


"""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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Tuple									=    StableDiffusionDiffEditPipeline
   _a   :       Dict									=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
   _a   :       Tuple									=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
   _a   :       List[str]									=    frozenset(
       []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a   :       Any									=    frozenset([]  )


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    torch.manual_seed(0						)
    lowercase__       :       List[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=lowerCamelCase__ ,     )
    lowercase__       :       Optional[Any]				=						DDIMScheduler(
        beta_start=0.0_0085 ,     beta_end=0.012 ,     beta_schedule="""scaled_linear""" ,     clip_sample=lowerCamelCase__ ,     set_alpha_to_one=lowerCamelCase__ ,     )
    lowercase__       :       Dict				=						DDIMInverseScheduler(
        beta_start=0.0_0085 ,     beta_end=0.012 ,     beta_schedule="""scaled_linear""" ,     clip_sample=lowerCamelCase__ ,     set_alpha_to_zero=lowerCamelCase__ ,     )
    torch.manual_seed(0						)
    lowercase__       :       List[Any]				=						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__       :       List[Any]				=						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=1000 ,     hidden_act="""gelu""" ,     projection_dim=512 ,     )
    lowercase__       :       Optional[Any]				=						CLIPTextModel(lowerCamelCase__						)
    lowercase__       :       List[Any]				=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

    lowercase__       :       List[str]				=						{
        """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 ,     lowerCamelCase__ ,     lowerCamelCase__=0						)      ->							Union[str, Any]:
    lowercase__       :       Any				=						floats_tensor((1, 16, 16) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						floats_tensor((1, 2, 4, 16, 16) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    if str(lowerCamelCase__						).startswith("""mps"""						):
     lowercase__       :       Any				=						torch.manual_seed(lowerCamelCase__						)
    else:
     lowercase__       :       Any				=						torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						{
        """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 ,     lowerCamelCase__ ,     lowerCamelCase__=0						)      ->							List[Any]:
    lowercase__       :       Tuple				=						floats_tensor((1, 3, 32, 32) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    lowercase__       :       Dict				=						image.cpu().permute(0 ,     2 ,     3 ,     1						)[0]
    lowercase__       :       Optional[Any]				=						Image.fromarray(np.uinta(lowerCamelCase__						)						).convert("""RGB"""						)
    if str(lowerCamelCase__						).startswith("""mps"""						):
     lowercase__       :       Union[str, Any]				=						torch.manual_seed(lowerCamelCase__						)
    else:
     lowercase__       :       Dict				=						torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
    lowercase__       :       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 ,     lowerCamelCase__ ,     lowerCamelCase__=0						)      ->							Optional[int]:
    lowercase__       :       Optional[int]				=						floats_tensor((1, 3, 32, 32) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    lowercase__       :       List[Any]				=						image.cpu().permute(0 ,     2 ,     3 ,     1						)[0]
    lowercase__       :       List[str]				=						Image.fromarray(np.uinta(lowerCamelCase__						)						).convert("""RGB"""						)
    if str(lowerCamelCase__						).startswith("""mps"""						):
     lowercase__       :       Optional[int]				=						torch.manual_seed(lowerCamelCase__						)
    else:
     lowercase__       :       Optional[Any]				=						torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
    lowercase__       :       Optional[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						)      ->							List[Any]:
    if not hasattr(self.pipeline_class ,     """_optional_components"""						):
     return

    lowercase__       :       Dict				=						self.get_dummy_components()
    lowercase__       :       Any				=						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__       :       int				=						self.get_dummy_inputs(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						pipe(**lowerCamelCase__						)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Tuple				=						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__       :       Optional[Any]				=						self.get_dummy_inputs(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						pipe_loaded(**lowerCamelCase__						)[0]

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


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Union[str, Any]				=						"""cpu"""

    lowercase__       :       int				=						self.get_dummy_components()
    lowercase__       :       int				=						self.pipeline_class(**lowerCamelCase__						)
    pipe.to(lowerCamelCase__						)
    pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

