import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__lowerCamelCase     :					Dict				  =						logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__lowerCamelCase     :					int				  =						{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__lowerCamelCase     :					Tuple				  =						{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__lowerCamelCase     :					Union[str, Any]				  =						sorted(arg_to_scheduler.keys())
__lowerCamelCase     :					Any				  =						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class __snake_case							(		pl.LightningModule						):




   def __init__(       self     :							Optional[Any]			,      _lowercase     :							argparse.Namespace			,      _lowercase     :							int=None			,      _lowercase     :							Optional[Any]="base"			,      _lowercase     :							str=None			,      _lowercase     :							Union[str, Any]=None			,      _lowercase     :							int=None			,      **_lowercase     :							Optional[int]			,      ):



     """simple docstring"""






     super().__init__()
     # TODO: move to self.save_hyperparameters()
     # self.save_hyperparameters()
     # can also expand arguments into trainer signature for easier reading

     self.save_hyperparameters(_lowercase )
     SCREAMING_SNAKE_CASE__     				= 0
     SCREAMING_SNAKE_CASE__     				= Path(self.hparams.output_dir )
     SCREAMING_SNAKE_CASE__     				= self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
       SCREAMING_SNAKE_CASE__     				= AutoConfig.from_pretrained(
           self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,      **({"""num_labels""": num_labels} if num_labels is not None else {})			,      cache_dir=_lowercase			,      **_lowercase			,      )
     else:
       SCREAMING_SNAKE_CASE__     				= config

     SCREAMING_SNAKE_CASE__     				= ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
     for p in extra_model_params:
       if getattr(self.hparams			,      _lowercase			,      _lowercase ):
         assert hasattr(self.config			,      _lowercase ), f"""model config doesn't have a `{p}` attribute"""
         setattr(self.config			,      _lowercase			,      getattr(self.hparams			,      _lowercase ) )

     if tokenizer is None:
       SCREAMING_SNAKE_CASE__     				= AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,      cache_dir=_lowercase			,      )
     else:
       SCREAMING_SNAKE_CASE__     				= tokenizer
     SCREAMING_SNAKE_CASE__     				= MODEL_MODES[mode]
     if model is None:
       SCREAMING_SNAKE_CASE__     				= self.model_type.from_pretrained(
           self.hparams.model_name_or_path			,      from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path )			,      config=self.config			,      cache_dir=_lowercase			,      )
     else:
       SCREAMING_SNAKE_CASE__     				= model




   def       __a		(       self     :							Union[str, Any]			,      *_lowercase     :							List[str]			,      **_lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_type.from_pretrained(*_lowercase			,      **_lowercase )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= arg_to_scheduler[self.hparams.lr_scheduler]
     SCREAMING_SNAKE_CASE__     				= get_schedule_func(
         self.opt			,      num_warmup_steps=self.hparams.warmup_steps			,      num_training_steps=self.total_steps() )
     SCREAMING_SNAKE_CASE__     				= {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
     return scheduler




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model
     SCREAMING_SNAKE_CASE__     				= ["""bias""", """LayerNorm.weight"""]
     SCREAMING_SNAKE_CASE__     				= [
         {
             """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
             """weight_decay""": self.hparams.weight_decay,
         },
         {
             """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
             """weight_decay""": 0.0,
         },
     ]
     if self.hparams.adafactor:
       SCREAMING_SNAKE_CASE__     				= Adafactor(
           _lowercase			,      lr=self.hparams.learning_rate			,      scale_parameter=_lowercase			,      relative_step=_lowercase )

     else:
       SCREAMING_SNAKE_CASE__     				= AdamW(
           _lowercase			,      lr=self.hparams.learning_rate			,      eps=self.hparams.adam_epsilon )
     SCREAMING_SNAKE_CASE__     				= optimizer

     SCREAMING_SNAKE_CASE__     				= self.get_lr_scheduler()

     return [optimizer], [scheduler]




