






'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__magic_name__		:     Optional[Any]					=    logging.getLogger(__name__)


__magic_name__		:     Tuple					=    list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__magic_name__		:     Optional[Any]					=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 	UpperCamelCase__					:

		"""simple docstring"""
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={
		        'help': (
		            'The model checkpoint for weights initialization. Leave None if you want to train a model from'
		            ' scratch.'
		        )
		    }      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCamelCase_			)}      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )







@dataclass
class 	UpperCamelCase__					:

		"""simple docstring"""
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'The input training data file (a text file).'}			)
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={
		        'help': (
		            'The input training data files (multiple files in glob format). '
		            'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
		        )
		    }      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}      ,     )

		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'}			)
		UpperCAmelCase__     			=						field(default=UpperCamelCase_      ,     metadata={'help': 'Whether ot not to use whole word mask.'}			)
		UpperCAmelCase__     			=						field(
		    default=0.15      ,     metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}			)
		UpperCAmelCase__     			=						field(
		    default=1 / 6      ,     metadata={
		        'help': (
		            'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
		            ' modeling.'
		        )
		    }      ,     )
		UpperCAmelCase__     			=						field(
		    default=5      ,     metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'}			)

		UpperCAmelCase__     			=						field(
		    default=-1      ,     metadata={
		        'help': (
		            'Optional input sequence length after tokenization.'
		            'The training dataset will be truncated in block of this size for training.'
		            'Default to the model max input length for single sentence inputs (take into account special tokens).'
		        )
		    }      ,     )
		UpperCAmelCase__     			=						field(
		    default=UpperCamelCase_      ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}			)





def     A__						(	A_    ,      A_    ,      A_ = False    ,      A_ = None    ,      )     ->				Optional[int]:


					def _dataset(A_    ,      A_=None		):
										if args.line_by_line:
															if ref_path is not None:
																				if not args.whole_word_mask or not args.mlm:
																									raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask"		)
																				return LineByLineWithRefDataset(
																				    tokenizer=lowerCamelCase_    ,      file_path=lowerCamelCase_    ,      block_size=args.block_size    ,      ref_path=lowerCamelCase_    ,      )

															return LineByLineTextDataset(tokenizer=lowerCamelCase_    ,      file_path=lowerCamelCase_    ,      block_size=args.block_size		)
										else:
															return TextDataset(
															    tokenizer=lowerCamelCase_    ,      file_path=lowerCamelCase_    ,      block_size=args.block_size    ,      overwrite_cache=args.overwrite_cache    ,      cache_dir=lowerCamelCase_    ,      )

					if evaluate:
										return _dataset(args.eval_data_file    ,      args.eval_ref_file		)
					elif args.train_data_files:
										return ConcatDataset([_dataset(lowerCamelCase_		) for f in glob(args.train_data_files		)]		)
					else:
										return _dataset(args.train_data_file    ,      args.train_ref_file		)



def     A__						(	)     ->				Any:
					_lowercase			= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
					_lowercase			= parser.parse_args_into_dataclasses()

					if data_args.eval_data_file is None and training_args.do_eval:
										raise ValueError(
										    "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
										    "or remove the --do_eval argument."		)
					if (
					    os.path.exists(training_args.output_dir		)
					    and os.listdir(training_args.output_dir		)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    " --overwrite_output_dir to overcome."		)

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,      datefmt="%m/%d/%Y %H:%M:%S"    ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,      )
					logger.warning(
					    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,      training_args.local_rank    ,      training_args.device    ,      training_args.n_gpu    ,      bool(training_args.local_rank != -1		)    ,      training_args.fpaa    ,      )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank		):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info("Training/evaluation parameters %s"    ,      lowerCamelCase_		)

					# Set seed
					set_seed(training_args.seed		)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					if model_args.config_name:
										_lowercase			= AutoConfig.from_pretrained(model_args.config_name    ,      cache_dir=model_args.cache_dir		)
					elif model_args.model_name_or_path:
										_lowercase			= AutoConfig.from_pretrained(model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir		)
					else:
										_lowercase			= CONFIG_MAPPING[model_args.model_type]()
										logger.warning("You are instantiating a new config instance from scratch."		)

					if model_args.tokenizer_name:
										_lowercase			= AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,      cache_dir=model_args.cache_dir		)
					elif model_args.model_name_or_path:
										_lowercase			= AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir		)
					else:
										raise ValueError(
										    "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
										    " script, save it,and load it from here, using --tokenizer_name"		)

					if model_args.model_name_or_path:
										_lowercase			= AutoModelWithLMHead.from_pretrained(
										    model_args.model_name_or_path    ,      from_tf=bool(".ckpt" in model_args.model_name_or_path		)    ,      config=lowerCamelCase_    ,      cache_dir=model_args.cache_dir    ,      )
					else:
										logger.info("Training new model from scratch"		)
										_lowercase			= AutoModelWithLMHead.from_config(lowerCamelCase_		)

					model.resize_token_embeddings(len(lowerCamelCase_		)		)

					if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
										raise ValueError(
										    "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
										    "--mlm flag (masked language modeling)."		)

					if data_args.block_size <= 0:
										_lowercase			= tokenizer.max_len
										# Our input block size will be the max possible for the model
					else:
										_lowercase			= min(data_args.block_size    ,      tokenizer.max_len		)

