
"""simple docstring"""


import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class    A_     (					snake_case_       ):



							"""simple docstring"""






							def __init__(  self  :str					,							lowerCamelCase_  :Any					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :List[str]=False					,							lowerCamelCase_  :Union[str, Any]=1_024					,							lowerCamelCase_  :Any=1_024					,							lowerCamelCase_  :Tuple=3.6					,							lowerCamelCase_  :int=False					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=tokenizer
										lowerCamelCase__     :					int		=tokenizer.bos_token_id
										lowerCamelCase__     :					Tuple		=dataset
										lowerCamelCase__     :					Any		=seq_length
										lowerCamelCase__     :					List[str]		=0
										lowerCamelCase__     :					List[str]		=infinite
										lowerCamelCase__     :					str		=0
										lowerCamelCase__     :					List[Any]		=tokenized

										if self.tokenized:
													lowerCamelCase__     :					List[str]		=seq_length * num_of_sequences
													lowerCamelCase__     :					Any		="input_ids"
										else:
													lowerCamelCase__     :					int		=seq_length * chars_per_token * num_of_sequences
													lowerCamelCase__     :					Optional[Any]		="content"





							def __iter__(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=iter(self.dataset	)
										lowerCamelCase__     :					Union[str, Any]		=True
										while more_examples:
													lowerCamelCase__     :					int		=[], 0
													while True:
																if buffer_len >= self.max_buffer_size:
																			break
																try:
																			buffer.append(next(lowerCamelCase_	)[self.content_field]	)
																			buffer_len += len(buffer[-1]	)
																except StopIteration:
																			if self.infinite:
																						lowerCamelCase__     :					List[str]		=iter(self.dataset	)
																						self.epoch += 1
																						logger.info(f"""Dataset epoch: {self.epoch}"""	)
																			else:
																						lowerCamelCase__     :					List[Any]		=False
																						break
													if self.tokenized:
																lowerCamelCase__     :					List[Any]		=buffer
													else:
																lowerCamelCase__     :					Dict		=self.tokenizer(lowerCamelCase_					,							truncation=lowerCamelCase_	)["input_ids"]
													lowerCamelCase__     :					int		=[]
													for tokenized_input in tokenized_inputs:
																all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
													for i in range(0					,							len(lowerCamelCase_	)					,							self.seq_length	):
																lowerCamelCase__     :					str		=all_token_ids[i : i + self.seq_length]
																if len(lowerCamelCase_	) == self.seq_length:
																			self.current_size += 1
																			yield torch.tensor(lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Dict					,							lowerCamelCase_  :Optional[Any]=1_000	):





										"""simple docstring"""

										return ShufflerIterDataPipe(self					,							buffer_size=lowerCamelCase_	)






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->Tuple:
			lowerCamelCase__     :					List[Any]		=args.model_ckpt.split('/'      )[-1]
			lowerCamelCase__     :					List[Any]		=logging.getLogger(__name__      )
			lowerCamelCase__     :					Tuple		=Path(args.save_dir      ) / "log/"
			log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
			lowerCamelCase__     :					Optional[Any]		=f"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,   datefmt='%m/%d/%Y %H:%M:%S'							,   level=logging.INFO							,   handlers=[logging.FileHandler(log_dir / filename      ), logging.StreamHandler()]							,   )
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(SCREAMING_SNAKE_CASE_							,   vars(SCREAMING_SNAKE_CASE_      )      )
						lowerCamelCase__     :					Any		=accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO      )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						lowerCamelCase__     :					Dict		=""
						logger.setLevel(logging.ERROR      )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name






