





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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						Any        =							UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, attention_head_dim=(2, 4)		, use_linear_projection=A		, )
          lowerCamelCase__       :						Dict        =							DDIMScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=A		, set_alpha_to_one=A		, )
          lowerCamelCase__       :						Optional[int]        =							DDIMInverseScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=A		, set_alpha_to_zero=A		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Union[str, Any]        =							AutoencoderKL(
              block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, sample_size=1_2_8		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							CLIPTextConfig(
              bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, hidden_act='''gelu'''		, projection_dim=5_1_2		, )
          lowerCamelCase__       :						List[str]        =							CLIPTextModel(A      )
          lowerCamelCase__       :						Optional[Any]        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

          lowerCamelCase__       :						Optional[int]        =							{
              '''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 						__lowerCamelCase   (				self					:     str		, A					:     Any		, A					:     Union[str, Any]=0      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							floats_tensor((1, 1_6, 1_6)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						List[str]        =							floats_tensor((1, 2, 4, 1_6, 1_6)		, rng=random.Random(A      )      ).to(A      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						Dict        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						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 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Union[str, Any]		, A					:     Optional[int]=0      )       ->List[Any]:
          lowerCamelCase__       :						List[str]        =							floats_tensor((1, 3, 3_2, 3_2)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						List[Any]        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						str        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						str        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						Union[str, Any]        =							{
              '''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 						__lowerCamelCase   (				self					:     int		, A					:     Tuple		, A					:     Optional[int]=0      )       ->Tuple:
          lowerCamelCase__       :						Optional[int]        =							floats_tensor((1, 3, 3_2, 3_2)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						Tuple        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						Union[str, Any]        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						Any        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						Tuple        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						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 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          if not hasattr(self.pipeline_class		, '''_optional_components'''      ):
             return

          lowerCamelCase__       :						List[Any]        =							self.get_dummy_components()
          lowerCamelCase__       :						int        =							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}      )

          lowerCamelCase__       :						Dict        =							self.get_dummy_inputs(A      )
          lowerCamelCase__       :						int        =							pipe(**A      )[0]

          with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(A      )
             lowerCamelCase__       :						Optional[int]        =							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."		, )

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_inputs(A      )
          lowerCamelCase__       :						List[str]        =							pipe_loaded(**A      )[0]

          lowerCamelCase__       :						int        =							np.abs(output - output_loaded      ).max()
          self.assertLess(A		, 1e-4      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							'''cpu'''

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_components()
          lowerCamelCase__       :						Tuple        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

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

          self.assertEqual(mask.shape		, (1, 1_6, 1_6)      )
          lowerCamelCase__       :						Any        =							np.array([0] * 9      )
          lowerCamelCase__       :						Optional[int]        =							np.abs(mask_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )
          self.assertEqual(mask[0, -3, -4]		, 0      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						List[Any]        =							'''cpu'''

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_components()
          lowerCamelCase__       :						Optional[int]        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Dict        =							self.get_dummy_inversion_inputs(A      )
          lowerCamelCase__       :						Dict        =							pipe.invert(**A      ).images
          lowerCamelCase__       :						Optional[Any]        =							image[0, -1, -3:, -3:]

          self.assertEqual(image.shape		, (2, 3_2, 3_2, 3)      )
          lowerCamelCase__       :						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]		, )
          lowerCamelCase__       :						Optional[Any]        =							np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->int:
          super().test_inference_batch_single_identical(expected_max_diff=5e-3      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          lowerCamelCase__       :						List[str]        =							'''cpu'''

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

          lowerCamelCase__       :						Dict        =							{'''beta_start''': 0.0_00_85, '''beta_end''': 0.0_12, '''beta_schedule''': '''scaled_linear'''}
          lowerCamelCase__       :						int        =							DPMSolverMultistepScheduler(**A      )
          lowerCamelCase__       :						Dict        =							DPMSolverMultistepInverseScheduler(**A      )

          lowerCamelCase__       :						Optional[Any]        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[int]        =							self.get_dummy_inversion_inputs(A      )
          lowerCamelCase__       :						List[str]        =							pipe.invert(**A      ).images
          lowerCamelCase__       :						Tuple        =							image[0, -1, -3:, -3:]

          self.assertEqual(image.shape		, (2, 3_2, 3_2, 3)      )
          lowerCamelCase__       :						Dict        =							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]		, )
          lowerCamelCase__       :						Any        =							np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )


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







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







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

          lowerCamelCase__       :						Dict        =							raw_image.convert('''RGB'''      ).resize((7_6_8, 7_6_8)      )

          lowerCamelCase__       :						List[Any]        =							raw_image







       def 						__lowerCamelCase   (				self					:     Any      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							torch.manual_seed(0      )

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

          lowerCamelCase__       :						Optional[int]        =							'''a bowl of fruit'''
          lowerCamelCase__       :						Any        =							'''a bowl of pears'''

          lowerCamelCase__       :						str        =							pipe.generate_mask(
              image=self.raw_image		, source_prompt=A		, target_prompt=A		, generator=A		, )

          lowerCamelCase__       :						List[str]        =							pipe.invert(
              prompt=A		, image=self.raw_image		, inpaint_strength=0.7		, generator=A      ).latents

          lowerCamelCase__       :						Union[str, Any]        =							pipe(
              prompt=A		, mask_image=A		, image_latents=A		, generator=A		, negative_prompt=A		, inpaint_strength=0.7		, output_type='''numpy'''		, ).images[0]

          lowerCamelCase__       :						List[Any]        =							(
              np.array(
                  load_image(
                      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                      '''/diffedit/pears.png'''      ).resize((7_6_8, 7_6_8)      )      )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()      ) < 5e-1







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							torch.manual_seed(0      )

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

          lowerCamelCase__       :						Any        =							'''a bowl of fruit'''
          lowerCamelCase__       :						Union[str, Any]        =							'''a bowl of pears'''

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

          lowerCamelCase__       :						Any        =							pipe.invert(
              prompt=A		, image=self.raw_image		, inpaint_strength=0.7		, generator=A		, num_inference_steps=2_5		, ).latents

          lowerCamelCase__       :						Any        =							pipe(
              prompt=A		, mask_image=A		, image_latents=A		, generator=A		, negative_prompt=A		, inpaint_strength=0.7		, num_inference_steps=2_5		, output_type='''numpy'''		, ).images[0]

          lowerCamelCase__       :						List[Any]        =							(
              np.array(
                  load_image(
                      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                      '''/diffedit/pears.png'''      ).resize((7_6_8, 7_6_8)      )      )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()      ) < 5e-1










from math import ceil, sqrt



def 	_a						(   UpperCAmelCase = 1000000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							0

   for outer_width in range(3      ,  (limit // 4) + 2					):
      if outer_width**2 > limit:
         lowerCamelCase__       :						List[Any]        =							max(ceil(sqrt(outer_width**2 - limit					)					)      ,  1					)
      else:
         lowerCamelCase__       :						Union[str, Any]        =							1
      if (outer_width - hole_width_lower_bound) % 2:
         hole_width_lower_bound += 1

      answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

   return answer


if __name__ == "__main__":
       print(F'''{solution() = }''')



