






"""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_							(      snake_case_					,							snake_case_					,							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     _snake_case					(							self							:						List[Any]	)				->					Dict:
										torch.manual_seed(0	)
										__magic_name__            =     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=__lowerCamelCase						,    )
										__magic_name__            =     DDIMScheduler(
										    beta_start=0.0_0085						,    beta_end=0.012						,    beta_schedule="scaled_linear"						,    clip_sample=__lowerCamelCase						,    set_alpha_to_one=__lowerCamelCase						,    )
										__magic_name__            =     DDIMInverseScheduler(
										    beta_start=0.0_0085						,    beta_end=0.012						,    beta_schedule="scaled_linear"						,    clip_sample=__lowerCamelCase						,    set_alpha_to_zero=__lowerCamelCase						,    )
										torch.manual_seed(0	)
										__magic_name__            =     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__            =     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__            =     CLIPTextModel(__lowerCamelCase	)
										__magic_name__            =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

										__magic_name__            =     self.get_dummy_components()
										__magic_name__            =     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}	)

										__magic_name__            =     self.get_dummy_inputs(__lowerCamelCase	)
										__magic_name__            =     pipe(**__lowerCamelCase	)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(__lowerCamelCase	)
																__magic_name__            =     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.'''						,    )

										__magic_name__            =     self.get_dummy_inputs(__lowerCamelCase	)
										__magic_name__            =     pipe_loaded(**__lowerCamelCase	)[0]

										__magic_name__            =     np.abs(output - output_loaded	).max()
										self.assertLess(__lowerCamelCase						,    1e-4	)







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

										__magic_name__            =     self.get_dummy_components()
										__magic_name__            =     self.pipeline_class(**__lowerCamelCase	)
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)

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

										self.assertEqual(mask.shape						,    (1, 1_6, 1_6)	)
										__magic_name__            =     np.array([0] * 9	)
										__magic_name__            =     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	)				->					Dict:
										__magic_name__            =     "cpu"

										__magic_name__            =     self.get_dummy_components()
										__magic_name__            =     self.pipeline_class(**__lowerCamelCase	)
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)

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

										self.assertEqual(image.shape						,    (2, 3_2, 3_2, 3)	)
										__magic_name__            =     np.array(
										    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]						,    )
										__magic_name__            =     np.abs(image_slice.flatten() - expected_slice	).max()
										self.assertLessEqual(__lowerCamelCase						,    1e-3	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										super().test_inference_batch_single_identical(expected_max_diff=5e-3	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										__magic_name__            =     "cpu"

										__magic_name__            =     self.get_dummy_components()

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

										__magic_name__            =     self.pipeline_class(**__lowerCamelCase	)
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)

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

										self.assertEqual(image.shape						,    (2, 3_2, 3_2, 3)	)
										__magic_name__            =     np.array(
										    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]						,    )
										__magic_name__            =     np.abs(image_slice.flatten() - expected_slice	).max()
										self.assertLessEqual(__lowerCamelCase						,    1e-3	)





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







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







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

										__magic_name__            =     raw_image.convert("RGB"	).resize((7_6_8, 7_6_8)	)

										__magic_name__            =     raw_image







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     torch.manual_seed(0	)

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

										__magic_name__            =     "a bowl of fruit"
										__magic_name__            =     "a bowl of pears"

										__magic_name__            =     pipe.generate_mask(
										    image=self.raw_image						,    source_prompt=__lowerCamelCase						,    target_prompt=__lowerCamelCase						,    generator=__lowerCamelCase						,    )

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

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

										__magic_name__            =     (
										    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     _snake_case					(							self							:						str	)				->					Tuple:
										__magic_name__            =     torch.manual_seed(0	)

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

										__magic_name__            =     "a bowl of fruit"
										__magic_name__            =     "a bowl of pears"

										__magic_name__            =     pipe.generate_mask(
										    image=self.raw_image						,    source_prompt=__lowerCamelCase						,    target_prompt=__lowerCamelCase						,    generator=__lowerCamelCase						,    )

										__magic_name__            =     pipe.invert(
										    prompt=__lowerCamelCase						,    image=self.raw_image						,    inpaint_strength=0.7						,    generator=__lowerCamelCase						,    num_inference_steps=2_5						,    ).latents

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

										__magic_name__            =     (
										    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











"""simple docstring"""

from copy import deepcopy

class 						A_							:







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						list[int] | None = None						,    __lowerCamelCase							:						int | None = None	)				->					None:

										if arr is None and size is not None:
																__magic_name__            =     size
																__magic_name__            =     [0] * size
										elif arr is not None:
																self.init(__lowerCamelCase	)
										else:
																raise ValueError("Either arr or size must be specified"	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						list[int]	)				->					None:
										__magic_name__            =     len(__lowerCamelCase	)
										__magic_name__            =     deepcopy(__lowerCamelCase	)
										for i in range(1						,    self.size	):
																__magic_name__            =     self.next_(__lowerCamelCase	)
																if j < self.size:
																						self.tree[j] += self.tree[i]







				def     _snake_case					(							self							:						Any	)				->					list[int]:
										__magic_name__            =     self.tree[:]
										for i in range(self.size - 1						,    0						,    -1	):
																__magic_name__            =     self.next_(__lowerCamelCase	)
																if j < self.size:
																						arr[j] -= arr[i]
										return arr







				@staticmethod
				def     _snake_case					(							__lowerCamelCase							:						int	)				->					int:
										return index + (index & (-index))







				@staticmethod
				def     _snake_case					(							__lowerCamelCase							:						int	)				->					int:
										return index - (index & (-index))







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										if index == 0:
																self.tree[0] += value
																return
										while index < self.size:
																self.tree[index] += value
																__magic_name__            =     self.next_(__lowerCamelCase	)







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										self.add(__lowerCamelCase						,    value - self.get(__lowerCamelCase	)	)







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						int	)				->					int:
										if right == 0:
																return 0
										__magic_name__            =     self.tree[0]
										right -= 1  # make right inclusive
										while right > 0:
																result += self.tree[right]
																__magic_name__            =     self.prev(__lowerCamelCase	)
										return result







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					int:
										return self.prefix(__lowerCamelCase	) - self.prefix(__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					int:
										return self.query(__lowerCamelCase						,    index + 1	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					int:
										value -= self.tree[0]
										if value < 0:
																return -1

										__magic_name__            =     1  # Largest power of 2 <= size
										while j * 2 < self.size:
																j *= 2

										__magic_name__            =     0

										while j > 0:
																if i + j < self.size and self.tree[i + j] <= value:
																						value -= self.tree[i + j]
																						i += j
																j //= 2
										return i


if __name__ == "__main__":
			import doctest

			doctest.testmod()