def        lowerCAmelCase_       (		snake_case_		:      Optional[Any]      )       ->List[Any]:
			lowerCamelCase__     :					Optional[int]		={"streaming": True}
			lowerCamelCase__     :					List[str]		=load_dataset(args.dataset_name_train							,   split='train'							,   **SCREAMING_SNAKE_CASE_      )
			lowerCamelCase__     :					Dict		=train_data.shuffle(buffer_size=args.shuffle_buffer							,   seed=args.seed      )
			lowerCamelCase__     :					Optional[Any]		=load_dataset(args.dataset_name_valid							,   split='train'							,   **SCREAMING_SNAKE_CASE_      )
			lowerCamelCase__     :					Optional[Any]		=ConstantLengthDataset(
			    SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   infinite=SCREAMING_SNAKE_CASE_							,   seq_length=args.seq_length							,   tokenized=args.tokenized      )
			lowerCamelCase__     :					Tuple		=ConstantLengthDataset(
			    SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_							,   infinite=SCREAMING_SNAKE_CASE_							,   seq_length=args.seq_length							,   tokenized=args.tokenized      )
			lowerCamelCase__     :					str		=train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
			lowerCamelCase__     :					List[Any]		=DataLoader(SCREAMING_SNAKE_CASE_							,   batch_size=args.train_batch_size							,   shuffle=SCREAMING_SNAKE_CASE_      )
			lowerCamelCase__     :					List[str]		=DataLoader(SCREAMING_SNAKE_CASE_							,   batch_size=args.valid_batch_size      )
			return train_dataloader, eval_dataloader






def        lowerCAmelCase_       (		snake_case_		:      Optional[Any]							,   snake_case_		:      str							,   snake_case_		:      Optional[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      )       ->Any:
			lowerCamelCase__     :					Any		=[], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay      ):
									params_without_wd.append(SCREAMING_SNAKE_CASE_      )
						else:
									params_with_wd.append(SCREAMING_SNAKE_CASE_      )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]






def        lowerCAmelCase_       (		snake_case_		:      Any							,   snake_case_		:      Optional[Any]      )       ->Union[str, Any]:
			logger.info(f"""Step {step}: {metrics}"""      )
			if accelerator.is_main_process:
						accelerator.log(SCREAMING_SNAKE_CASE_							,   SCREAMING_SNAKE_CASE_      )






def        lowerCAmelCase_       (		snake_case_		:      Tuple							,   snake_case_		:      Any							,   snake_case_		:      Optional[int]      )       ->Dict:
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowerCamelCase__     :					str		=accelerator.unwrap_model(SCREAMING_SNAKE_CASE_      ).config
			lowerCamelCase__     :					Optional[int]		=4 if args.gradient_checkpointing else 3
			lowerCamelCase__     :					int		=args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCamelCase__     :					Optional[int]		=2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCamelCase__     :					List[str]		=factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			lowerCamelCase__     :					List[Any]		=flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
			return tflops






def        lowerCAmelCase_       (		snake_case_		:      List[Any]      )       ->Dict:
			model.eval()
			lowerCamelCase__     :					Any		=[]
			for step, batch in enumerate(SCREAMING_SNAKE_CASE_      ):
						with torch.no_grad():
									lowerCamelCase__     :					str		=model(SCREAMING_SNAKE_CASE_							,   labels=SCREAMING_SNAKE_CASE_      )
						lowerCamelCase__     :					Union[str, Any]		=outputs.loss.repeat(args.valid_batch_size      )
						losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_      )      )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			lowerCamelCase__     :					Any		=torch.cat(SCREAMING_SNAKE_CASE_      )
			lowerCamelCase__     :					Union[str, Any]		=losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCamelCase__     :					Tuple		=torch.exp(SCREAMING_SNAKE_CASE_      )
			except OverflowError:
						lowerCamelCase__     :					Any		=float('inf'      )
			return loss.item(), perplexity.item()


# Settings
lowerCAmelCase								=						HfArgumentParser(TrainingArguments)
lowerCAmelCase								=						parser.parse_args()

# Accelerator
lowerCAmelCase								=						ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
lowerCAmelCase								=						Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
lowerCAmelCase								=						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCAmelCase								=						Namespace(**vars(args), **acc_state)
lowerCAmelCase								=						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
							lowerCAmelCase								=						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCAmelCase								=						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
							hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
lowerCAmelCase								=						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
							model.gradient_checkpointing_enable()
lowerCAmelCase								=						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCAmelCase								=						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCAmelCase								=						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCAmelCase								=						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)






