




'''simple docstring'''



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase_    	=				"true"




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any=82      ,__UpperCamelCase:					Union[str, Any]=16    ):

      """simple docstring"""






      set_seed(42    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						RegressionModel()
      SCREAMING_SNAKE_CASE   :      List[str]    					=						deepcopy(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						RegressionDataset(length=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DataLoader(__UpperCamelCase      ,batch_size=__UpperCamelCase    )
      model.to(accelerator.device    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return model, ddp_model, dataloader




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					Tuple=False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						load_dataset('glue'      ,'mrpc'      ,split='validation'    )

      def tokenize_function(__UpperCamelCase:					str    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer(examples['sentence1']      ,examples['sentence2']      ,truncation=__UpperCamelCase      ,max_length=__UpperCamelCase    )
            return outputs

      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						dataset.map(
                __UpperCamelCase      ,batched=__UpperCamelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

      SCREAMING_SNAKE_CASE   :      Any    					=						tokenized_datasets.rename_column('label'      ,'labels'    )

      def collate_fn(__UpperCamelCase:					List[Any]    ):
            if use_longest:
                  return tokenizer.pad(__UpperCamelCase      ,padding='longest'      ,return_tensors='pt'    )
            return tokenizer.pad(__UpperCamelCase      ,padding='max_length'      ,max_length=1_28      ,return_tensors='pt'    )

      return DataLoader(__UpperCamelCase      ,shuffle=__UpperCamelCase      ,collate_fn=__UpperCamelCase      ,batch_size=16    )




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						Accelerator(dispatch_batches=__UpperCamelCase      ,split_batches=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						get_dataloader(__UpperCamelCase      ,not dispatch_batches    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoModelForSequenceClassification.from_pretrained(
          'hf-internal-testing/mrpc-bert-base-cased'      ,return_dict=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def   lowercase__(			__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
      for batch in dataloader:
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						batch.values()
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE   :      int    					=						model(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((logit, target)    )
                  logits_and_targets.append((logit, target)    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[], []
      for logit, targ in logits_and_targets:
            logits.append(__UpperCamelCase    )
            targs.append(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Any    					=						torch.cat(__UpperCamelCase    ), torch.cat(__UpperCamelCase    )
      return logits, targs




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					int=82      ,__UpperCamelCase:					List[Any]=False      ,__UpperCamelCase:					Optional[Any]=False      ,__UpperCamelCase:					str=16    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						get_basic_setup(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						generate_predictions(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      assert (
          len(__UpperCamelCase    ) == num_samples
      ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCamelCase    )}"




def   lowercase__(			__UpperCamelCase:					bool = False      ,__UpperCamelCase:					bool = False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						evaluate.load('glue'      ,'mrpc'    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						get_mrpc_setup(__UpperCamelCase      ,__UpperCamelCase    )
      # First do baseline
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						setup['no']
      model.to(__UpperCamelCase    )
      model.eval()
      for batch in dataloader:
            batch.to(__UpperCamelCase    )
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						outputs.logits.argmax(dim=-1    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=batch['labels']    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						metric.compute()

      # Then do distributed
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						setup['ddp']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      str    					=						outputs.logits.argmax(dim=-1    )
            SCREAMING_SNAKE_CASE   :      Tuple    					=						batch['labels']
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((preds, references)    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]      ,distributed[key]    ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('**Testing gather_for_metrics**'    )
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"    )
                        test_mrpc(__UpperCamelCase      ,__UpperCamelCase    )
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test torch metrics**'    )
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"    )
                  test_torch_metrics(__UpperCamelCase      ,99    )
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test last batch is not dropped when perfectly divisible**'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Accelerator()
      test_torch_metrics(__UpperCamelCase      ,5_12    )
      accelerator.state._reset_state()




def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()








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





       '''simple docstring'''






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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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=A,							)
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_one=A,							)
             SCREAMING_SNAKE_CASE   :      str    					=						DDIMInverseScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_zero=A,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						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  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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=1_000,							hidden_act='gelu',							projection_dim=512,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

             SCREAMING_SNAKE_CASE   :      int    					=						{
                 '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,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						floats_tensor((1, 16, 16),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						floats_tensor((1, 2, 4, 16, 16),							rng=random.Random(A  )  ).to(A  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, 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        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						{
                 '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,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      int    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						{
                 '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  ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

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

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe(**A  )[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.pipeline_class.from_pretrained(A  )
                   pipe_loaded.to(A  )
                   pipe_loaded.set_progress_bar_config(disable=A  )

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

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						pipe_loaded(**A  )[0]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						np.abs(output - output_loaded  ).max()
             self.assertLess(A,							1E-4  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_mask_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.generate_mask(**A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						mask[0, -3:, -3:]

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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],							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_components()

             SCREAMING_SNAKE_CASE   :      Dict    					=						{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DPMSolverMultistepScheduler(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						DPMSolverMultistepInverseScheduler(**A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

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






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





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						raw_image.convert('RGB'  ).resize((768, 768)  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						raw_image







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						DDIMScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A  ).latents

             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      List[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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      int    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A,							num_inference_steps=25,							).latents

             SCREAMING_SNAKE_CASE   :      str    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							num_inference_steps=25,							output_type='numpy',							).images[0]

             SCREAMING_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


