




'''simple docstring'''





import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_SCREAMING_SNAKE_CASE				:  List[str] 						=					"."


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE				:  List[Any] 						=					os.path.join(REPO_PATH, "utils/documentation_tests.txt")
		_SCREAMING_SNAKE_CASE				:  List[str] 						=					[]
		_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					[]
		with open(doctest_file_path) as fp:
				for line in fp:
						_SCREAMING_SNAKE_CASE				:  Tuple 						=					line.strip()
						_SCREAMING_SNAKE_CASE				:  Union[str, Any] 						=					os.path.join(REPO_PATH, line)
						if not (os.path.isfile(path) or os.path.isdir(path)):
								non_existent_paths.append(line)
						all_paths.append(path)
		if len(non_existent_paths) > 0:
				_SCREAMING_SNAKE_CASE				:  List[Any] 						=					"\n".join(non_existent_paths)
				raise ValueError(F"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
		if all_paths != sorted(all_paths):
				raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")







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