def        lowerCAmelCase_       (		)       ->int:
			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCAmelCase								=						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
							if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
														accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
														accelerator.load_state(args.resume_from_checkpoint)
														lowerCAmelCase								=						os.path.basename(args.resume_from_checkpoint)
							else:
														# Get the most recent checkpoint
														lowerCAmelCase								=						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and '''step''' in str(f)]
														dirs.sort(key=os.path.getctime)
														lowerCAmelCase								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
							# Extract the step of the checkpoint to continue from there
							lowerCAmelCase								=						os.path.splitext(path)[0]
							lowerCAmelCase								=						int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
lowerCAmelCase								=						0
lowerCAmelCase								=						time.time()
lowerCAmelCase								=						0
for step, batch in enumerate(train_dataloader, start=1):
							if args.resume_from_checkpoint and step < resume_step:
														continue  # we need to skip steps until we reach the resumed step
							lowerCAmelCase								=						model(batch, labels=batch, use_cache=False).loss
							lowerCAmelCase								=						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
							loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
							log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
							lowerCAmelCase								=						loss / args.gradient_accumulation_steps
							if step % args.gradient_accumulation_steps != 0:
														# Prevent backward from doing gradient all_reduce in every step
														if accelerator.distributed_type == DistributedType.MULTI_GPU:
																					with model.no_sync():
																												accelerator.backward(loss)
														else:
																					accelerator.backward(loss)
							else:
														lowerCAmelCase								=						get_lr()
														accelerator.backward(loss)
														accelerator.clip_grad_norm_(model.parameters(), 1.0)
														optimizer.step()
														lr_scheduler.step()
														optimizer.zero_grad()
														lowerCAmelCase								=						time.time() - t_start
														lowerCAmelCase								=						compute_tflops(elapsed_time, accelerator, args)
														log_metrics(
														    step,
														    {
														        """steps""": completed_steps,
														        """loss/train""": loss_tracking,
														        """lr""": lr,
														        """tflops""": tflops,
														        """time_per_iteration""": elapsed_time,
														    },
														)
														lowerCAmelCase								=						time.time()
														lowerCAmelCase								=						0
														completed_steps += 1
							if step % args.save_checkpoint_steps == 0:
														logger.info("""Evaluating and saving model checkpoint""")
														lowerCAmelCase								=						evaluate(args)
														log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
														accelerator.wait_for_everyone()
														lowerCAmelCase								=						os.path.join(args.save_dir, f"""step_{step}""")
														accelerator.save_state(save_dir)
														if accelerator.is_main_process:
																					hf_repo.push_to_hub(commit_message=f"""step {step}""")
														model.train()
							if completed_steps >= args.max_train_steps:
														break

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCAmelCase								=						evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCAmelCase								=						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCAmelCase								=						os.path.join(args.save_dir, f"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
							hf_repo.push_to_hub(commit_message="""final model""")

"""simple docstring"""


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_     (					A__				, A__				, A__				, unittest.TestCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       StableDiffusionImageVariationPipeline
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_PARAMS
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_BATCH_PARAMS
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )
							# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										torch.manual_seed(0	)
										lowerCamelCase__     :					int		=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					,							)
										lowerCamelCase__     :					int		=PNDMScheduler(skip_prk_steps=lowerCamelCase_	)
										torch.manual_seed(0	)
										lowerCamelCase__     :					Optional[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					,							)
										torch.manual_seed(0	)
										lowerCamelCase__     :					Optional[int]		=CLIPVisionConfig(
										    hidden_size=32					,							projection_dim=32					,							intermediate_size=37					,							layer_norm_eps=1e-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							image_size=32					,							patch_size=4					,							)
										lowerCamelCase__     :					Optional[Any]		=CLIPVisionModelWithProjection(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=CLIPImageProcessor(crop_size=32					,							size=32	)

										lowerCamelCase__     :					Optional[int]		={
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'image_encoder': image_encoder,
										    'feature_extractor': feature_extractor,
										    'safety_checker': None,
										}
										return components





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Optional[int]=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=floats_tensor((1, 3, 32, 32)					,							rng=random.Random(lowerCamelCase_	)	)
										lowerCamelCase__     :					Optional[Any]		=image.cpu().permute(0					,							2					,							3					,							1	)[0]
										lowerCamelCase__     :					List[Any]		=Image.fromarray(np.uinta(lowerCamelCase_	)	).convert('RGB'	).resize((32, 32)	)
										if str(lowerCamelCase_	).startswith('mps'	):
													lowerCamelCase__     :					int		=torch.manual_seed(lowerCamelCase_	)
										else:
													lowerCamelCase__     :					Tuple		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					Any		={
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__     :					str		='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__     :					List[str]		=self.get_dummy_components()
										lowerCamelCase__     :					Union[str, Any]		=StableDiffusionImageVariationPipeline(**lowerCamelCase_	)
										lowerCamelCase__     :					int		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					str		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=sd_pipe(**lowerCamelCase_	).images
										lowerCamelCase__     :					Tuple		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__     :					str		=np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]	)

