

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			snake_case_     (	__A			, unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       RePaintPipeline
			SCREAMING_SNAKE_CASE  :     str		    =       IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			SCREAMING_SNAKE_CASE  :     str		    =       IMAGE_INPAINTING_BATCH_PARAMS
			SCREAMING_SNAKE_CASE  :     Dict		    =       False







			def        snake_case__(  self				:       Optional[int]							)   ->Dict:
									torch.manual_seed(0							)
									torch.manual_seed(0							)
									snake_case_     =			UNetaDModel(
									    block_out_channels=(3_2, 6_4)							,     layers_per_block=2							,     sample_size=3_2							,     in_channels=3							,     out_channels=3							,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')							,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')							,     )
									snake_case_     =			RePaintScheduler()
									snake_case_     =			{'''unet''': unet, '''scheduler''': scheduler}
									return components







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       Union[str, Any]=0							)   ->str:
									if str(_UpperCamelCase							).startswith('''mps'''							):
															snake_case_     =			torch.manual_seed(_UpperCamelCase							)
									else:
															snake_case_     =			torch.Generator(device=_UpperCamelCase							).manual_seed(_UpperCamelCase							)
									snake_case_     =			np.random.RandomState(_UpperCamelCase							).standard_normal((1, 3, 3_2, 3_2)							)
									snake_case_     =			torch.from_numpy(_UpperCamelCase							).to(device=_UpperCamelCase							,     dtype=torch.floataa							)
									snake_case_     =			(image > 0).to(device=_UpperCamelCase							,     dtype=torch.floataa							)
									snake_case_     =			{
									    '''image''': image,
									    '''mask_image''': mask,
									    '''generator''': generator,
									    '''num_inference_steps''': 5,
									    '''eta''': 0.0,
									    '''jump_length''': 2,
									    '''jump_n_sample''': 2,
									    '''output_type''': '''numpy''',
									}
									return inputs







			def        snake_case__(  self				:       List[Any]							)   ->int:
									snake_case_     =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									snake_case_     =			self.get_dummy_components()
									snake_case_     =			RePaintPipeline(**_UpperCamelCase							)
									snake_case_     =			sd_pipe.to(_UpperCamelCase							)
									sd_pipe.set_progress_bar_config(disable=_UpperCamelCase							)

									snake_case_     =			self.get_dummy_inputs(_UpperCamelCase							)
									snake_case_     =			sd_pipe(**_UpperCamelCase							).images
									snake_case_     =			image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									snake_case_     =			np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]							)

									assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3







			@skip_mps
			def        snake_case__(  self				:       Union[str, Any]							)   ->Optional[Any]:
									return super().test_save_load_local()







			@unittest.skip('''non-deterministic pipeline'''							)
			def        snake_case__(  self				:       int							)   ->Tuple:
									return super().test_inference_batch_single_identical()







			@skip_mps
			def        snake_case__(  self				:       Optional[Any]							)   ->Optional[int]:
									return super().test_dict_tuple_outputs_equivalent()







			@skip_mps
			def        snake_case__(  self				:       Optional[Any]							)   ->Dict:
									return super().test_save_load_optional_components()







			@skip_mps
			def        snake_case__(  self				:       Union[str, Any]							)   ->Optional[int]:
									return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

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







			def        snake_case__(  self				:       List[Any]							)   ->Tuple:
									snake_case_     =			load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
									    '''repaint/celeba_hq_256.png'''							)
									snake_case_     =			load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''							)
									snake_case_     =			load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
									    '''repaint/celeba_hq_256_result.npy'''							)

									snake_case_     =			'''google/ddpm-ema-celebahq-256'''
									snake_case_     =			UNetaDModel.from_pretrained(_UpperCamelCase							)
									snake_case_     =			RePaintScheduler.from_pretrained(_UpperCamelCase							)

									snake_case_     =			RePaintPipeline(unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							).to(_UpperCamelCase							)
									repaint.set_progress_bar_config(disable=_UpperCamelCase							)
									repaint.enable_attention_slicing()

									snake_case_     =			torch.manual_seed(0							)
									snake_case_     =			repaint(
									    _UpperCamelCase							,     _UpperCamelCase							,     num_inference_steps=2_5_0							,     eta=0.0							,     jump_length=1_0							,     jump_n_sample=1_0							,     generator=_UpperCamelCase							,     output_type='''np'''							,     )
									snake_case_     =			output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)
									assert np.abs(expected_image - image							).mean() < 1e-2


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase_ 			=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     str		    =       ["safety_checker", "feature_extractor"]







			def __init__(  self				:       Any							,     _UpperCamelCase				:       AutoencoderKL							,     _UpperCamelCase				:       CLIPTextModel							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       UNetaDConditionModel							,     _UpperCamelCase				:       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,     _UpperCamelCase				:       StableDiffusionSafetyChecker							,     _UpperCamelCase				:       CLIPImageProcessor							,     _UpperCamelCase				:       bool = True							,     )   ->Any:
									super().__init__()
									if hasattr(scheduler.config							,     '''steps_offset'''							) and scheduler.config.steps_offset != 1:
															snake_case_     =			(
															    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
															    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
															    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
															    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
															    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
															    ''' file'''
															)
															deprecate('''steps_offset!=1'''							,     '''1.0.0'''							,     _UpperCamelCase							,     standard_warn=_UpperCamelCase							)
															snake_case_     =			dict(scheduler.config							)
															snake_case_     =			1
															snake_case_     =			FrozenDict(_UpperCamelCase							)

									self.register_modules(
									    vae=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							,     safety_checker=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							,     )
									self.register_to_config(requires_safety_checker=_UpperCamelCase							)







			@property
			def        snake_case__(  self				:       int							)   ->Dict[str, Any]:
									return {k: getattr(self							,     _UpperCamelCase							) for k in self.config.keys() if not k.startswith('''_'''							)}







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       Optional[Union[str, int]] = "auto"							)   ->Union[str, Any]:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_     =			self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_UpperCamelCase							)







			def        snake_case__(  self				:       str							)   ->Tuple:
									self.enable_attention_slicing(_UpperCamelCase							)







			@torch.no_grad()
			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       float = 0.8							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       Optional[float] = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     )   ->Dict:
									# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
									return StableDiffusionInpaintPipelineLegacy(**self.components							)(
									    prompt=_UpperCamelCase							,     image=_UpperCamelCase							,     mask_image=_UpperCamelCase							,     strength=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     )







			@torch.no_grad()
			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       float = 0.8							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       Optional[float] = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     **_UpperCamelCase				:       Tuple							,     )   ->Union[str, Any]:
									# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
									return StableDiffusionImgaImgPipeline(**self.components							)(
									    prompt=_UpperCamelCase							,     image=_UpperCamelCase							,     strength=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     callback_steps=_UpperCamelCase							,     )







			@torch.no_grad()
			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       int = 5_1_2							,     _UpperCamelCase				:       int = 5_1_2							,     _UpperCamelCase				:       int = 5_0							,     _UpperCamelCase				:       float = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     )   ->List[Any]:
									# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
									return StableDiffusionPipeline(**self.components							)(
									    prompt=_UpperCamelCase							,     height=_UpperCamelCase							,     width=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     latents=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     callback_steps=_UpperCamelCase							,     )