
'''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				(_lowerCamelCase  ,					_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




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






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				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__       ,							)
          _SCREAMING_SNAKE_CASE      =				DDIMScheduler(
              beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							clip_sample=A__       ,							set_alpha_to_one=A__       ,							)
          _SCREAMING_SNAKE_CASE      =				DDIMInverseScheduler(
              beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							clip_sample=A__       ,							set_alpha_to_zero=A__       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				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=1_28       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
          _SCREAMING_SNAKE_CASE      =				CLIPTextModel(A__      )
          _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

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

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_components()
          _SCREAMING_SNAKE_CASE      =				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}      )

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				pipe(**A__      )[0]

          with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(A__      )
             _SCREAMING_SNAKE_CASE      =				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."       ,							)

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				pipe_loaded(**A__      )[0]

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






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				"""cpu"""

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

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

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






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				"""cpu"""

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

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

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






       def        UpperCamelCase				(							self      )    ->			int:
          super().test_inference_batch_single_identical(expected_max_diff=5E-3      )






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				"""cpu"""

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_components()

          _SCREAMING_SNAKE_CASE      =				{"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
          _SCREAMING_SNAKE_CASE      =				DPMSolverMultistepScheduler(**A__      )
          _SCREAMING_SNAKE_CASE      =				DPMSolverMultistepInverseScheduler(**A__      )

          _SCREAMING_SNAKE_CASE      =				self.pipeline_class(**A__      )
          pipe.to(A__      )
          pipe.set_progress_bar_config(disable=A__      )

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

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






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





       """simple docstring"""




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






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

          _SCREAMING_SNAKE_CASE      =				raw_image.convert("""RGB"""      ).resize((7_68, 7_68)      )

          _SCREAMING_SNAKE_CASE      =				raw_image






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				torch.manual_seed(0      )

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

          _SCREAMING_SNAKE_CASE      =				"""a bowl of fruit"""
          _SCREAMING_SNAKE_CASE      =				"""a bowl of pears"""

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

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

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

          _SCREAMING_SNAKE_CASE      =				(
              np.array(
                  load_image(
                      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                      """/diffedit/pears.png"""      ).resize((7_68, 7_68)      )      )
              / 2_55
          )
          assert np.abs((expected_image - image).max()      ) < 5E-1






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				torch.manual_seed(0      )

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

          _SCREAMING_SNAKE_CASE      =				"""a bowl of fruit"""
          _SCREAMING_SNAKE_CASE      =				"""a bowl of pears"""

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

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

          _SCREAMING_SNAKE_CASE      =				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]

          _SCREAMING_SNAKE_CASE      =				(
              np.array(
                  load_image(
                      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                      """/diffedit/pears.png"""      ).resize((7_68, 7_68)      )      )
              / 2_55
          )
          assert np.abs((expected_image - image).max()      ) < 5E-1








'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""multiplicative_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""multiplicative_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				1
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total *= numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""additive_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""additive_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				0
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total += numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps


if __name__ == "__main__":
     import doctest

     doctest.testmod()






