



'''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  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





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






    def  A       (  self      :					Dict ):


       """simple docstring"""







       torch.manual_seed(0 )
       UpperCamelCase					=  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__  ,						)
       UpperCamelCase					=  DDIMScheduler(
           beta_start=0.0_0_0_8_5  ,						beta_end=0.0_1_2  ,						beta_schedule='scaled_linear'  ,						clip_sample=UpperCamelCase__  ,						set_alpha_to_one=UpperCamelCase__  ,						)
       UpperCamelCase					=  DDIMInverseScheduler(
           beta_start=0.0_0_0_8_5  ,						beta_end=0.0_1_2  ,						beta_schedule='scaled_linear'  ,						clip_sample=UpperCamelCase__  ,						set_alpha_to_zero=UpperCamelCase__  ,						)
       torch.manual_seed(0 )
       UpperCamelCase					=  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 )
       UpperCamelCase					=  CLIPTextConfig(
           bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=3_2  ,						intermediate_size=3_7  ,						layer_norm_eps=1E-0_5  ,						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  ,						)
       UpperCamelCase					=  CLIPTextModel(UpperCamelCase__ )
       UpperCamelCase					=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

       UpperCamelCase					=  {
           '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  A       (  self      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]=0 ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor((1, 1_6, 1_6)  ,						rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
       UpperCamelCase					=  floats_tensor((1, 2, 4, 1_6, 1_6)  ,						rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
       if str(UpperCamelCase__ ).startswith('mps' ):
          UpperCamelCase					=  torch.manual_seed(UpperCamelCase__ )
       else:
          UpperCamelCase					=  torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
       UpperCamelCase					=  {
           '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  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]=0 ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor((1, 3, 3_2, 3_2)  ,						rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
       UpperCamelCase					=  image.cpu().permute(0  ,						2  ,						3  ,						1 )[0]
       UpperCamelCase					=  Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert('RGB' )
       if str(UpperCamelCase__ ).startswith('mps' ):
          UpperCamelCase					=  torch.manual_seed(UpperCamelCase__ )
       else:
          UpperCamelCase					=  torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
       UpperCamelCase					=  {
           '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  A       (  self      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]=0 ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor((1, 3, 3_2, 3_2)  ,						rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
       UpperCamelCase					=  image.cpu().permute(0  ,						2  ,						3  ,						1 )[0]
       UpperCamelCase					=  Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert('RGB' )
       if str(UpperCamelCase__ ).startswith('mps' ):
          UpperCamelCase					=  torch.manual_seed(UpperCamelCase__ )
       else:
          UpperCamelCase					=  torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
       UpperCamelCase					=  {
           '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  A       (  self      :					List[Any] ):


       """simple docstring"""







       if not hasattr(self.pipeline_class  ,						'_optional_components' ):
          return

       UpperCamelCase					=  self.get_dummy_components()
       UpperCamelCase					=  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} )

       UpperCamelCase					=  self.get_dummy_inputs(UpperCamelCase__ )
       UpperCamelCase					=  pipe(**UpperCamelCase__ )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
          pipe.save_pretrained(UpperCamelCase__ )
          UpperCamelCase					=  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."""  ,						)

       UpperCamelCase					=  self.get_dummy_inputs(UpperCamelCase__ )
       UpperCamelCase					=  pipe_loaded(**UpperCamelCase__ )[0]

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






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  'cpu'

       UpperCamelCase					=  self.get_dummy_components()
       UpperCamelCase					=  self.pipeline_class(**UpperCamelCase__ )
       pipe.to(UpperCamelCase__ )
       pipe.set_progress_bar_config(disable=UpperCamelCase__ )

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

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






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  'cpu'

       UpperCamelCase					=  self.get_dummy_components()
       UpperCamelCase					=  self.pipeline_class(**UpperCamelCase__ )
       pipe.to(UpperCamelCase__ )
       pipe.set_progress_bar_config(disable=UpperCamelCase__ )

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

       self.assertEqual(image.shape  ,						(2, 3_2, 3_2, 3) )
       UpperCamelCase					=  np.array(
           [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]  ,						)
       UpperCamelCase					=  np.abs(image_slice.flatten() - expected_slice ).max()
       self.assertLessEqual(UpperCamelCase__  ,						1E-3 )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       super().test_inference_batch_single_identical(expected_max_diff=5E-3 )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  'cpu'

       UpperCamelCase					=  self.get_dummy_components()

       UpperCamelCase					=  {'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
       UpperCamelCase					=  DPMSolverMultistepScheduler(**UpperCamelCase__ )
       UpperCamelCase					=  DPMSolverMultistepInverseScheduler(**UpperCamelCase__ )

       UpperCamelCase					=  self.pipeline_class(**UpperCamelCase__ )
       pipe.to(UpperCamelCase__ )
       pipe.set_progress_bar_config(disable=UpperCamelCase__ )

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

       self.assertEqual(image.shape  ,						(2, 3_2, 3_2, 3) )
       UpperCamelCase					=  np.array(
           [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]  ,						)
       UpperCamelCase					=  np.abs(image_slice.flatten() - expected_slice ).max()
       self.assertLessEqual(UpperCamelCase__  ,						1E-3 )

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






    """simple docstring"""





    def  A       (  self      :					str ):


       """simple docstring"""







       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    @classmethod
    def  A       (  cls      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )

       UpperCamelCase					=  raw_image.convert('RGB' ).resize((7_6_8, 7_6_8) )

       UpperCamelCase					=  raw_image






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  torch.manual_seed(0 )

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

       UpperCamelCase					=  'a bowl of fruit'
       UpperCamelCase					=  'a bowl of pears'

       UpperCamelCase					=  pipe.generate_mask(
           image=self.raw_image  ,						source_prompt=UpperCamelCase__  ,						target_prompt=UpperCamelCase__  ,						generator=UpperCamelCase__  ,						)

       UpperCamelCase					=  pipe.invert(
           prompt=UpperCamelCase__  ,						image=self.raw_image  ,						inpaint_strength=0.7  ,						generator=UpperCamelCase__ ).latents

       UpperCamelCase					=  pipe(
           prompt=UpperCamelCase__  ,						mask_image=UpperCamelCase__  ,						image_latents=UpperCamelCase__  ,						generator=UpperCamelCase__  ,						negative_prompt=UpperCamelCase__  ,						inpaint_strength=0.7  ,						output_type='numpy'  ,						).images[0]

       UpperCamelCase					=  (
           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       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  torch.manual_seed(0 )

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

       UpperCamelCase					=  'a bowl of fruit'
       UpperCamelCase					=  'a bowl of pears'

       UpperCamelCase					=  pipe.generate_mask(
           image=self.raw_image  ,						source_prompt=UpperCamelCase__  ,						target_prompt=UpperCamelCase__  ,						generator=UpperCamelCase__  ,						)

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

       UpperCamelCase					=  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]

       UpperCamelCase					=  (
           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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       UpperCamelCase					=  [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase					=  torch.tensor(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = None ):


       """simple docstring"""







       if self.config.s_min <= sigma <= self.config.s_max:
          UpperCamelCase					=  min(self.config.s_churn / self.num_inference_steps  ,						2**0.5 - 1 )
       else:
          UpperCamelCase					=  0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase					=  self.config.s_noise * randn_tensor(sample.shape  ,						generator=UpperCamelCase__ ).to(sample.device )
       UpperCamelCase					=  sigma + gamma * sigma
       UpperCamelCase					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_hat + sigma_hat * model_output
       UpperCamelCase					=  (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_prev + sigma_prev * model_output
       UpperCamelCase					=  (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