					# Get datasets

					_lowercase			= (
					    get_dataset(lowerCamelCase_    ,      tokenizer=lowerCamelCase_    ,      cache_dir=model_args.cache_dir		) if training_args.do_train else None
					)
					_lowercase			= (
					    get_dataset(lowerCamelCase_    ,      tokenizer=lowerCamelCase_    ,      evaluate=lowerCamelCase_    ,      cache_dir=model_args.cache_dir		)
					    if training_args.do_eval
					    else None
					)
					if config.model_type == "xlnet":
										_lowercase			= DataCollatorForPermutationLanguageModeling(
										    tokenizer=lowerCamelCase_    ,      plm_probability=data_args.plm_probability    ,      max_span_length=data_args.max_span_length    ,      )
					else:
										if data_args.mlm and data_args.whole_word_mask:
															_lowercase			= DataCollatorForWholeWordMask(
															    tokenizer=lowerCamelCase_    ,      mlm_probability=data_args.mlm_probability		)
										else:
															_lowercase			= DataCollatorForLanguageModeling(
															    tokenizer=lowerCamelCase_    ,      mlm=data_args.mlm    ,      mlm_probability=data_args.mlm_probability		)

															 # Initialize our Trainer
					_lowercase			= Trainer(
					    model=lowerCamelCase_    ,      args=lowerCamelCase_    ,      data_collator=lowerCamelCase_    ,      train_dataset=lowerCamelCase_    ,      eval_dataset=lowerCamelCase_    ,      prediction_loss_only=lowerCamelCase_    ,      )

					# Training
					if training_args.do_train:
										_lowercase			= (
										    model_args.model_name_or_path
										    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		)
										    else None
										)
										trainer.train(model_path=lowerCamelCase_		)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir		)

															 # Evaluation
					_lowercase			= {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"		)

										_lowercase			= trainer.evaluate()

										_lowercase			= math.exp(eval_output["eval_loss"]		)
										_lowercase			= {"perplexity": perplexity}

										_lowercase			= os.path.join(training_args.output_dir    ,      "eval_results_lm.txt"		)
										if trainer.is_world_master():
															with open(lowerCamelCase_    ,      "w"		) as writer:
																				logger.info("***** Eval results *****"		)
																				for key in sorted(result.keys()		):
																									logger.info("  %s = %s"    ,      lowerCamelCase_    ,      str(result[key]		)		)
																									writer.write("%s = %s\n" % (key, str(result[key]		))		)

										results.update(lowerCamelCase_		)

					return results



def     A__						(	A_		)     ->				List[str]:
					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    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				int	=			StableDiffusionDiffEditPipeline
 A_				:				List[Any]	=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 A_				:				str	=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 A_				:				Dict	=			frozenset(
     []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 A_				:				Union[str, Any]	=			frozenset([]  )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   str     		=	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__			,							)
  lowerCAmelCase__     :   str     		=	DDIMScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_one=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMInverseScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_zero=a__			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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    )
  lowerCAmelCase__     :   Dict     		=	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=1000			,							hidden_act="gelu"			,							projection_dim=512			,							)
  lowerCAmelCase__     :   Tuple     		=	CLIPTextModel(a__    )
  lowerCAmelCase__     :   int     		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

  lowerCAmelCase__     :   List[Any]     		=	{
      "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  : Dict			,							a__  : List[Any]			,							a__  : str=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor((1, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	floats_tensor((1, 2, 4, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Any     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   int     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Optional[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    _A	(    self  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Tuple=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   str     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   List[str]     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   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    _A	(    self  : Tuple			,							a__  : Any			,							a__  : Optional[Any]=0    ):
  '''simple docstring'''

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

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

  lowerCAmelCase__     :   int     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Optional[Any]     		=	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}    )

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   List[Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	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.'''			,							)

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   int     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Dict     		=	np.abs(output - output_loaded    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"cpu"

  lowerCAmelCase__     :   Any     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_mask_inputs(a__    )
  lowerCAmelCase__     :   List[str]     		=	pipe.generate_mask(**a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	mask[0, -3:, -3:]

  self.assertEqual(mask.shape			,							(1, 16, 16)    )
  lowerCAmelCase__     :   str     		=	np.array([0] * 9    )
  lowerCAmelCase__     :   Dict     		=	np.abs(mask_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )
  self.assertEqual(mask[0, -3, -4]			,							0    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"cpu"

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   List[str]     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Dict     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Tuple     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

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


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"cpu"

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

  lowerCAmelCase__     :   List[Any]     		=	{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepScheduler(**a__    )
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepInverseScheduler(**a__    )

  lowerCAmelCase__     :   Any     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Dict     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Any     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Dict     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   int     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )




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


 def    _A	(    self  : int    ):
  '''simple docstring'''

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


 @classmethod
 def    _A	(    cls  : Dict    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   Dict     		=	raw_image.convert("RGB"    ).resize((768, 768)    )

  lowerCAmelCase__     :   str     		=	raw_image


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   Optional[Any]     		=	DDIMScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   int     		=	DDIMInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Union[str, Any]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   str     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__    ).latents

  lowerCAmelCase__     :   Tuple     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   List[str]     		=	(
      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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   int     		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   Any     		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Optional[int]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__			,							num_inference_steps=25			,							).latents

  lowerCAmelCase__     :   Any     		=	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]

  lowerCAmelCase__     :   Optional[int]     		=	(
      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





