


'''simple docstring'''





import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase__      =							1_6
UpperCAmelCase__      =							3_2






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):






			"""simple docstring"""




			return int(x / 2**20   )
class 	a__				:





			'''simple docstring'''

			def __enter__(     self       : str  )			->		Optional[Any]:
						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__A=  torch.cuda.memory_allocated()
						__A=  psutil.Process()

						__A=  self.cpu_mem_used()
						__A=  True
						__A=  threading.Thread(target=self.peak_monitor_func  )
						__A=  True
						peak_monitor_thread.start()
						return self
			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
						return self.process.memory_info().rss
			def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
						__A=  -1

						while True:
									__A=  max(self.cpu_mem_used() ,						self.cpu_peak  )

									# can't sleep or will not catch the peak right (this comment is here on purpose)
									# time.sleep(0.001) # 1msec

									if not self.peak_monitoring:
												break

			def __exit__(     self       : List[Any] ,						*lowerCAmelCase_       : Dict  )			->		List[Any]:
						__A=  False

						gc.collect()
						torch.cuda.empty_cache()
						__A=  torch.cuda.memory_allocated()
						__A=  torch.cuda.max_memory_allocated()
						__A=  bamb(self.end - self.begin  )
						__A=  bamb(self.peak - self.begin  )

						__A=  self.cpu_mem_used()
						__A=  bamb(self.cpu_end - self.cpu_begin  )
						__A=  bamb(self.cpu_peak - self.cpu_begin  )
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					UpperCAmelCase__      =							mocked_dataloaders  # noqa: F811






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int]   ):






			"""simple docstring"""




			if os.environ.get('TESTING_MOCKED_DATALOADERS',_SCREAMING_SNAKE_CASE   ) == "1":
						__A=  2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			__A=  FullyShardedDataParallelPlugin(
			    state_dict_config=FullStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),)

			# Initialize accelerator
			if args.with_tracking:
						__A=  Accelerator(
						    cpu=args.cpu,mixed_precision=args.mixed_precision,log_with='wandb',project_dir=args.logging_dir,fsdp_plugin=_SCREAMING_SNAKE_CASE,)
			else:
						__A=  Accelerator(fsdp_plugin=_SCREAMING_SNAKE_CASE   )
			accelerator.print(accelerator.distributed_type   )

			if hasattr(args.checkpointing_steps,'isdigit'   ):
						if args.checkpointing_steps == "epoch":
									__A=  args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									__A=  int(args.checkpointing_steps   )
						else:
									raise ValueError(
									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
			else:
						__A=  None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__A=  config['lr']
			__A=  int(config['num_epochs']   )
			__A=  int(config['seed']   )
			__A=  int(config['batch_size']   )

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						__A=  vars(_SCREAMING_SNAKE_CASE   )
						accelerator.init_trackers('fsdp_glue_no_trainer',_SCREAMING_SNAKE_CASE   )

			__A=  AutoTokenizer.from_pretrained(args.model_name_or_path   )
			__A=  load_dataset('glue','mrpc'   )
			__A=  evaluate.load('glue','mrpc'   )

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

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

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			__A=  tokenized_datasets.rename_column('label','labels'   )

			# If the batch size is too big we use gradient accumulation
			__A=  1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__A=  batch_size // MAX_GPU_BATCH_SIZE
						__A=  MAX_GPU_BATCH_SIZE

			def collate_fn(_SCREAMING_SNAKE_CASE				:   int   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__A=  128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__A=  16
						elif accelerator.mixed_precision != "no":
									__A=  8
						else:
									__A=  None

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

			# Instantiate dataloaders.
			__A=  DataLoader(
			    tokenized_datasets['train'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
			__A=  DataLoader(
			    tokenized_datasets['validation'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )

			set_seed(_SCREAMING_SNAKE_CASE   )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__A=  AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path,return_dict=_SCREAMING_SNAKE_CASE,low_cpu_mem_usage=_SCREAMING_SNAKE_CASE   )

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			__A=  accelerator.prepare(_SCREAMING_SNAKE_CASE   )
			accelerator.print(_SCREAMING_SNAKE_CASE   )

			# Instantiate optimizer
			# New Code #
			# For FSDP feature, at present it doesn't support multiple parameter groups,
			# so we need to create a single parameter group for the whole model
			__A=  torch.optim.AdamW(params=model.parameters(),lr=_SCREAMING_SNAKE_CASE,weight_decay=2e-4   )

			# Instantiate scheduler
			__A=  get_linear_schedule_with_warmup(
			    optimizer=_SCREAMING_SNAKE_CASE,num_warmup_steps=10,num_training_steps=(len(_SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps,)

			# New Code #
			# For FSDP feature, prepare everything except the model as we have already prepared the model
			# before creating the optimizer
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			__A,  __A,  __A,  __A=  accelerator.prepare(
			    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )

			__A=  0

			# Potentially 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   )
									__A=  os.path.basename(args.resume_from_checkpoint   )
						else:
									# Get the most recent checkpoint
									__A=  [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
									dirs.sort(key=os.path.getctime   )
									__A=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						__A=  os.path.splitext(_SCREAMING_SNAKE_CASE   )[0]

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace('epoch_',''   )   )
									__A=  None
						else:
									__A=  int(training_difference.replace('step_',''   )   )
									num_epochs -= resume_step // len(_SCREAMING_SNAKE_CASE   )
									# If resuming by step, we also need to know exactly how far into the DataLoader we went
									__A=  (num_epochs * len(_SCREAMING_SNAKE_CASE   )) - resume_step

