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
#
########################################################################


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							16
_SCREAMING_SNAKE_CASE:    List[Any]     						=							32






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			return int(x / 2**20	)

class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __enter__(      self     :					Optional[int] ):






					"""simple docstring"""
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_snake_case														=   torch.cuda.memory_allocated()
					_snake_case														=   psutil.Process()

					_snake_case														=   self.cpu_mem_used()
					_snake_case														=   True
					_snake_case														=   threading.Thread(target=self.peak_monitor_func )
					_snake_case														=   True
					peak_monitor_thread.start()
					return self



		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					return self.process.memory_info().rss



		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   -1

					while True:
								_snake_case														=   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     :					int	,       *__SCREAMING_SNAKE_CASE     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   False

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

					_snake_case														=   self.cpu_mem_used()
					_snake_case														=   bamb(self.cpu_end - self.cpu_begin )
					_snake_case														=   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

			_SCREAMING_SNAKE_CASE:    Tuple     						=							mocked_dataloaders  # noqa: F811






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			if os.environ.get("TESTING_MOCKED_DATALOADERS"				,						lowercase_	) == "1":
						_snake_case														=   2

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

			# Initialize accelerator
			if args.with_tracking:
						_snake_case														=   Accelerator(
						    cpu=args.cpu				,						mixed_precision=args.mixed_precision				,						log_with="wandb"				,						project_dir=args.logging_dir				,						fsdp_plugin=lowercase_				,						)
			else:
						_snake_case														=   Accelerator(fsdp_plugin=lowercase_	)
			accelerator.print(accelerator.distributed_type	)

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

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

			_snake_case														=   AutoTokenizer.from_pretrained(args.model_name_or_path	)
			_snake_case														=   load_dataset("glue"				,						"mrpc"	)
			_snake_case														=   evaluate.load("glue"				,						"mrpc"	)

			def tokenize_function(lowerCamelCase_	):
						# max_length=None => use the model max length (it's actually the default)
						_snake_case														=   tokenizer(examples["sentence1"]				,						examples["sentence2"]				,						truncation=lowercase_				,						max_length=lowercase_	)
						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():
						_snake_case														=   datasets.map(
						    lowercase_				,						batched=lowercase_				,						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
			_snake_case														=   tokenized_datasets.rename_column("label"				,						"labels"	)

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

			def collate_fn(lowerCamelCase_	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						_snake_case														=   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":
									_snake_case														=   16
						elif accelerator.mixed_precision != "no":
									_snake_case														=   8
						else:
									_snake_case														=   None

						return tokenizer.pad(
						    lowercase_				,						padding="longest"				,						max_length=lowercase_				,						pad_to_multiple_of=lowercase_				,						return_tensors="pt"				,						)

			# Instantiate dataloaders.
			_snake_case														=   DataLoader(
			    tokenized_datasets["train"]				,						shuffle=lowercase_				,						collate_fn=lowercase_				,						batch_size=lowercase_	)
			_snake_case														=   DataLoader(
			    tokenized_datasets["validation"]				,						shuffle=lowercase_				,						collate_fn=lowercase_				,						batch_size=lowercase_	)

			set_seed(lowercase_	)

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

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

			# 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
			_snake_case														=   torch.optim.AdamW(params=model.parameters()				,						lr=lowercase_				,						weight_decay=2E-4	)

			# Instantiate scheduler
			_snake_case														=   get_linear_schedule_with_warmup(
			    optimizer=lowercase_				,						num_warmup_steps=10				,						num_training_steps=(len(lowercase_	) * 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.
			_snake_case							,			_snake_case							,			_snake_case							,			_snake_case														=   accelerator.prepare(
			    lowercase_				,						lowercase_				,						lowercase_				,						lowercase_	)

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

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

									 # Now we train the model
			for epoch in range(lowercase_	):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												_snake_case														=   0
									for step, batch in enumerate(lowercase_	):
												# 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	)
												_snake_case														=   model(**lowercase_	)
												_snake_case														=   outputs.loss
												_snake_case														=   loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(lowercase_	)
												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(lowercase_				,						lowercase_	):
															_snake_case														=   f'step_{overall_step}'
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					_snake_case														=   os.path.join(args.output_dir				,						lowercase_	)
																		accelerator.save_state(lowercase_	)
																		  # 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=lowercase_				,						)

