowercase      			=create_dataloader(accelerator=A__ ,  dataset_size=A__ ,  batch_size=A__	)

      _lowercase      			=[len(batch[0]	) for batch in dl]

      if accelerator.process_index == 0:
            assert batch_sizes == process_0_expected_batch_sizes
      elif accelerator.process_index == 1:
            assert batch_sizes == process_1_expected_batch_sizes
def 	a (     )       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=create_accelerator()

      # without padding, we would expect a different number of batches
      verify_dataloader_batch_sizes(
          A__ ,  dataset_size=3 ,  batch_size=1 ,  process_0_expected_batch_sizes=[1, 1] ,  process_1_expected_batch_sizes=[1, 1] ,  )

      # without padding, we would expect the same number of batches, but different sizes
      verify_dataloader_batch_sizes(
          A__ ,  dataset_size=7 ,  batch_size=2 ,  process_0_expected_batch_sizes=[2, 2] ,  process_1_expected_batch_sizes=[2, 2] ,  )
def 	a (     )       -> int:





      """simple docstring"""



      _lowercase      			=create_accelerator(even_batches=A__	)

      verify_dataloader_batch_sizes(
          A__ ,  dataset_size=3 ,  batch_size=1 ,  process_0_expected_batch_sizes=[1, 1] ,  process_1_expected_batch_sizes=[1] ,  )

      verify_dataloader_batch_sizes(
          A__ ,  dataset_size=7 ,  batch_size=2 ,  process_0_expected_batch_sizes=[2, 2] ,  process_1_expected_batch_sizes=[2, 1] ,  )
def 	a (     )       -> List[str]:





      """simple docstring"""



      _lowercase      			=create_accelerator(even_batches=A__	)

      _lowercase      			=torch.nn.Linear(1 ,  1	)
      _lowercase      			=accelerator.prepare(A__	)

      _lowercase      			=create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1	)

      _lowercase      			=[]
      with accelerator.join_uneven_inputs([ddp_model]	):
            for batch_idx, batch in enumerate(A__	):
                  _lowercase      			=ddp_model(batch[0].float()	)
                  _lowercase      			=output.sum()
                  loss.backward()
                  batch_idxs.append(A__	)

      accelerator.wait_for_everyone()

      if accelerator.process_index == 0:
            assert batch_idxs == [0, 1]
      elif accelerator.process_index == 1:
            assert batch_idxs == [0]
def 	a (     A__      :  Tuple	)       -> int:





      """simple docstring"""



      with warnings.catch_warnings(record=A__	) as w:
            with accelerator.join_uneven_inputs([Mock()]	):
                  pass

            assert issubclass(w[-1].category ,  A__	)
            assert "only supported for multi-GPU" in str(w[-1].message	)
def 	a (     )       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=True
      _lowercase      			=False
      _lowercase      			=create_accelerator(even_batches=A__	)
      _lowercase      			=torch.nn.Linear(1 ,  1	)
      _lowercase      			=accelerator.prepare(A__	)
      _lowercase      			=create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1	)
      _lowercase      			=create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1	)

      with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=A__	):
            _lowercase      			=train_dl.batch_sampler.even_batches
            _lowercase      			=valid_dl.batch_sampler.even_batches

      assert train_dl_overridden_value == overridden_even_batches
      assert valid_dl_overridden_value == overridden_even_batches
      assert train_dl.batch_sampler.even_batches == default_even_batches
      assert valid_dl.batch_sampler.even_batches == default_even_batches
def 	a (     )       -> List[str]:





      """simple docstring"""



      _lowercase      			=True
      _lowercase      			=False
      _lowercase      			=create_accelerator(even_batches=A__	)
      _lowercase      			=torch.nn.Linear(1 ,  1	)
      _lowercase      			=accelerator.prepare(A__	)
      create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1 ,  iterable=A__	)
      _lowercase      			=create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1	)

      with warnings.catch_warnings():
            warnings.filterwarnings('ignore'	)
            try:
                  with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=A__	):
                        _lowercase      			=batch_dl.batch_sampler.even_batches
            except AttributeError:
                  # ensure attribute error is not raised when processing iterable dl
                  raise AssertionError

      assert batch_dl_overridden_value == overridden_even_batches
      assert batch_dl.batch_sampler.even_batches == default_even_batches
def 	a (     )       -> List[Any]:





