






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

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Union[str, Any]:
											torch.manual_seed(0					)
											__magic_name__:	Tuple							=       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=__UpperCAmelCase     ,		)
											__magic_name__:	Optional[int]							=       DDIMScheduler(
											    beta_start=0.00085     ,		beta_end=0.012     ,		beta_schedule="""scaled_linear"""     ,		clip_sample=__UpperCAmelCase     ,		set_alpha_to_one=__UpperCAmelCase     ,		)
											__magic_name__:	Tuple							=       DDIMInverseScheduler(
											    beta_start=0.00085     ,		beta_end=0.012     ,		beta_schedule="""scaled_linear"""     ,		clip_sample=__UpperCAmelCase     ,		set_alpha_to_zero=__UpperCAmelCase     ,		)
											torch.manual_seed(0					)
											__magic_name__:	Optional[int]							=       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					)
											__magic_name__:	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     ,		)
											__magic_name__:	Optional[int]							=       CLIPTextModel(__UpperCAmelCase					)
											__magic_name__:	List[Any]							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

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

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

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

											__magic_name__:	Optional[int]							=       self.get_dummy_inputs(__UpperCAmelCase					)
											__magic_name__:	Tuple							=       pipe(**__UpperCAmelCase					)[0]

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

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

											__magic_name__:	Optional[Any]							=       self.get_dummy_inputs(__UpperCAmelCase					)
											__magic_name__:	int							=       pipe_loaded(**__UpperCAmelCase					)[0]

											__magic_name__:	Optional[Any]							=       np.abs(output - output_loaded					).max()
											self.assertLess(__UpperCAmelCase     ,		1E-4					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Union[str, Any]:
											__magic_name__:	Tuple							=       """cpu"""

											__magic_name__:	List[Any]							=       self.get_dummy_components()
											__magic_name__:	Any							=       self.pipeline_class(**__UpperCAmelCase					)
											pipe.to(__UpperCAmelCase					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

											__magic_name__:	str							=       self.get_dummy_mask_inputs(__UpperCAmelCase					)
											__magic_name__:	Any							=       pipe.generate_mask(**__UpperCAmelCase					)
											__magic_name__:	Union[str, Any]							=       mask[0, -3:, -3:]

											self.assertEqual(mask.shape     ,		(1, 1_6, 1_6)					)
											__magic_name__:	int							=       np.array([0] * 9					)
											__magic_name__:	Tuple							=       np.abs(mask_slice.flatten() - expected_slice					).max()
											self.assertLessEqual(__UpperCAmelCase     ,		1E-3					)
											self.assertEqual(mask[0, -3, -4]     ,		0					)

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

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

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

											self.assertEqual(image.shape     ,		(2, 3_2, 3_2, 3)					)
											__magic_name__:	Tuple							=       np.array(
											    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,		)
											__magic_name__:	List[Any]							=       np.abs(image_slice.flatten() - expected_slice					).max()
											self.assertLessEqual(__UpperCAmelCase     ,		1E-3					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Optional[int]:
											super().test_inference_batch_single_identical(expected_max_diff=5E-3					)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Tuple:
											__magic_name__:	Any							=       """cpu"""

											__magic_name__:	Tuple							=       self.get_dummy_components()

											__magic_name__:	int							=       {"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
											__magic_name__:	int							=       DPMSolverMultistepScheduler(**__UpperCAmelCase					)
											__magic_name__:	str							=       DPMSolverMultistepInverseScheduler(**__UpperCAmelCase					)

											__magic_name__:	int							=       self.pipeline_class(**__UpperCAmelCase					)
											pipe.to(__UpperCAmelCase					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

											self.assertEqual(image.shape     ,		(2, 3_2, 3_2, 3)					)
											__magic_name__:	Dict							=       np.array(
											    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,		)
											__magic_name__:	int							=       np.abs(image_slice.flatten() - expected_slice					).max()
											self.assertLessEqual(__UpperCAmelCase     ,		1E-3					)



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

					def   lowerCamelCase__   (			self  :      int					)			->							Optional[Any]:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

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

											__magic_name__:	List[Any]							=       raw_image.convert("""RGB"""					).resize((7_6_8, 7_6_8)					)

											__magic_name__:	Any							=       raw_image

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Tuple:
											__magic_name__:	Dict							=       torch.manual_seed(0					)

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

											__magic_name__:	Dict							=       """a bowl of fruit"""
											__magic_name__:	Optional[Any]							=       """a bowl of pears"""

											__magic_name__:	List[Any]							=       pipe.generate_mask(
											    image=self.raw_image     ,		source_prompt=__UpperCAmelCase     ,		target_prompt=__UpperCAmelCase     ,		generator=__UpperCAmelCase     ,		)

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

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

											__magic_name__:	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  :      Optional[Any]					)			->							str:
											__magic_name__:	int							=       torch.manual_seed(0					)

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

											__magic_name__:	Optional[int]							=       """a bowl of fruit"""
											__magic_name__:	str							=       """a bowl of pears"""

											__magic_name__:	str							=       pipe.generate_mask(
											    image=self.raw_image     ,		source_prompt=__UpperCAmelCase     ,		target_prompt=__UpperCAmelCase     ,		generator=__UpperCAmelCase     ,		)

											__magic_name__:	Optional[Any]							=       pipe.invert(
											    prompt=__UpperCAmelCase     ,		image=self.raw_image     ,		inpaint_strength=0.7     ,		generator=__UpperCAmelCase     ,		num_inference_steps=2_5     ,		).latents

											__magic_name__:	List[Any]							=       pipe(
											    prompt=__UpperCAmelCase     ,		mask_image=__UpperCAmelCase     ,		image_latents=__UpperCAmelCase     ,		generator=__UpperCAmelCase     ,		negative_prompt=__UpperCAmelCase     ,		inpaint_strength=0.7     ,		num_inference_steps=2_5     ,		output_type="""numpy"""     ,		).images[0]

											__magic_name__:	Dict							=       (
											    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 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


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

  testmod()