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





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__     :					Any		=self.get_dummy_components()
										lowerCamelCase__     :					Any		=StableDiffusionImageVariationPipeline(**lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Any		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Union[str, Any]		=2 * [inputs['image']]
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					str		=output.images

										lowerCamelCase__     :					str		=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 64, 64, 3)
										lowerCamelCase__     :					int		=np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]	)

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





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

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






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



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Union[str, Any]					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :Optional[Any]="cpu"					,							lowerCamelCase_  :Any=torch.floataa					,							lowerCamelCase_  :str=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					Tuple		=load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
										lowerCamelCase__     :					str		=np.random.RandomState(lowerCamelCase_	).standard_normal((1, 4, 64, 64)	)
										lowerCamelCase__     :					List[str]		=torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_					,							dtype=lowerCamelCase_	)
										lowerCamelCase__     :					str		={
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										lowerCamelCase__     :					Tuple		=StableDiffusionImageVariationPipeline.from_pretrained(
										    'lambdalabs/sd-image-variations-diffusers'					,							safety_checker=lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Union[str, Any]		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe(**lowerCamelCase_	).images
										lowerCamelCase__     :					Optional[int]		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__     :					List[str]		=np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]	)
										assert np.abs(image_slice - expected_slice	).max() < 6e-3





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=0

										def callback_fn(lowerCamelCase_  :int					,							lowerCamelCase_  :int					,							lowerCamelCase_  :torch.FloatTensor	) -> None:
													lowerCamelCase__     :					Union[str, Any]		=True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																lowerCamelCase__     :					Dict		=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase__     :					str		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					str		=np.array(
																    [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]	)

																assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
													elif step == 2:
																lowerCamelCase__     :					Union[str, Any]		=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase__     :					Dict		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					Optional[Any]		=np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]	)

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

										lowerCamelCase__     :					str		=False

										lowerCamelCase__     :					Optional[Any]		=StableDiffusionImageVariationPipeline.from_pretrained(
										    'fusing/sd-image-variations-diffusers'					,							safety_checker=lowerCamelCase_					,							torch_dtype=torch.floataa					,							)
										pipe.to(lowerCamelCase_	)
										pipe.set_progress_bar_config(disable=lowerCamelCase_	)
										pipe.enable_attention_slicing()

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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

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

										lowerCamelCase__     :					str		='fusing/sd-image-variations-diffusers'
										lowerCamelCase__     :					Union[str, Any]		=StableDiffusionImageVariationPipeline.from_pretrained(
										    lowerCamelCase_					,							safety_checker=lowerCamelCase_					,							torch_dtype=torch.floataa	)
										lowerCamelCase__     :					Any		=pipe.to(lowerCamelCase_	)
										pipe.set_progress_bar_config(disable=lowerCamelCase_	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase__     :					Dict		=self.get_inputs(lowerCamelCase_					,							dtype=torch.floataa	)
										lowerCamelCase__     :					Tuple		=pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					Any		=torch.cuda.max_memory_allocated()
										# make sure that less than 2.6 GB is allocated
										assert mem_bytes < 2.6 * 10**9






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



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :Union[str, Any]="cpu"					,							lowerCamelCase_  :Union[str, Any]=torch.floataa					,							lowerCamelCase_  :Union[str, Any]=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
										lowerCamelCase__     :					int		=np.random.RandomState(lowerCamelCase_	).standard_normal((1, 4, 64, 64)	)
										lowerCamelCase__     :					List[str]		=torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_					,							dtype=lowerCamelCase_	)
										lowerCamelCase__     :					str		={
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 50,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Optional[Any]		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe(**lowerCamelCase_	).images[0]

										lowerCamelCase__     :					Optional[int]		=load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'	)
										lowerCamelCase__     :					Dict		=np.abs(expected_image - image	).max()
										assert max_diff < 1e-3





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										lowerCamelCase__     :					Optional[Any]		=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Tuple		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					int		=25
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	).images[0]

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