      """simple docstring"""



      _lowercase      			=create_accelerator()
      _lowercase      			=torch.nn.Linear(1 ,  1	)
      _lowercase      			=accelerator.prepare(A__	)
      create_dataloader(A__ ,  dataset_size=3 ,  batch_size=1 ,  iterable=A__	)

      with warnings.catch_warnings(record=A__	) as w:
            with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=A__	):
                  pass

            assert issubclass(w[-1].category ,  A__	)
            assert "only supported for map-style datasets" in str(w[-1].message	)
def 	a (     )       -> int:





      """simple docstring"""



      _lowercase      			=create_accelerator()

      accelerator.print('Test that even_batches variable ensures uniform batches across processes'	)
      test_default_ensures_even_batch_sizes()

      accelerator.print('Run tests with even_batches disabled'	)
      test_can_disable_even_batches()

      accelerator.print('Test joining uneven inputs'	)
      test_can_join_uneven_inputs()

      accelerator.print('Test overriding even_batches when joining uneven inputs'	)
      test_join_can_override_even_batches()

      accelerator.print('Test overriding even_batches for mixed dataloader types'	)
      test_join_can_override_for_mixed_type_dataloaders()

      accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'	)
      test_join_raises_warning_for_iterable_when_overriding_even_batches()

      accelerator.print('Test join with non DDP distributed raises warning'	)
      _lowercase      			=accelerator.state.distributed_type
      _lowercase      			=DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(A__	)
      _lowercase      			=original_state


if __name__ == "__main__":
  main()





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

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


enable_full_determinism()





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    StableDiffusionDiffEditPipeline
  _a		     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
  _a		     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
  _a		     =    frozenset(
      []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _a		     =    frozenset([]				)




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=UNetaDConditionModel(
            block_out_channels=(32, 64)				,  layers_per_block=2				,  sample_size=32				,  in_channels=4				,  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=lowerCAmelCase				,  )
        _lowercase      			=DDIMScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_one=lowerCAmelCase				,  )
        _lowercase      			=DDIMInverseScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_zero=lowerCAmelCase				,  )
        torch.manual_seed(0   )
        _lowercase      			=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   )
        _lowercase      			=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				,  )
        _lowercase      			=CLIPTextModel(lowerCAmelCase   )
        _lowercase      			=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

        _lowercase      			={
            'unet': unet,
            'scheduler': scheduler,
            'inverse_scheduler': inverse_scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=floats_tensor((1, 2, 4, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            'prompt': 'a dog and a newt',
            'mask_image': mask,
            'image_latents': latents,
            'generator': generator,
            'num_inference_steps': 2,
            'inpaint_strength': 1.0,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }

        return inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            'image': image,
            'source_prompt': 'a cat and a frog',
            'target_prompt': 'a dog and a newt',
            'generator': generator,
            'num_inference_steps': 2,
            'num_maps_per_mask': 2,
            'mask_encode_strength': 1.0,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }

        return inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            'image': image,
            'prompt': 'a cat and a frog',
            'generator': generator,
            'num_inference_steps': 2,
            'inpaint_strength': 1.0,
            'guidance_scale': 6.0,
            'decode_latents': True,
            'output_type': 'numpy',
        }
        return inputs




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        if not hasattr(self.pipeline_class				,  '_optional_components'   ):
              return

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
              setattr(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe(**lowerCAmelCase   )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(lowerCAmelCase   )
              _lowercase      			=self.pipeline_class.from_pretrained(lowerCAmelCase   )
              pipe_loaded.to(lowerCAmelCase   )
              pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase   )

        for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(lowerCAmelCase				,  lowerCAmelCase   ) is None				,  F'''`{optional_component}` did not stay set to None after loading.'''				,  )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe_loaded(**lowerCAmelCase   )[0]

