
"""simple docstring"""





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 A_	(lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		StableDiffusionDiffEditPipeline
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		frozenset(
      []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE__			:						str       =		frozenset([]      )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )
     UpperCAmelCase_					:					str			     =    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=lowercase_				,   )
     UpperCAmelCase_					:					str			     =    DDIMScheduler(
         beta_start=0.0_00_85				,   beta_end=0.0_12				,   beta_schedule="scaled_linear"				,   clip_sample=lowercase_				,   set_alpha_to_one=lowercase_				,   )
     UpperCAmelCase_					:					str			     =    DDIMInverseScheduler(
         beta_start=0.0_00_85				,   beta_end=0.0_12				,   beta_schedule="scaled_linear"				,   clip_sample=lowercase_				,   set_alpha_to_zero=lowercase_				,   )
     torch.manual_seed(0  )
     UpperCAmelCase_					:					str			     =    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  )
     UpperCAmelCase_					:					int			     =    CLIPTextConfig(
         bos_token_id=0				,   eos_token_id=2				,   hidden_size=32				,   intermediate_size=37				,   layer_norm_eps=1E-0_5				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1000				,   hidden_act="gelu"				,   projection_dim=512				,   )
     UpperCAmelCase_					:					Optional[int]			     =    CLIPTextModel(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

     UpperCAmelCase_					:					List[Any]			     =    {
         "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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    floats_tensor((1, 16, 16)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					int			     =    floats_tensor((1, 2, 4, 16, 16)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					List[Any]			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					Optional[int]			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    {
         "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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    floats_tensor((1, 3, 32, 32)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    image.cpu().permute(0				,   2				,   3				,   1  )[0]
     UpperCAmelCase_					:					Optional[int]			     =    Image.fromarray(np.uinta(lowercase_  )  ).convert("RGB"  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					int			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    {
         "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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    floats_tensor((1, 3, 32, 32)				,   rng=random.Random(lowercase_  )  ).to(lowercase_  )
     UpperCAmelCase_					:					int			     =    image.cpu().permute(0				,   2				,   3				,   1  )[0]
     UpperCAmelCase_					:					Dict			     =    Image.fromarray(np.uinta(lowercase_  )  ).convert("RGB"  )
     if str(lowercase_  ).startswith("mps"  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    torch.manual_seed(lowercase_  )
     else:
        UpperCAmelCase_					:					str			     =    torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    {
         "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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not hasattr(self.pipeline_class				,   "_optional_components"  ):
        return

     UpperCAmelCase_					:					str			     =    self.get_dummy_components()
     UpperCAmelCase_					:					List[str]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

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

     UpperCAmelCase_					:					List[Any]			     =    self.get_dummy_inputs(lowercase_  )
     UpperCAmelCase_					:					Dict			     =    pipe(**lowercase_  )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowercase_  )
        UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class.from_pretrained(lowercase_  )
        pipe_loaded.to(lowercase_  )
        pipe_loaded.set_progress_bar_config(disable=lowercase_  )

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

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_dummy_inputs(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    pipe_loaded(**lowercase_  )[0]

     UpperCAmelCase_					:					Any			     =    np.abs(output - output_loaded  ).max()
     self.assertLess(lowercase_				,   1E-4  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    "cpu"

     UpperCAmelCase_					:					List[Any]			     =    self.get_dummy_components()
     UpperCAmelCase_					:					Any			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    self.get_dummy_mask_inputs(lowercase_  )
     UpperCAmelCase_					:					str			     =    pipe.generate_mask(**lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    mask[0, -3:, -3:]

     self.assertEqual(mask.shape				,   (1, 16, 16)  )
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array([0] * 9  )
     UpperCAmelCase_					:					Dict			     =    np.abs(mask_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )
     self.assertEqual(mask[0, -3, -4]				,   0  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    "cpu"

