





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       =	logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_lowerCamelCase       =	{
    '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       =	{
    '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       =	sorted(arg_to_scheduler.keys())
_lowerCamelCase       =	'{' + ', '.join(arg_to_scheduler_choices) + '}'




class      UpperCamelCase_				(   pl.LightningModule			):


							def __init__(							self			:str							, __A			:argparse.Namespace							, __A			:Optional[Any]=None							, __A			:Dict="base"							, __A			:Optional[Any]=None							, __A			:Tuple=None							, __A			:Dict=None							, **__A			:List[Any]							, )      ->      Optional[Any]:



									"""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(__A						)
									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=__A							, **__A							, )
									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							, __A							, __A						):
													assert hasattr(self.config							, __A						), f'''model config doesn\'t have a `{p}` attribute'''
													setattr(self.config							, __A							, getattr(self.hparams							, __A						)						)

									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=__A							, )
									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=__A							, )
									else:
											SCREAMING_SNAKE_CASE__								=					model


							def    _snake_case     (							self			:int							, *__A			:int							, **__A			:Optional[int]						)      ->      Dict:



									"""simple docstring"""



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


							def    _snake_case     (							self			:Optional[int]						)      ->      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    _snake_case     (							self			:Union[str, Any]						)      ->      Optional[Any]:



									"""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(
											    __A							, lr=self.hparams.learning_rate							, scale_parameter=__A							, relative_step=__A						)

									else:
											SCREAMING_SNAKE_CASE__								=					AdamW(
											    __A							, 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    _snake_case     (							self			:str							, __A			:Optional[Any]							, __A			:int						)      ->      Dict:



									"""simple docstring"""



									return self.validation_step(__A							, __A						)


							def    _snake_case     (							self			:List[Any]							, __A			:Dict						)      ->      int:



									"""simple docstring"""



									return self.validation_end(__A						)


							def    _snake_case     (							self			:Union[str, Any]						)      ->      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    _snake_case     (							self			:Dict							, __A			:Dict						)      ->      Dict:



									"""simple docstring"""



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


							def    _snake_case     (							self			:int							, __A			:str							, __A			:int							, __A			:bool = False						)      ->      Union[str, Any]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:Dict						)      ->      int:



									"""simple docstring"""



									return self.train_loader


							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[Any]						)      ->      Optional[int]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:int							, __A			:Optional[int]						)      ->      Union[str, Any]:



									"""simple docstring"""



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


							@pl.utilities.rank_zero_only
							def    _snake_case     (							self			:Optional[int]							, __A			:Dict[str, Any]						)      ->      None:



									"""simple docstring"""



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


							@staticmethod
							def    _snake_case     (							__A			:Any							, __A			:Tuple						)      ->      str:



									"""simple docstring"""



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




class      UpperCamelCase_				(   pl.Callback			):


							def    _snake_case     (							self			:Tuple							, __A			:str							, __A			:int						)      ->      str:



									"""simple docstring"""



									if (
									    trainer.is_global_zero and trainer.global_rank == 0
									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
											pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class      UpperCamelCase_				(   pl.Callback			):


							def    _snake_case     (							self			:Any							, __A			:int							, __A			:int						)      ->      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(__A						)


							def    _snake_case     (							self			:Optional[Any]							, __A			:pl.Trainer							, __A			:pl.LightningModule						)      ->      Tuple:



									"""simple docstring"""



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


							def    _snake_case     (							self			:Optional[Any]							, __A			:pl.Trainer							, __A			:pl.LightningModule						)      ->      List[str]:



									"""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(__A							, """w"""						) as writer:
											for key in sorted(__A						):
													if key not in ["log", "progress_bar"]:
															rank_zero_info("""{} = {}\n""".format(__A							, str(metrics[key]						)						)						)
															writer.write("""{} = {}\n""".format(__A							, str(metrics[key]						)						)						)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							int			):
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		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__:							Optional[Any]=None    ,      UpperCamelCase__:							Dict=True    ,      UpperCamelCase__:							List[str]=None    ,      UpperCamelCase__:							Dict=[]    ,      UpperCamelCase__:							Optional[int]=None    ,      UpperCamelCase__:							Union[str, Any]=None    ,      **UpperCamelCase__:							Tuple    ,      ):
		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
				# train_params["amp_level"] = args.fp16_opt_level

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

		SCREAMING_SNAKE_CASE__								=					args.accumulate_grad_batches
		SCREAMING_SNAKE_CASE__								=					None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		SCREAMING_SNAKE_CASE__								=					"""auto"""

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

		if args.do_train:
				trainer.fit(UpperCamelCase__			)

		return trainer





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class       __a					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    int									=  StableDiffusionImageVariationPipeline
     _A				:    List[str]									=  IMAGE_VARIATION_PARAMS
     _A				:    List[str]									=  IMAGE_VARIATION_BATCH_PARAMS
     _A				:    Any									=  frozenset([]     )
     # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _A				:    Tuple									=  frozenset([]     )




     def   __A				(	self			: List[str]						)		->   Optional[int]:


         '''simple docstring'''