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

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

									if checkpointing_steps == "epoch":
												_snake_case														=   f'epoch_{epoch}'
												if args.output_dir is not None:
															_snake_case														=   os.path.join(args.output_dir				,						lowercase_	)
												accelerator.save_state(lowercase_	)
												    # 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=lowercase_				,						)

			if args.with_tracking:
						accelerator.end_training()






def  UpperCAmelCase__(    ):



			"""simple docstring"""





			_snake_case														=   argparse.ArgumentParser(description="Simple example of training script."	)
			parser.add_argument(
			    "--mixed_precision"				,						type=lowercase_				,						default=lowercase_				,						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=lowercase_				,						default=lowercase_				,						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=lowercase_				,						default=lowercase_				,						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=lowercase_				,						default="."				,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,						)
			parser.add_argument(
			    "--logging_dir"				,						type=lowercase_				,						default="logs"				,						help="Location on where to store experiment tracking logs`"				,						)
			parser.add_argument(
			    "--model_name_or_path"				,						type=lowercase_				,						help="Path to pretrained model or model identifier from huggingface.co/models."				,						required=lowercase_				,						)
			_snake_case														=   parser.parse_args()
			_snake_case														=   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(lowercase_				,						lowercase_	)


if __name__ == "__main__":
			main()









import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device





def 						A_					(						lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		{}
 for name in unet.attn_processors.keys():
  SCREAMING_SNAKE_CASE				    =		None if name.endswith('attn1.processor'			) else unet.config.cross_attention_dim
  if name.startswith('mid_block'			):
   SCREAMING_SNAKE_CASE				    =		unet.config.block_out_channels[-1]
  elif name.startswith('up_blocks'			):
   SCREAMING_SNAKE_CASE				    =		int(name[len('up_blocks.'			)]			)
   SCREAMING_SNAKE_CASE				    =		list(reversed(unet.config.block_out_channels			)			)[block_id]
  elif name.startswith('down_blocks'			):
   SCREAMING_SNAKE_CASE				    =		int(name[len('down_blocks.'			)]			)
   SCREAMING_SNAKE_CASE				    =		unet.config.block_out_channels[block_id]
  SCREAMING_SNAKE_CASE				    =		(
      LoRAAttnProcessor2_0 if hasattr(lowercase_	,  'scaled_dot_product_attention'			) else LoRAAttnProcessor
  )
  SCREAMING_SNAKE_CASE				    =		lora_attn_processor_class(
      hidden_size=lowercase_	,  cross_attention_dim=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		AttnProcsLayers(lowercase_			)
 return lora_attn_procs, unet_lora_layers





def 						A_					(						lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		{}
 SCREAMING_SNAKE_CASE				    =		(
     LoRAAttnProcessor2_0 if hasattr(lowercase_	,  'scaled_dot_product_attention'			) else LoRAAttnProcessor
 )
 for name, module in text_encoder.named_modules():
  if name.endswith(lowercase_			):
   SCREAMING_SNAKE_CASE				    =		lora_attn_processor_class(
       hidden_size=module.out_proj.out_features	,  cross_attention_dim=lowercase_			)
 return text_lora_attn_procs





def 						A_					(						lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		AttnProcsLayers(lowercase_			)
 return text_encoder_lora_layers





def 						A_					(						lowercase_	,  lowercase_=False			)				->Optional[Any]:







 """simple docstring"""

 for _, attn_proc in text_lora_attn_procs.items():
  # set up.weights
  for layer_name, layer_module in attn_proc.named_modules():
   if layer_name.endswith('_lora'			):
    SCREAMING_SNAKE_CASE				    =		(
        torch.randn_like(layer_module.up.weight			)
        if randn_weight
        else torch.zeros_like(layer_module.up.weight			)
    )
    SCREAMING_SNAKE_CASE				    =		torch.nn.Parameter(lowercase_			)
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Dict)							->	str:

      """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				    =		DDIMScheduler(
          beta_start=0.0_00_85  ,  beta_end=0.0_12  ,  beta_schedule='scaled_linear'  ,  clip_sample=lowerCAmelCase__  ,  set_alpha_to_one=lowerCAmelCase__  ,  steps_offset=1  ,  )
      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  ,  )
      SCREAMING_SNAKE_CASE				    =		CLIPTextConfig(
          bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=3_2  ,  intermediate_size=3_7  ,  layer_norm_eps=1e-05  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_0_0_0  ,  )
      SCREAMING_SNAKE_CASE				    =		CLIPTextModel(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		create_unet_lora_layers(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_layers(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      SCREAMING_SNAKE_CASE				    =		{
          'unet_lora_layers': unet_lora_layers,
          'text_encoder_lora_layers': text_encoder_lora_layers,
          'unet_lora_attn_procs': unet_lora_attn_procs,
      }
      return pipeline_components, lora_components





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		1
      SCREAMING_SNAKE_CASE				    =		1_0
      SCREAMING_SNAKE_CASE				    =		4
      SCREAMING_SNAKE_CASE				    =		(3_2, 3_2)

      SCREAMING_SNAKE_CASE				    =		torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		floats_tensor((batch_size, num_channels) + sizes)
      SCREAMING_SNAKE_CASE				    =		torch.randint(1  ,  lowerCAmelCase__  ,  size=(batch_size, sequence_length)  ,  generator=lowerCAmelCase__)

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

      return noise, input_ids, pipeline_inputs

      # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		7_7

      SCREAMING_SNAKE_CASE				    =		torch.randint(2  ,  5_6  ,  size=(1, max_seq_length)  ,  generator=torch.manual_seed(0))

      SCREAMING_SNAKE_CASE				    =		{}
      SCREAMING_SNAKE_CASE				    =		inputs
      return prepared_inputs





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	List[Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      LoraLoaderMixin.save_lora_weights(
          save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  )
      self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))





     def     __UpperCamelCase						(		self					:	Any)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  )
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	Tuple)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  safe_serialization=lowerCAmelCase__  ,  )
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.safetensors')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		lora_components['unet_lora_attn_procs']
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		sd_pipe.unet
       unet.set_attn_processor(lowerCAmelCase__)
       unet.save_attn_procs(lowerCAmelCase__)
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		self.get_dummy_tokens()

      # inference without lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_without_lora.shape == (1, 7_7, 3_2)

      # create lora_attn_procs with zeroed out up.weights
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
      set_lora_up_weights(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

      # create lora_attn_procs with randn up.weights
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
      set_lora_up_weights(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert not torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		self.get_dummy_tokens()

      # inference without lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_without_lora.shape == (1, 7_7, 3_2)

      # create lora_attn_procs with randn up.weights
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
      set_lora_up_weights(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert not torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "lora outputs should be different to without lora outputs"

      # remove monkey patch
      pipe._remove_text_encoder_monkey_patch()

      # inference with removed lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

      assert torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "remove lora monkey patch should restore the original outputs"





     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  )
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__  ,  cross_attention_kwargs={'scale': 0.5}).images
      SCREAMING_SNAKE_CASE				    =		lora_images_with_scale[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(
          torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
       self.create_lora_weight_file(lowerCAmelCase__)

       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

       # check if vanilla attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  (AttnProcessor, AttnProcessor2_0))

            # load LoRA weight file
       sd_pipe.load_lora_weights(lowerCAmelCase__)

       # check if lora attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         SCREAMING_SNAKE_CASE				    =		(
             LoRAAttnProcessor2_0 if hasattr(lowerCAmelCase__  ,  'scaled_dot_product_attention') else LoRAAttnProcessor
         )
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)





     @unittest.skipIf(torch_device != 'cuda'  ,  'This test is supposed to run on GPU')
     def     __UpperCamelCase						(		self					:	str)							->	int:

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
       self.create_lora_weight_file(lowerCAmelCase__)

       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

       # enable XFormers
       sd_pipe.enable_xformers_memory_efficient_attention()

       # check if xFormers attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)

            # load LoRA weight file
       sd_pipe.load_lora_weights(lowerCAmelCase__)

       # check if lora attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)





     @unittest.skipIf(torch_device != 'cuda'  ,  'This test is supposed to run on GPU')
     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      # enable XFormers
      sd_pipe.enable_xformers_memory_efficient_attention()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  )
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))