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


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       List[str]				=						"""cpu"""

    lowercase__       :       List[Any]				=						self.get_dummy_components()
    lowercase__       :       Tuple				=						self.pipeline_class(**lowerCamelCase__						)
    pipe.to(lowerCamelCase__						)
    pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

    self.assertEqual(image.shape ,     (2, 32, 32, 3)						)
    lowercase__       :       Dict				=						np.array(
        [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,     )
    lowercase__       :       Any				=						np.abs(image_slice.flatten() - expected_slice						).max()
    self.assertLessEqual(lowerCamelCase__ ,     1E-3						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    super().test_inference_batch_single_identical(expected_max_diff=5E-3						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       int				=						"""cpu"""

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

    lowercase__       :       Optional[Any]				=						{"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
    lowercase__       :       Optional[int]				=						DPMSolverMultistepScheduler(**lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						DPMSolverMultistepInverseScheduler(**lowerCamelCase__						)

    lowercase__       :       Dict				=						self.pipeline_class(**lowerCamelCase__						)
    pipe.to(lowerCamelCase__						)
    pipe.set_progress_bar_config(disable=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						self.get_dummy_inversion_inputs(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						pipe.invert(**lowerCamelCase__						).images
    lowercase__       :       Union[str, Any]				=						image[0, -1, -3:, -3:]

    self.assertEqual(image.shape ,     (2, 32, 32, 3)						)
    lowercase__       :       str				=						np.array(
        [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,     )
    lowercase__       :       List[str]				=						np.abs(image_slice.flatten() - expected_slice						).max()
    self.assertLessEqual(lowerCamelCase__ ,     1E-3						)

@require_torch_gpu
@slow
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							List[str]:
    lowercase__       :       List[Any]				=						load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""						)

    lowercase__       :       Optional[int]				=						raw_image.convert("""RGB"""						).resize((768, 768)						)

    lowercase__       :       Any				=						raw_image


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						torch.manual_seed(0						)

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

    lowercase__       :       str				=						"""a bowl of fruit"""
    lowercase__       :       Any				=						"""a bowl of pears"""

    lowercase__       :       Optional[Any]				=						pipe.generate_mask(
        image=self.raw_image ,     source_prompt=lowerCamelCase__ ,     target_prompt=lowerCamelCase__ ,     generator=lowerCamelCase__ ,     )

    lowercase__       :       Tuple				=						pipe.invert(
        prompt=lowerCamelCase__ ,     image=self.raw_image ,     inpaint_strength=0.7 ,     generator=lowerCamelCase__						).latents

    lowercase__       :       Optional[Any]				=						pipe(
        prompt=lowerCamelCase__ ,     mask_image=lowerCamelCase__ ,     image_latents=lowerCamelCase__ ,     generator=lowerCamelCase__ ,     negative_prompt=lowerCamelCase__ ,     inpaint_strength=0.7 ,     output_type="""numpy""" ,     ).images[0]

    lowercase__       :       Dict				=						(
        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						)      ->							List[Any]:
    lowercase__       :       List[Any]				=						torch.manual_seed(0						)

    lowercase__       :       int				=						StableDiffusionDiffEditPipeline.from_pretrained(
        """stabilityai/stable-diffusion-2-1""" ,     safety_checker=lowerCamelCase__ ,     torch_dtype=torch.floataa						)
    lowercase__       :       int				=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
    lowercase__       :       Tuple				=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=lowerCamelCase__						)

    lowercase__       :       Union[str, Any]				=						"""a bowl of fruit"""
    lowercase__       :       int				=						"""a bowl of pears"""

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

    lowercase__       :       List[Any]				=						pipe.invert(
        prompt=lowerCamelCase__ ,     image=self.raw_image ,     inpaint_strength=0.7 ,     generator=lowerCamelCase__ ,     num_inference_steps=25 ,     ).latents

    lowercase__       :       List[str]				=						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__       :       Optional[int]				=						(
        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"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()