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict			,      _lowercase     :							List[str] ):



     """simple docstring"""






     return self.validation_step(_lowercase			,      _lowercase )




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.validation_end(_lowercase )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= max(1			,      self.hparams.gpus )  # TODO: consider num_tpu_cores
     SCREAMING_SNAKE_CASE__     				= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




   def       __a		(       self     :							int			,      _lowercase     :							List[str] ):



     """simple docstring"""






     if mode == "test":
       SCREAMING_SNAKE_CASE__     				= len(self.test_dataloader().dataset )
     else:
       SCREAMING_SNAKE_CASE__     				= self.get_dataloader("""train"""			,      self.hparams.train_batch_size			,      shuffle=_lowercase )
       SCREAMING_SNAKE_CASE__     				= len(self.train_dataloader().dataset )




   def       __a		(       self     :							List[Any]			,      _lowercase     :							str			,      _lowercase     :							int			,      _lowercase     :							bool = False ):



     """simple docstring"""






     raise NotImplementedError("""You must implement this for your task""" )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     return self.train_loader




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     return self.get_dataloader("""dev"""			,      self.hparams.eval_batch_size			,      shuffle=_lowercase )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.get_dataloader("""test"""			,      self.hparams.eval_batch_size			,      shuffle=_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






     return os.path.join(
         self.hparams.data_dir			,      """cached_{}_{}_{}""".format(
             _lowercase			,      list(filter(_lowercase			,      self.hparams.model_name_or_path.split("""/""" ) ) ).pop()			,      str(self.hparams.max_seq_length )			,      )			,      )




   @pl.utilities.rank_zero_only
   def       __a		(       self     :							Any			,      _lowercase     :							Dict[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.output_dir.joinpath("""best_tfmr""" )
     SCREAMING_SNAKE_CASE__     				= self.step_count
     self.model.save_pretrained(_lowercase )
     self.tokenizer.save_pretrained(_lowercase )




   @staticmethod
   def       __a		(       _lowercase     :							Any			,      _lowercase     :							List[Any] ):



     """simple docstring"""






     parser.add_argument(
         """--model_name_or_path"""			,      default=_lowercase			,      type=_lowercase			,      required=_lowercase			,      help="""Path to pretrained model or model identifier from huggingface.co/models"""			,      )
     parser.add_argument(
         """--config_name"""			,      default=""""""			,      type=_lowercase			,      help="""Pretrained config name or path if not the same as model_name""" )
     parser.add_argument(
         """--tokenizer_name"""			,      default=_lowercase			,      type=_lowercase			,      help="""Pretrained tokenizer name or path if not the same as model_name"""			,      )
     parser.add_argument(
         """--cache_dir"""			,      default=""""""			,      type=_lowercase			,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""			,      )
     parser.add_argument(
         """--encoder_layerdrop"""			,      type=_lowercase			,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""			,      )
     parser.add_argument(
         """--decoder_layerdrop"""			,      type=_lowercase			,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""			,      )
     parser.add_argument(
         """--dropout"""			,      type=_lowercase			,      help="""Dropout probability (Optional). Goes into model.config"""			,      )
     parser.add_argument(
         """--attention_dropout"""			,      type=_lowercase			,      help="""Attention dropout probability (Optional). Goes into model.config"""			,      )
     parser.add_argument("""--learning_rate"""			,      default=5E-5			,      type=_lowercase			,      help="""The initial learning rate for Adam.""" )
     parser.add_argument(
         """--lr_scheduler"""			,      default="""linear"""			,      choices=_lowercase			,      metavar=_lowercase			,      type=_lowercase			,      help="""Learning rate scheduler"""			,      )
     parser.add_argument("""--weight_decay"""			,      default=0.0			,      type=_lowercase			,      help="""Weight decay if we apply some.""" )
     parser.add_argument("""--adam_epsilon"""			,      default=1E-8			,      type=_lowercase			,      help="""Epsilon for Adam optimizer.""" )
     parser.add_argument("""--warmup_steps"""			,      default=0			,      type=_lowercase			,      help="""Linear warmup over warmup_steps.""" )
     parser.add_argument("""--num_workers"""			,      default=4			,      type=_lowercase			,      help="""kwarg passed to DataLoader""" )
     parser.add_argument("""--num_train_epochs"""			,      dest="""max_epochs"""			,      default=3			,      type=_lowercase )
     parser.add_argument("""--train_batch_size"""			,      default=32			,      type=_lowercase )
     parser.add_argument("""--eval_batch_size"""			,      default=32			,      type=_lowercase )
     parser.add_argument("""--adafactor"""			,      action="""store_true""" )







