


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"""


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

							def    __snake_case (							self	:  Optional[Any]    )     ->   List[Any]:
														torch.manual_seed(0    )
														__snake_case		:      Dict										=       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=lowerCamelCase   ,     )
														__snake_case		:      List[str]										=       DDIMScheduler(
														    beta_start=0.0_00_85   ,     beta_end=0.0_12   ,     beta_schedule="scaled_linear"   ,     clip_sample=lowerCamelCase   ,     set_alpha_to_one=lowerCamelCase   ,     )
														__snake_case		:      Optional[int]										=       DDIMInverseScheduler(
														    beta_start=0.0_00_85   ,     beta_end=0.0_12   ,     beta_schedule="scaled_linear"   ,     clip_sample=lowerCamelCase   ,     set_alpha_to_zero=lowerCamelCase   ,     )
														torch.manual_seed(0    )
														__snake_case		:      Optional[int]										=       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    )
														__snake_case		:      List[Any]										=       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   ,     )
														__snake_case		:      Optional[Any]										=       CLIPTextModel(lowerCamelCase    )
														__snake_case		:      int										=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

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

														__snake_case		:      List[Any]										=       self.get_dummy_components()
														__snake_case		:      List[str]										=       self.pipeline_class(**lowerCamelCase    )
														pipe.to(lowerCamelCase    )
														pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

														__snake_case		:      Tuple										=       self.get_dummy_inputs(lowerCamelCase    )
														__snake_case		:      str										=       pipe(**lowerCamelCase    )[0]

														with tempfile.TemporaryDirectory() as tmpdir:
																					pipe.save_pretrained(lowerCamelCase    )
																					__snake_case		:      int										=       self.pipeline_class.from_pretrained(lowerCamelCase    )
																					pipe_loaded.to(lowerCamelCase    )
																					pipe_loaded.set_progress_bar_config(disable=lowerCamelCase    )

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

														__snake_case		:      List[str]										=       self.get_dummy_inputs(lowerCamelCase    )
														__snake_case		:      Tuple										=       pipe_loaded(**lowerCamelCase    )[0]

														__snake_case		:      Any										=       np.abs(output - output_loaded    ).max()
														self.assertLess(lowerCamelCase   ,     1E-4    )

							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       "cpu"

														__snake_case		:      List[str]										=       self.get_dummy_components()
														__snake_case		:      List[Any]										=       self.pipeline_class(**lowerCamelCase    )
														pipe.to(lowerCamelCase    )
														pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

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

							def    __snake_case (							self	:  Any    )     ->   Union[str, Any]:
														__snake_case		:      Union[str, Any]										=       "cpu"

														__snake_case		:      int										=       self.get_dummy_components()
														__snake_case		:      Dict										=       self.pipeline_class(**lowerCamelCase    )
														pipe.to(lowerCamelCase    )
														pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

														self.assertEqual(image.shape   ,     (2, 32, 32, 3)    )
														__snake_case		:      List[str]										=       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]   ,     )
														__snake_case		:      Optional[int]										=       np.abs(image_slice.flatten() - expected_slice    ).max()
														self.assertLessEqual(lowerCamelCase   ,     1E-3    )

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






							def    __snake_case (							self	:  Union[str, Any]    )     ->   Union[str, Any]:
														__snake_case		:      Optional[Any]										=       "cpu"

														__snake_case		:      Union[str, Any]										=       self.get_dummy_components()

														__snake_case		:      Optional[Any]										=       {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
														__snake_case		:      int										=       DPMSolverMultistepScheduler(**lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       DPMSolverMultistepInverseScheduler(**lowerCamelCase    )

														__snake_case		:      int										=       self.pipeline_class(**lowerCamelCase    )
														pipe.to(lowerCamelCase    )
														pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

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

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




							"""simple docstring"""


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

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

														__snake_case		:      int										=       raw_image.convert("RGB"    ).resize((768, 768)    )

														__snake_case		:      Optional[int]										=       raw_image

							def    __snake_case (							self	:  List[Any]    )     ->   int:
														__snake_case		:      List[str]										=       torch.manual_seed(0    )

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

														__snake_case		:      Optional[Any]										=       "a bowl of fruit"
														__snake_case		:      str										=       "a bowl of pears"

														__snake_case		:      Optional[int]										=       pipe.generate_mask(
														    image=self.raw_image   ,     source_prompt=lowerCamelCase   ,     target_prompt=lowerCamelCase   ,     generator=lowerCamelCase   ,     )

														__snake_case		:      Tuple										=       pipe.invert(
														    prompt=lowerCamelCase   ,     image=self.raw_image   ,     inpaint_strength=0.7   ,     generator=lowerCamelCase    ).latents

														__snake_case		:      str										=       pipe(
														    prompt=lowerCamelCase   ,     mask_image=lowerCamelCase   ,     image_latents=lowerCamelCase   ,     generator=lowerCamelCase   ,     negative_prompt=lowerCamelCase   ,     inpaint_strength=0.7   ,     output_type="numpy"   ,     ).images[0]

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

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

														__snake_case		:      List[Any]										=       "a bowl of fruit"
														__snake_case		:      Tuple										=       "a bowl of pears"

														__snake_case		:      Any										=       pipe.generate_mask(
														    image=self.raw_image   ,     source_prompt=lowerCamelCase   ,     target_prompt=lowerCamelCase   ,     generator=lowerCamelCase   ,     )

														__snake_case		:      Dict										=       pipe.invert(
														    prompt=lowerCamelCase   ,     image=self.raw_image   ,     inpaint_strength=0.7   ,     generator=lowerCamelCase   ,     num_inference_steps=25   ,     ).latents

														__snake_case		:      Dict										=       pipe(
														    prompt=lowerCamelCase   ,     mask_image=lowerCamelCase   ,     image_latents=lowerCamelCase   ,     generator=lowerCamelCase   ,     negative_prompt=lowerCamelCase   ,     inpaint_strength=0.7   ,     num_inference_steps=25   ,     output_type="numpy"   ,     ).images[0]

														__snake_case		:      Tuple										=       (
														    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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return x if y == 0 else greatest_common_divisor(__lowerCamelCase							,     x % y     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (x * y) // greatest_common_divisor(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase = 2_0     ):
							__snake_case		:      Optional[Any]										=       1
							for i in range(1							,     n + 1     ):
														__snake_case		:      Any										=       lcm(__lowerCamelCase							,     __lowerCamelCase     )
							return g


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