         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)				,layers_per_block=2				,sample_size=3_2				,in_channels=4				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=3_2				,)
         SCREAMING_SNAKE_CASE__								=PNDMScheduler(skip_prk_steps=_UpperCamelCase						)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=AutoencoderKL(
             block_out_channels=[3_2, 6_4]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=CLIPVisionConfig(
             hidden_size=3_2				,projection_dim=3_2				,intermediate_size=3_7				,layer_norm_eps=1e-05				,num_attention_heads=4				,num_hidden_layers=5				,image_size=3_2				,patch_size=4				,)
         SCREAMING_SNAKE_CASE__								=CLIPVisionModelWithProjection(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=CLIPImageProcessor(crop_size=3_2				,size=3_2						)

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




     def   __A				(	self			: str				,_UpperCamelCase			: Optional[int]				,_UpperCamelCase			: Union[str, Any]=0						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=floats_tensor((1, 3, 3_2, 3_2)				,rng=random.Random(_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__								=image.cpu().permute(0				,2				,3				,1						)[0]
         SCREAMING_SNAKE_CASE__								=Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((3_2, 3_2)						)
         if str(_UpperCamelCase						).startswith("""mps"""						):
             SCREAMING_SNAKE_CASE__								=torch.manual_seed(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: int						)		->   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__								=StableDiffusionImageVariationPipeline(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

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

         assert image.shape == (1, 6_4, 6_4, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]						)

         assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




     def   __A				(	self			: Tuple						)		->   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__								=StableDiffusionImageVariationPipeline(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_dummy_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=2 * [inputs["""image"""]]
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=output.images

         SCREAMING_SNAKE_CASE__								=image[-1, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]						)

         assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




     def   __A				(	self			: Dict						)		->   Dict:


         '''simple docstring'''





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






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


     """simple docstring"""


     def   __A				(	self			: str						)		->   List[str]:


         '''simple docstring'''





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




     def   __A				(	self			: Optional[int]				,_UpperCamelCase			: Dict				,_UpperCamelCase			: Union[str, Any]="cpu"				,_UpperCamelCase			: Tuple=torch.floataa				,_UpperCamelCase			: List[Any]=0						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/input_image_vermeer.png"""						)
         SCREAMING_SNAKE_CASE__								=np.random.RandomState(_UpperCamelCase						).standard_normal((1, 4, 6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__								=torch.from_numpy(_UpperCamelCase						).to(device=_UpperCamelCase				,dtype=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": init_image,
             """latents""": latents,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: List[Any]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             """lambdalabs/sd-image-variations-diffusers"""				,safety_checker=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]						)
         assert np.abs(image_slice - expected_slice						).max() < 6e-3




     def   __A				(	self			: Optional[int]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=0

         def callback_fn(_UpperCamelCase			: int				,_UpperCamelCase			: int				,_UpperCamelCase			: torch.FloatTensor						) -> None:
             SCREAMING_SNAKE_CASE__								=True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                 SCREAMING_SNAKE_CASE__								=latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 SCREAMING_SNAKE_CASE__								=latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__								=np.array(
                     [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]						)

                 assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2
             elif step == 2:
                 SCREAMING_SNAKE_CASE__								=latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 SCREAMING_SNAKE_CASE__								=latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__								=np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]						)

                 assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2

         SCREAMING_SNAKE_CASE__								=False

         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             """fusing/sd-image-variations-diffusers"""				,safety_checker=_UpperCamelCase				,torch_dtype=torch.floataa				,)
         pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase				,dtype=torch.floataa						)
         pipe(**_UpperCamelCase				,callback=_UpperCamelCase				,callback_steps=1						)
         assert callback_fn.has_been_called
         assert number_of_steps == inputs["num_inference_steps"]




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


         '''simple docstring'''





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

         SCREAMING_SNAKE_CASE__								="""fusing/sd-image-variations-diffusers"""
         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             _UpperCamelCase				,safety_checker=_UpperCamelCase				,torch_dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__								=pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)
         pipe.enable_attention_slicing(1						)
         pipe.enable_sequential_cpu_offload()

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase				,dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__								=pipe(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=torch.cuda.max_memory_allocated()
         # make sure that less than 2.6 GB is allocated
         assert mem_bytes < 2.6 * 1_0**9






@nightly
@require_torch_gpu
class       __a					( unittest.TestCase     ):


     """simple docstring"""


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


         '''simple docstring'''





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




     def   __A				(	self			: Dict				,_UpperCamelCase			: List[str]				,_UpperCamelCase			: Union[str, Any]="cpu"				,_UpperCamelCase			: Any=torch.floataa				,_UpperCamelCase			: Tuple=0						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/input_image_vermeer.png"""						)
         SCREAMING_SNAKE_CASE__								=np.random.RandomState(_UpperCamelCase						).standard_normal((1, 4, 6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__								=torch.from_numpy(_UpperCamelCase						).to(device=_UpperCamelCase				,dtype=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": init_image,
             """latents""": latents,
             """generator""": generator,
             """num_inference_steps""": 5_0,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""						)
         sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images[0]

         SCREAMING_SNAKE_CASE__								=load_numpy(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""						)
         SCREAMING_SNAKE_CASE__								=np.abs(expected_image - image						).max()
         assert max_diff < 1e-3




     def   __A				(	self			: List[str]						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""						)
         SCREAMING_SNAKE_CASE__								=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config						)
         sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=2_5
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images[0]

         SCREAMING_SNAKE_CASE__								=load_numpy(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""						)
         SCREAMING_SNAKE_CASE__								=np.abs(expected_image - image						).max()
         assert max_diff < 1e-3






