


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_ :Union[str, Any]  							=			16
a_ :Any  							=			32




def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__ = 1_6							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		DatasetDict(
          {
              '''train''': dataset['''train'''].select(A__							),
              '''validation''': dataset['''train'''].select(A__							),
              '''test''': dataset['''validation'''],
          }							)

      def tokenize_function(A__							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__       :  Dict						     =		tokenizer(examples['''sentence1''']      ,    examples['''sentence2''']      ,    truncation=A__      ,    max_length=A__							)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		datasets.map(
                A__      ,    batched=A__      ,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,    )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tokenized_datasets.rename_column('''label'''      ,    '''labels'''							)

      def collate_fn(A__							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE__       :  Dict						     =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		8
            else:
                  SCREAMING_SNAKE_CASE__       :  int						     =		None

            return tokenizer.pad(
                A__      ,    padding='''longest'''      ,    max_length=A__      ,    pad_to_multiple_of=A__      ,    return_tensors='''pt'''      ,    )

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		DataLoader(
          tokenized_datasets['''train''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		DataLoader(
          tokenized_datasets['''validation''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		DataLoader(
          tokenized_datasets['''test''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      return train_dataloader, eval_dataloader, test_dataloader




def    a   (							A__      ,    A__							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[]
      # Download the dataset
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		load_dataset('''glue'''      ,    '''mrpc'''							)
      # Create our splits
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		StratifiedKFold(n_splits=int(args.num_folds							)							)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__       :  int						     =		Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision							)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		config['''lr''']
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		int(config['''batch_size''']							)

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		evaluate.load('''glue'''      ,    '''mrpc'''							)

      # If the batch size is too big we use gradient accumulation
      SCREAMING_SNAKE_CASE__       :  Any						     =		1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		batch_size // MAX_GPU_BATCH_SIZE
            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		MAX_GPU_BATCH_SIZE

      set_seed(A__							)

      # New Code #
      # Create our folds:
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		kfold.split(np.zeros(datasets['''train'''].num_rows							)      ,    datasets['''train''']['''label''']							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(A__							):
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		get_fold_dataloaders(
                A__      ,    A__      ,    A__      ,    A__      ,    )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''      ,    return_dict=A__							)

            # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
            # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
            # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model.to(accelerator.device							)

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		AdamW(params=model.parameters()      ,    lr=A__							)

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		get_linear_schedule_with_warmup(
                optimizer=A__      ,    num_warmup_steps=1_0_0      ,    num_training_steps=(len(A__							) * num_epochs) // gradient_accumulation_steps      ,    )

            # Prepare everything
            # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
            # prepare method.
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  int						     =		accelerator.prepare(
                A__      ,    A__      ,    A__      ,    A__      ,    A__							)

            # Now we train the model
            for epoch in range(A__							):
                  model.train()
                  for step, batch in enumerate(A__							):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device							)
                        SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model(**A__							)
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		outputs.loss
                        SCREAMING_SNAKE_CASE__       :  List[str]						     =		loss / gradient_accumulation_steps
                        accelerator.backward(A__							)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  model.eval()
                  for step, batch in enumerate(A__							):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device							)
                        with torch.no_grad():
                              SCREAMING_SNAKE_CASE__       :  List[str]						     =		model(**A__							)
                        SCREAMING_SNAKE_CASE__       :  Dict						     =		outputs.logits.argmax(dim=-1							)
                        SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                        metric.add_batch(
                            predictions=A__      ,    references=A__      ,    )

                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"""epoch {epoch}:"""      ,    A__							)

            # New Code #
            # We also run predictions on the test set at the very end
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		[]
            for step, batch in enumerate(A__							):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device							)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE__       :  Any						     =		model(**A__							)
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		outputs.logits
                  SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                  fold_predictions.append(predictions.cpu()							)
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()							)
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(A__      ,    dim=0							)							)
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		torch.cat(A__      ,    dim=0							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		torch.stack(A__      ,    dim=0							).sum(dim=0							).div(int(args.num_folds							)							).argmax(dim=-1							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		metric.compute(predictions=A__      ,    references=A__							)
      accelerator.print('''Average test metrics from all folds:'''      ,    A__							)




def    a   (							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		argparse.ArgumentParser(description='''Simple example of training script.'''							)
      parser.add_argument(
          '''--mixed_precision'''      ,    type=A__      ,    default=A__      ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']      ,    help='''Whether to use mixed precision. Choose'''
          '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
          '''and an Nvidia Ampere GPU.'''      ,    )
      parser.add_argument('''--cpu'''      ,    action='''store_true'''      ,    help='''If passed, will train on the CPU.'''							)
      # New Code #
      parser.add_argument('''--num_folds'''      ,    type=A__      ,    default=3      ,    help='''The number of splits to perform across the dataset'''							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		parser.parse_args()
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(A__      ,    A__							)


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


