





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




 def 							_snake_case   (	self		)    ->							int:
     torch.manual_seed(0		)
     _lowerCAmelCase				=      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			,     )
     _lowerCAmelCase				=      DDIMScheduler(
         beta_start=0.00085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=_lowerCAmelCase			,     set_alpha_to_one=_lowerCAmelCase			,     )
     _lowerCAmelCase				=      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		)
     _lowerCAmelCase				=      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		)
     _lowerCAmelCase				=      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			,     )
     _lowerCAmelCase				=      CLIPTextModel(_lowerCAmelCase		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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

     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      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}		)

     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      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.'''			,     )

     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe_loaded(**_lowerCAmelCase		)[0]

     _lowerCAmelCase				=      np.abs(output - output_loaded		).max()
     self.assertLess(_lowerCAmelCase			,     1E-4		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      "cpu"

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

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

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




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      "cpu"

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

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

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




 def 							_snake_case   (	self		)    ->							Any:
     super().test_inference_batch_single_identical(expected_max_diff=5E-3		)





 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      "cpu"

     _lowerCAmelCase				=      self.get_dummy_components()

     _lowerCAmelCase				=      {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
     _lowerCAmelCase				=      DPMSolverMultistepScheduler(**_lowerCAmelCase		)
     _lowerCAmelCase				=      DPMSolverMultistepInverseScheduler(**_lowerCAmelCase		)

     _lowerCAmelCase				=      self.pipeline_class(**_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

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







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




 def 							_snake_case   (	self		)    ->							Tuple:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 @classmethod
 def 							_snake_case   (	cls		)    ->							Tuple:
     _lowerCAmelCase				=      load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"		)

     _lowerCAmelCase				=      raw_image.convert("RGB"		).resize((768, 768)		)

     _lowerCAmelCase				=      raw_image




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      torch.manual_seed(0		)

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

     _lowerCAmelCase				=      "a bowl of fruit"
     _lowerCAmelCase				=      "a bowl of pears"

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

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

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

     _lowerCAmelCase				=      (
         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 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      torch.manual_seed(0		)

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

     _lowerCAmelCase				=      "a bowl of fruit"
     _lowerCAmelCase				=      "a bowl of pears"

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

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

     _lowerCAmelCase				=      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]

     _lowerCAmelCase				=      (
         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 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise ValueError("check_bouncy() accepts only integer arguments"    )
    _lowerCAmelCase				=      str(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      "".join(sorted(SCREAMING_SNAKE_CASE_    )    )
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 	__a(SCREAMING_SNAKE_CASE_  :					float = 99    ):



    '''simple docstring'''





    if not 0 < percent < 100:
        raise ValueError("solution() only accepts values from 0 to 100"    )
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      1

    while True:
        if check_bouncy(SCREAMING_SNAKE_CASE_    ):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


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

       testmod()
       print(f'''{solution(99)}''')