    # Now we train the model
			for epoch in range(_SCREAMING_SNAKE_CASE   ):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												__A=  0
									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
												# We need to skip steps until we reach the resumed step
												if args.resume_from_checkpoint and epoch == 0:
															if resume_step is not None and step < resume_step:
																		pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												__A=  model(**_SCREAMING_SNAKE_CASE   )
												__A=  outputs.loss
												__A=  loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(_SCREAMING_SNAKE_CASE   )
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# accelerator.print(lr_scheduler.get_lr())

												overall_step += 1

												if isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ):
															__A=  f"""step_{overall_step}"""
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
																		accelerator.save_state(_SCREAMING_SNAKE_CASE   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin   )   )   )
						accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used   )   )
						accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked   )   )
						accelerator.print(
						    'Total Peak Memory consumed during the train (max): {}'.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin   ),
									    },step=_SCREAMING_SNAKE_CASE,)

						# New Code #
						# context manager to track the peak memory usage during the evaluation
						with TorchTracemalloc() as tracemalloc:
									model.eval()
									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												with torch.no_grad():
															__A=  model(**_SCREAMING_SNAKE_CASE   )
												__A=  outputs.logits.argmax(dim=-1   )
												__A,  __A=  accelerator.gather_for_metrics((predictions, batch['labels'])   )
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE,references=_SCREAMING_SNAKE_CASE,)

									__A=  metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f"""epoch {epoch}:""",_SCREAMING_SNAKE_CASE   )
									if args.with_tracking:
												accelerator.log(
												    {
												        'accuracy': eval_metric['accuracy'],
												        'f1': eval_metric['f1'],
												        'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE   ),
												    },step=_SCREAMING_SNAKE_CASE,)

									if checkpointing_steps == "epoch":
												__A=  f"""epoch_{epoch}"""
												if args.output_dir is not None:
															__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
												accelerator.save_state(_SCREAMING_SNAKE_CASE   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin   )   )   )
						accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used   )   )
						accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked   )   )
						accelerator.print(
						    'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin   ),
									    },step=_SCREAMING_SNAKE_CASE,)

			if args.with_tracking:
						accelerator.end_training()






def 	UpperCAmelCase__(							):






			"""simple docstring"""




			__A=  argparse.ArgumentParser(description='Simple example of training script.'   )
			parser.add_argument(
			    '--mixed_precision',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,choices=['no', 'fp16', 'bf16', 'fp8'],help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.',)
			parser.add_argument('--cpu',action='store_true',help='If passed, will train on the CPU.'   )
			parser.add_argument(
			    '--checkpointing_steps',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',)
			parser.add_argument(
			    '--resume_from_checkpoint',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='If the training should continue from a checkpoint folder.',)
			parser.add_argument(
			    '--with_tracking',action='store_true',help='Whether to load in all available experiment trackers from the environment and use them for logging.',)
			parser.add_argument(
			    '--output_dir',type=_SCREAMING_SNAKE_CASE,default='.',help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',)
			parser.add_argument(
			    '--logging_dir',type=_SCREAMING_SNAKE_CASE,default='logs',help='Location on where to store experiment tracking logs`',)
			parser.add_argument(
			    '--model_name_or_path',type=_SCREAMING_SNAKE_CASE,help='Path to pretrained model or model identifier from huggingface.co/models.',required=_SCREAMING_SNAKE_CASE,)
			__A=  parser.parse_args()
			__A=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
					main()







'''simple docstring'''





# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
						torch.manual_seed(0  )
						__A=  UNetaDConditionModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=32 ,						)
						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2
						__A=  randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Optional[int]:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : List[Any]  )			->		str:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )


class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							int							= frozenset([]      )
			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
						torch.manual_seed(0  )
						__A=  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 ,						)
						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components


class 	a__				(				a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
						torch.manual_seed(0  )
						__A=  UNetaDConditionModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=32 ,						)
						torch.manual_seed(0  )

						def init_weights(lowerCAmelCase_       : Optional[int]  ):
									if isinstance(lowerCAmelCase_ ,						torch.nn.Convad  ):
												torch.nn.init.normal(m.weight  )
												m.bias.data.fill_(1.0  )

						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )

						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )

						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  MultiControlNetModel([controlneta, controlneta]  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2

						__A=  [
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
						]
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )

						__A=  10.0
						__A=  4

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.1 ,						control_guidance_end=0.2  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=[0.1, 0.3] ,						control_guidance_end=[0.2, 0.7]  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.4 ,						control_guidance_end=[0.5, 0.8]  )[0]

						# make sure that all outputs are different
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
			def 				lowerCAmelCase    (     self       : str  )			->		Any:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
			def 				lowerCAmelCase    (     self       : Any  )			->		int:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
						with tempfile.TemporaryDirectory() as tmpdir:
									try:
												# save_pretrained is not implemented for Multi-ControlNet
												pipe.save_pretrained(lowerCAmelCase_  )
									except NotImplementedError:
												pass


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





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(0  )
						__A=  load_image(
						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_inpaint/input_bench_mask.png'  ).resize((512, 512)  )

						__A=  'pitch black hole'

						__A=  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((512, 512)  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)

						__A=  output.images[0]

						assert image.shape == (512, 512, 3)

						__A=  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2

			def 				lowerCAmelCase    (     self       : str  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
						__A=  init_image.resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
						__A=  mask_image.resize((512, 512)  )

						__A=  'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
									__A=  np.array(image_mask.convert('L'  )  ).astype(np.floataa  ) / 2_55.0

									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
									__A=  -1.0  # set as masked pixel
									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
									__A=  torch.from_numpy(lowerCAmelCase_  )
									return image

						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						guidance_scale=9.0 ,						eta=1.0 ,						generator=lowerCAmelCase_ ,						num_inference_steps=20 ,						output_type='np' ,						)
						__A=  output.images[0]

						assert image.shape == (512, 512, 3)

						__A=  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2



