

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



 def 			snake_case__  (   self		:   Tuple      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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=a__			,				)
      __magic_name__						       =    DDIMScheduler(
          beta_start=0.00_085			,				beta_end=0.012			,				beta_schedule='''scaled_linear'''			,				clip_sample=a__			,				set_alpha_to_one=a__			,				)
      __magic_name__						       =    DDIMInverseScheduler(
          beta_start=0.00_085			,				beta_end=0.012			,				beta_schedule='''scaled_linear'''			,				clip_sample=a__			,				set_alpha_to_zero=a__			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    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      )
      __magic_name__						       =    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			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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

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

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

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    pipe(**a__      )[0]

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

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

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    pipe_loaded(**a__      )[0]

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



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    '''cpu'''

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

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

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



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    '''cpu'''

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

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

      self.assertEqual(image.shape			,				(2, 32, 32, 3)      )
      __magic_name__						       =    np.array(
          [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]			,				)
      __magic_name__						       =    np.abs(image_slice.flatten() - expected_slice      ).max()
      self.assertLessEqual(a__			,				1E-3      )



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



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    '''cpu'''

      __magic_name__						       =    self.get_dummy_components()

      __magic_name__						       =    {'''beta_start''': 0.00_085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
      __magic_name__						       =    DPMSolverMultistepScheduler(**a__      )
      __magic_name__						       =    DPMSolverMultistepInverseScheduler(**a__      )

      __magic_name__						       =    self.pipeline_class(**a__      )
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )

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

      self.assertEqual(image.shape			,				(2, 32, 32, 3)      )
      __magic_name__						       =    np.array(
          [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]			,				)
      __magic_name__						       =    np.abs(image_slice.flatten() - expected_slice      ).max()
      self.assertLessEqual(a__			,				1E-3      )






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



 def 			snake_case__  (   self		:   int      ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 @classmethod
 def 			snake_case__  (   cls		:   int      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''      )

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

      __magic_name__						       =    raw_image



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    torch.manual_seed(0      )

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

      __magic_name__						       =    '''a bowl of fruit'''
      __magic_name__						       =    '''a bowl of pears'''

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

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

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

      __magic_name__						       =    (
          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		:   Dict      ):
      __magic_name__						       =    torch.manual_seed(0      )

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

      __magic_name__						       =    '''a bowl of fruit'''
      __magic_name__						       =    '''a bowl of pears'''

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

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

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

      __magic_name__						       =    (
          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

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