        _lowercase      			=np.abs(output - output_loaded   ).max()
        self.assertLess(lowerCAmelCase				,  1e-4   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			=self.get_dummy_mask_inputs(lowerCAmelCase   )
        _lowercase      			=pipe.generate_mask(**lowerCAmelCase   )
        _lowercase      			=mask[0, -3:, -3:]

        self.assertEqual(mask.shape				,  (1, 16, 16)   )
        _lowercase      			=np.array([0] * 9   )
        _lowercase      			=np.abs(mask_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )
        self.assertEqual(mask[0, -3, -4]				,  0   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			=self.get_dummy_inversion_inputs(lowerCAmelCase   )
        _lowercase      			=pipe.invert(**lowerCAmelCase   ).images
        _lowercase      			=image[0, -1, -3:, -3:]

        self.assertEqual(image.shape				,  (2, 32, 32, 3)   )
        _lowercase      			=np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]				,  )
        _lowercase      			=np.abs(image_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=5e-3   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()

        _lowercase      			={'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
        _lowercase      			=DPMSolverMultistepScheduler(**lowerCAmelCase   )
        _lowercase      			=DPMSolverMultistepInverseScheduler(**lowerCAmelCase   )

        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			=self.get_dummy_inversion_inputs(lowerCAmelCase   )
        _lowercase      			=pipe.invert(**lowerCAmelCase   ).images
        _lowercase      			=image[0, -1, -3:, -3:]

        self.assertEqual(image.shape				,  (2, 32, 32, 3)   )
        _lowercase      			=np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]				,  )
        _lowercase      			=np.abs(image_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )





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




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






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




  @classmethod
  def     A__	(							cls   )    ->					int:
        '''simple docstring'''






        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'   )

        _lowercase      			=raw_image.convert('RGB'   ).resize((768, 768)   )

        _lowercase      			=raw_image




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

        _lowercase      			=StableDiffusionDiffEditPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-1'				,  safety_checker=lowerCAmelCase				,  torch_dtype=torch.floataa   )
        _lowercase      			=DDIMScheduler.from_config(pipe.scheduler.config   )
        _lowercase      			=DDIMInverseScheduler.from_config(pipe.scheduler.config   )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

        _lowercase      			=pipe.invert(
            prompt=lowerCAmelCase				,  image=self.raw_image				,  inpaint_strength=0.7				,  generator=lowerCAmelCase   ).latents

        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  mask_image=lowerCAmelCase				,  image_latents=lowerCAmelCase				,  generator=lowerCAmelCase				,  negative_prompt=lowerCAmelCase				,  inpaint_strength=0.7				,  output_type='numpy'				,  ).images[0]

        _lowercase      			=(
            np.array(
                load_image(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    '/diffedit/pears.png'   ).resize((768, 768)   )   )
            / 255
        )
        assert np.abs((expected_image - image).max()   ) < 5e-1




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

        _lowercase      			=StableDiffusionDiffEditPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-1'				,  safety_checker=lowerCAmelCase				,  torch_dtype=torch.floataa   )
        _lowercase      			=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
        _lowercase      			=DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

        _lowercase      			=pipe.invert(
            prompt=lowerCAmelCase				,  image=self.raw_image				,  inpaint_strength=0.7				,  generator=lowerCAmelCase				,  num_inference_steps=25				,  ).latents

        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  mask_image=lowerCAmelCase				,  image_latents=lowerCAmelCase				,  generator=lowerCAmelCase				,  negative_prompt=lowerCAmelCase				,  inpaint_strength=0.7				,  num_inference_steps=25				,  output_type='numpy'				,  ).images[0]

        _lowercase      			=(
            np.array(
                load_image(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    '/diffedit/pears.png'   ).resize((768, 768)   )   )
            / 255
        )
        assert np.abs((expected_image - image).max()   ) < 5e-1