class __snake_case							(		pl.Callback						):




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Tuple			,      _lowercase     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= trainer.lr_schedulers[0]["""scheduler"""]
     SCREAMING_SNAKE_CASE__     				= {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
     pl_module.logger.log_metrics(_lowercase )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							pl.Trainer			,      _lowercase     :							pl.LightningModule ):



     """simple docstring"""






     rank_zero_info("""***** Validation results *****""" )
     SCREAMING_SNAKE_CASE__     				= trainer.callback_metrics
     # Log results
     for key in sorted(_lowercase ):
       if key not in ["log", "progress_bar"]:
         rank_zero_info("""{} = {}\n""".format(_lowercase			,      str(metrics[key] ) ) )




   def       __a		(       self     :							str			,      _lowercase     :							pl.Trainer			,      _lowercase     :							pl.LightningModule ):



     """simple docstring"""






     rank_zero_info("""***** Test results *****""" )
     SCREAMING_SNAKE_CASE__     				= trainer.callback_metrics
     # Log and save results to file
     SCREAMING_SNAKE_CASE__     				= os.path.join(pl_module.hparams.output_dir			,      """test_results.txt""" )
     with open(_lowercase			,      """w""" ) as writer:
       for key in sorted(_lowercase ):
         if key not in ["log", "progress_bar"]:
           rank_zero_info("""{} = {}\n""".format(_lowercase			,      str(metrics[key] ) ) )
           writer.write("""{} = {}\n""".format(_lowercase			,      str(metrics[key] ) ) )





def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			Optional[Any]      )				->   None:







  """simple docstring"""





  parser.add_argument(
      """--output_dir"""					,	default=__UpperCamelCase					,	type=__UpperCamelCase					,	required=__UpperCamelCase					,	help="""The output directory where the model predictions and checkpoints will be written."""					,	)
  parser.add_argument(
      """--fp16"""					,	action="""store_true"""					,	help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""					,	)

  parser.add_argument(
      """--fp16_opt_level"""					,	type=__UpperCamelCase					,	default="""O2"""					,	help=(
          """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      )					,	)
  parser.add_argument("""--n_tpu_cores"""					,	dest="""tpu_cores"""					,	type=__UpperCamelCase      )
  parser.add_argument("""--max_grad_norm"""					,	dest="""gradient_clip_val"""					,	default=1.0					,	type=__UpperCamelCase					,	help="""Max gradient norm"""      )
  parser.add_argument("""--do_train"""					,	action="""store_true"""					,	help="""Whether to run training."""      )
  parser.add_argument("""--do_predict"""					,	action="""store_true"""					,	help="""Whether to run predictions on the test set."""      )
  parser.add_argument(
      """--gradient_accumulation_steps"""					,	dest="""accumulate_grad_batches"""					,	type=__UpperCamelCase					,	default=1					,	help="""Number of updates steps to accumulate before performing a backward/update pass."""					,	)
  parser.add_argument("""--seed"""					,	type=__UpperCamelCase					,	default=42					,	help="""random seed for initialization"""      )
  parser.add_argument(
      """--data_dir"""					,	default=__UpperCamelCase					,	type=__UpperCamelCase					,	required=__UpperCamelCase					,	help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""					,	)






def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			BaseTransformer					,	__UpperCamelCase       :			argparse.Namespace					,	__UpperCamelCase       :			str=None					,	__UpperCamelCase       :			List[str]=True					,	__UpperCamelCase       :			str=[]					,	__UpperCamelCase       :			str=None					,	__UpperCamelCase       :			str=None					,	**__UpperCamelCase       :			Tuple					,	)				->   List[Any]:







