

"""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							(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           torch.manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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__   :      int      		=	DDIMScheduler(
               beta_start=0.00_085	,    beta_end=0.012	,    beta_schedule="""scaled_linear"""	,    clip_sample=_a	,    set_alpha_to_one=_a	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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  )
           SCREAMING_SNAKE_CASE__   :      str      		=	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  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	CLIPTextConfig(
               bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1E-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_000	,    hidden_act="""gelu"""	,    projection_dim=512	,    )
           SCREAMING_SNAKE_CASE__   :      str      		=	CLIPTextModel(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

           SCREAMING_SNAKE_CASE__   :      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 					_a			(						self	,    _a	,    _a=0  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	floats_tensor((1, 16, 16)	,    rng=random.Random(_a  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	floats_tensor((1, 2, 4, 16, 16)	,    rng=random.Random(_a  )  ).to(_a  )
           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.Generator(device=_a  ).manual_seed(_a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
               """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 					_a			(						self	,    _a	,    _a=0  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	floats_tensor((1, 3, 32, 32)	,    rng=random.Random(_a  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	image.cpu().permute(0	,    2	,    3	,    1  )[0]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	Image.fromarray(np.uinta(_a  )  ).convert("""RGB"""  )
           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	torch.Generator(device=_a  ).manual_seed(_a  )
           SCREAMING_SNAKE_CASE__   :      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 					_a			(						self	,    _a	,    _a=0  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	floats_tensor((1, 3, 32, 32)	,    rng=random.Random(_a  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	image.cpu().permute(0	,    2	,    3	,    1  )[0]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Image.fromarray(np.uinta(_a  )  ).convert("""RGB"""  )
           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.Generator(device=_a  ).manual_seed(_a  )
           SCREAMING_SNAKE_CASE__   :      List[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 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           if not hasattr(self.pipeline_class	,    """_optional_components"""  ):
                 return

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_dummy_components()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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__   :      str      		=	self.get_dummy_inputs(_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe(**_a  )[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(_a  )
                 SCREAMING_SNAKE_CASE__   :      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.'''	,    )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_dummy_inputs(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pipe_loaded(**_a  )[0]

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	np.abs(output - output_loaded  ).max()
           self.assertLess(_a	,    1E-4  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_dummy_components()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.pipeline_class(**_a  )
           pipe.to(_a  )
           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_dummy_mask_inputs(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe.generate_mask(**_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	mask[0, -3:, -3:]

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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_dummy_components()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.pipeline_class(**_a  )
           pipe.to(_a  )
           pipe.set_progress_bar_config(disable=_a  )

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

           self.assertEqual(image.shape	,    (2, 32, 32, 3)  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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]	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.abs(image_slice.flatten() - expected_slice  ).max()
           self.assertLessEqual(_a	,    1E-3  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           super().test_inference_batch_single_identical(expected_max_diff=5E-3  )





     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      int      		=	{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	DPMSolverMultistepScheduler(**_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	DPMSolverMultistepInverseScheduler(**_a  )

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

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_dummy_inversion_inputs(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	pipe.invert(**_a  ).images
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	image[0, -1, -3:, -3:]

           self.assertEqual(image.shape	,    (2, 32, 32, 3)  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	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]	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.abs(image_slice.flatten() - expected_slice  ).max()
           self.assertLessEqual(_a	,    1E-3  )





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






     '''simple docstring'''

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     @classmethod
     def 					_a			(						cls  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	raw_image.convert("""RGB"""  ).resize((768, 768)  )

           SCREAMING_SNAKE_CASE__   :      int      		=	raw_image

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.manual_seed(0  )

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

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""a bowl of fruit"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""a bowl of pears"""

           SCREAMING_SNAKE_CASE__   :      int      		=	pipe.generate_mask(
               image=self.raw_image	,    source_prompt=_a	,    target_prompt=_a	,    generator=_a	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	pipe.invert(
               prompt=_a	,    image=self.raw_image	,    inpaint_strength=0.7	,    generator=_a  ).latents

           SCREAMING_SNAKE_CASE__   :      int      		=	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__   :      List[str]      		=	(
               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 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""	,    safety_checker=_a	,    torch_dtype=torch.floataa  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""a bowl of fruit"""
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""a bowl of pears"""

           SCREAMING_SNAKE_CASE__   :      str      		=	pipe.generate_mask(
               image=self.raw_image	,    source_prompt=_a	,    target_prompt=_a	,    generator=_a	,    )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	pipe.invert(
               prompt=_a	,    image=self.raw_image	,    inpaint_strength=0.7	,    generator=_a	,    num_inference_steps=25	,    ).latents

           SCREAMING_SNAKE_CASE__   :      int      		=	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__   :      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







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      if divisor % 5 == 0 or divisor % 2 == 0:
            return 0
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	1
      SCREAMING_SNAKE_CASE__   :      int      		=	1
      while repunit:
            SCREAMING_SNAKE_CASE__   :      str      		=	(10 * repunit + 1) % divisor
            repunit_index += 1
      return repunit_index




def  _lowercase					(    __lowerCAmelCase = 100_0000							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	limit - 1
      if divisor % 2 == 0:
            divisor += 1
      while least_divisible_repunit(__lowerCAmelCase							) <= limit:
            divisor += 2
      return divisor


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




