
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 							UpperCAmelCase				(  lowerCAmelCase__      ,      lowerCAmelCase__      ,      unittest.TestCase  ):




 '''simple docstring'''





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






 def     UpperCamelCase_  (	self :			Dict			):
     torch.manual_seed(0			)
     __A             =			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=_SCREAMING_SNAKE_CASE							,)
     __A             =			DDIMScheduler(
         beta_start=0.0_00_85							,beta_end=0.0_12							,beta_schedule="scaled_linear"							,clip_sample=_SCREAMING_SNAKE_CASE							,set_alpha_to_one=_SCREAMING_SNAKE_CASE							,)
     __A             =			DDIMInverseScheduler(
         beta_start=0.0_00_85							,beta_end=0.0_12							,beta_schedule="scaled_linear"							,clip_sample=_SCREAMING_SNAKE_CASE							,set_alpha_to_zero=_SCREAMING_SNAKE_CASE							,)
     torch.manual_seed(0			)
     __A             =			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=1_28							,)
     torch.manual_seed(0			)
     __A             =			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=10_00							,hidden_act="gelu"							,projection_dim=5_12							,)
     __A             =			CLIPTextModel(_SCREAMING_SNAKE_CASE			)
     __A             =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

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

     __A             =			self.get_dummy_components()
     __A             =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
     pipe.to(_SCREAMING_SNAKE_CASE			)
     pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

     __A             =			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
     __A             =			pipe(**_SCREAMING_SNAKE_CASE			)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(_SCREAMING_SNAKE_CASE			)
         __A             =			self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE			)
         pipe_loaded.to(_SCREAMING_SNAKE_CASE			)
         pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

     __A             =			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
     __A             =			pipe_loaded(**_SCREAMING_SNAKE_CASE			)[0]

     __A             =			np.abs(output - output_loaded			).max()
     self.assertLess(_SCREAMING_SNAKE_CASE							,1E-4			)






 def     UpperCamelCase_  (	self :			Dict			):
     __A             =			"cpu"

     __A             =			self.get_dummy_components()
     __A             =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
     pipe.to(_SCREAMING_SNAKE_CASE			)
     pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

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






 def     UpperCamelCase_  (	self :			Tuple			):
     __A             =			"cpu"

     __A             =			self.get_dummy_components()
     __A             =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
     pipe.to(_SCREAMING_SNAKE_CASE			)
     pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

     self.assertEqual(image.shape							,(2, 32, 32, 3)			)
     __A             =			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]							,)
     __A             =			np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE							,1E-3			)






 def     UpperCamelCase_  (	self :			Tuple			):
     super().test_inference_batch_single_identical(expected_max_diff=5E-3			)







 def     UpperCamelCase_  (	self :			Any			):
     __A             =			"cpu"

     __A             =			self.get_dummy_components()

     __A             =			{"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
     __A             =			DPMSolverMultistepScheduler(**_SCREAMING_SNAKE_CASE			)
     __A             =			DPMSolverMultistepInverseScheduler(**_SCREAMING_SNAKE_CASE			)

     __A             =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
     pipe.to(_SCREAMING_SNAKE_CASE			)
     pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

     self.assertEqual(image.shape							,(2, 32, 32, 3)			)
     __A             =			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]							,)
     __A             =			np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(_SCREAMING_SNAKE_CASE							,1E-3			)





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




 '''simple docstring'''





 def     UpperCamelCase_  (	self :			Union[str, Any]			):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






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

     __A             =			raw_image.convert("RGB"			).resize((7_68, 7_68)			)

     __A             =			raw_image






 def     UpperCamelCase_  (	self :			List[str]			):
     __A             =			torch.manual_seed(0			)

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

     __A             =			"a bowl of fruit"
     __A             =			"a bowl of pears"

     __A             =			pipe.generate_mask(
         image=self.raw_image							,source_prompt=_SCREAMING_SNAKE_CASE							,target_prompt=_SCREAMING_SNAKE_CASE							,generator=_SCREAMING_SNAKE_CASE							,)

     __A             =			pipe.invert(
         prompt=_SCREAMING_SNAKE_CASE							,image=self.raw_image							,inpaint_strength=0.7							,generator=_SCREAMING_SNAKE_CASE			).latents

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

     __A             =			(
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"			).resize((7_68, 7_68)			)			)
         / 2_55
     )
     assert np.abs((expected_image - image).max()			) < 5E-1







 def     UpperCamelCase_  (	self :			int			):
     __A             =			torch.manual_seed(0			)

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

     __A             =			"a bowl of fruit"
     __A             =			"a bowl of pears"

     __A             =			pipe.generate_mask(
         image=self.raw_image							,source_prompt=_SCREAMING_SNAKE_CASE							,target_prompt=_SCREAMING_SNAKE_CASE							,generator=_SCREAMING_SNAKE_CASE							,)

     __A             =			pipe.invert(
         prompt=_SCREAMING_SNAKE_CASE							,image=self.raw_image							,inpaint_strength=0.7							,generator=_SCREAMING_SNAKE_CASE							,num_inference_steps=25							,).latents

     __A             =			pipe(
         prompt=_SCREAMING_SNAKE_CASE							,mask_image=_SCREAMING_SNAKE_CASE							,image_latents=_SCREAMING_SNAKE_CASE							,generator=_SCREAMING_SNAKE_CASE							,negative_prompt=_SCREAMING_SNAKE_CASE							,inpaint_strength=0.7							,num_inference_steps=25							,output_type="numpy"							,).images[0]

     __A             =			(
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"			).resize((7_68, 7_68)			)			)
         / 2_55
     )
     assert np.abs((expected_image - image).max()			) < 5E-1


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="text-classification"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"text": Value("string"  )}  )
	snake_case_    =					Features({"labels": ClassLabel}  )
	snake_case_    =					"text"
	snake_case_    =					"labels"






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if self.label_column not in features:
									raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
					if not isinstance(features[self.label_column]							,A			):
									raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.label_schema.copy()
					__A             =			features[self.label_column]
					__A             =			label_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return {
					    self.text_column: "text",
					    self.label_column: "labels",
					}