     UpperCAmelCase_					:					str			     =    self.get_dummy_components()
     UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    self.get_dummy_inversion_inputs(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    pipe.invert(**lowercase_  ).images
     UpperCAmelCase_					:					Union[str, Any]			     =    image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,   (2, 32, 32, 3)  )
     UpperCAmelCase_					:					Optional[int]			     =    np.array(
         [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]				,   )
     UpperCAmelCase_					:					Optional[Any]			     =    np.abs(image_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    "cpu"

     UpperCAmelCase_					:					Optional[int]			     =    self.get_dummy_components()

     UpperCAmelCase_					:					Optional[Any]			     =    {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
     UpperCAmelCase_					:					Any			     =    DPMSolverMultistepScheduler(**lowercase_  )
     UpperCAmelCase_					:					Dict			     =    DPMSolverMultistepInverseScheduler(**lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    self.pipeline_class(**lowercase_  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.get_dummy_inversion_inputs(lowercase_  )
     UpperCAmelCase_					:					Any			     =    pipe.invert(**lowercase_  ).images
     UpperCAmelCase_					:					str			     =    image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,   (2, 32, 32, 3)  )
     UpperCAmelCase_					:					Optional[Any]			     =    np.array(
         [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]				,   )
     UpperCAmelCase_					:					Optional[Any]			     =    np.abs(image_slice.flatten() - expected_slice  ).max()
     self.assertLessEqual(lowercase_				,   1E-3  )




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




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

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







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"  )

     UpperCAmelCase_					:					int			     =    raw_image.convert("RGB"  ).resize((768, 768)  )

     UpperCAmelCase_					:					List[Any]			     =    raw_image







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    torch.manual_seed(0  )

     UpperCAmelCase_					:					int			     =    StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,   safety_checker=lowercase_				,   torch_dtype=torch.floataa  )
     UpperCAmelCase_					:					List[str]			     =    DDIMScheduler.from_config(pipe.scheduler.config  )
     UpperCAmelCase_					:					Union[str, Any]			     =    DDIMInverseScheduler.from_config(pipe.scheduler.config  )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    "a bowl of fruit"
     UpperCAmelCase_					:					Any			     =    "a bowl of pears"

     UpperCAmelCase_					:					List[str]			     =    pipe.generate_mask(
         image=self.raw_image				,   source_prompt=lowercase_				,   target_prompt=lowercase_				,   generator=lowercase_				,   )

     UpperCAmelCase_					:					List[Any]			     =    pipe.invert(
         prompt=lowercase_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=lowercase_  ).latents

     UpperCAmelCase_					:					Tuple			     =    pipe(
         prompt=lowercase_				,   mask_image=lowercase_				,   image_latents=lowercase_				,   generator=lowercase_				,   negative_prompt=lowercase_				,   inpaint_strength=0.7				,   output_type="numpy"				,   ).images[0]

     UpperCAmelCase_					:					List[Any]			     =    (
         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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    torch.manual_seed(0  )

     UpperCAmelCase_					:					Optional[Any]			     =    StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,   safety_checker=lowercase_				,   torch_dtype=torch.floataa  )
     UpperCAmelCase_					:					Any			     =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
     UpperCAmelCase_					:					List[Any]			     =    DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    "a bowl of fruit"
     UpperCAmelCase_					:					Optional[Any]			     =    "a bowl of pears"

     UpperCAmelCase_					:					List[Any]			     =    pipe.generate_mask(
         image=self.raw_image				,   source_prompt=lowercase_				,   target_prompt=lowercase_				,   generator=lowercase_				,   )

     UpperCAmelCase_					:					Tuple			     =    pipe.invert(
         prompt=lowercase_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=lowercase_				,   num_inference_steps=25				,   ).latents

     UpperCAmelCase_					:					List[str]			     =    pipe(
         prompt=lowercase_				,   mask_image=lowercase_				,   image_latents=lowercase_				,   generator=lowercase_				,   negative_prompt=lowercase_				,   inpaint_strength=0.7				,   num_inference_steps=25				,   output_type="numpy"				,   ).images[0]

     UpperCAmelCase_					:					List[Any]			     =    (
         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




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