  """simple docstring"""





  pl.seed_everything(args.seed      )

  # init model
  SCREAMING_SNAKE_CASE__     				= Path(model.hparams.output_dir      )
  odir.mkdir(exist_ok=__UpperCamelCase      )

  # add custom checkpoints
  if checkpoint_callback is None:
    SCREAMING_SNAKE_CASE__     				= pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir					,	prefix="""checkpoint"""					,	monitor="""val_loss"""					,	mode="""min"""					,	save_top_k=1      )
  if early_stopping_callback:
    extra_callbacks.append(__UpperCamelCase      )
  if logging_callback is None:
    SCREAMING_SNAKE_CASE__     				= LoggingCallback()

  SCREAMING_SNAKE_CASE__     				= {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    SCREAMING_SNAKE_CASE__     				= 16
    SCREAMING_SNAKE_CASE__     				= args.fpaa_opt_level

  if args.gpus > 1:
    SCREAMING_SNAKE_CASE__     				= """ddp"""

  SCREAMING_SNAKE_CASE__     				= args.accumulate_grad_batches
  SCREAMING_SNAKE_CASE__     				= extra_train_kwargs.get("""accelerator"""					,	__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= extra_train_kwargs.get("""profiler"""					,	__UpperCamelCase      )

  SCREAMING_SNAKE_CASE__     				= pl.Trainer.from_argparse_args(
      __UpperCamelCase					,	weights_summary=__UpperCamelCase					,	callbacks=[logging_callback] + extra_callbacks					,	logger=__UpperCamelCase					,	checkpoint_callback=__UpperCamelCase					,	**__UpperCamelCase					,	)

  if args.do_train:
    trainer.fit(__UpperCamelCase      )

  return trainer

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				StableDiffusionModelEditingPipeline
   lowerCAmelCase_								=				TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= 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			,      )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler()
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= 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			,      )
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= CLIPTextConfig(
         bos_token_id=0			,      eos_token_id=2			,      hidden_size=32			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=10_00			,      )
     SCREAMING_SNAKE_CASE__     				= CLIPTextModel(_lowercase )
     SCREAMING_SNAKE_CASE__     				= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

     SCREAMING_SNAKE_CASE__     				= {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         # Setting height and width to None to prevent OOMs on CPU.
         """height""": None,
         """width""": None,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]
     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= """french fries"""
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase			,      negative_prompt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= output.images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= PNDMScheduler()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     # the pipeline does not expect pndm so test if it raises error.
     with self.assertRaises(_lowercase ):
       SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






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




   def       __a		(       self     :							Any ):



     """simple docstring"""






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







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




   def       __a		(       self     :							str ):



     """simple docstring"""






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




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(_lowercase			,      safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__     				= np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58] )

     assert np.abs(expected_slice - image_slice ).max() < 1E-2

     # make sure image changes after editing
     pipe.edit_model("""A pack of roses"""			,      """A pack of blue roses""" )

     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     assert np.abs(expected_slice - image_slice ).max() > 1E-1




   def       __a		(       self     :							Any ):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_pretrained(_lowercase			,      subfolder="""scheduler""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(
         _lowercase			,      scheduler=_lowercase			,      safety_checker=_lowercase )
     SCREAMING_SNAKE_CASE__     				= pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.cuda.max_memory_allocated()
     # make sure that less than 4.4 GB is allocated
     assert mem_bytes < 4.4 * 10**9
