






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)  # pylint: disable=invalid-name

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					CLIPSegForImageSegmentation ,  __lowerCamelCase				:					CLIPSegProcessor ,  __lowerCamelCase				:					AutoencoderKL ,  __lowerCamelCase				:					CLIPTextModel ,  __lowerCamelCase				:					CLIPTokenizer ,  __lowerCamelCase				:					UNetaDConditionModel ,  __lowerCamelCase				:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,  __lowerCamelCase				:					StableDiffusionSafetyChecker ,  __lowerCamelCase				:					CLIPImageProcessor ,  ):
		super().__init__()

		if hasattr(scheduler.config ,  """steps_offset"""	) and scheduler.config.steps_offset != 1:
			UpperCamelCase     :str          =	(
			    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
			    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
			    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
			    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
			    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
			    """ file"""
			)
			deprecate("""steps_offset!=1""" ,  """1.0.0""" ,  __lowerCamelCase ,  standard_warn=__lowerCamelCase	)
			UpperCamelCase     :Optional[Any]          =	dict(scheduler.config	)
			UpperCamelCase     :Tuple          =	1
			UpperCamelCase     :Dict          =	FrozenDict(__lowerCamelCase	)

		if hasattr(scheduler.config ,  """skip_prk_steps"""	) and scheduler.config.skip_prk_steps is False:
			UpperCamelCase     :Any          =	(
			    F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
			    """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
			    """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
			    """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
			    """ Hub, it would be very nice if you could open a Pull request for the"""
			    """ `scheduler/scheduler_config.json` file"""
			)
			deprecate("""skip_prk_steps not set""" ,  """1.0.0""" ,  __lowerCamelCase ,  standard_warn=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	dict(scheduler.config	)
			UpperCamelCase     :Any          =	True
			UpperCamelCase     :List[str]          =	FrozenDict(__lowerCamelCase	)

		if safety_checker is None:
			logger.warning(
			    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
			    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
			    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
			    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
			    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
			    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""	)

		self.register_modules(
		    segmentation_model=__lowerCamelCase ,  segmentation_processor=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  unet=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  safety_checker=__lowerCamelCase ,  feature_extractor=__lowerCamelCase ,  )





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					Optional[Union[str, int]] = "auto"	):
		if slice_size == "auto":
			# half the attention head size is usually a good trade-off between
			# speed and memory
			UpperCamelCase     :Optional[Any]          =	self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		self.enable_attention_slicing(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError("""Please install accelerate via `pip install accelerate`"""	)

		UpperCamelCase     :Any          =	torch.device("""cuda"""	)

		for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
			if cpu_offloaded_model is not None:
				cpu_offload(__lowerCamelCase ,  __lowerCamelCase	)





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						_A (		self				:					Tuple	):
		if self.device != torch.device("""meta"""	) or not hasattr(self.unet ,  """_hf_hook"""	):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(__lowerCamelCase ,  """_hf_hook"""	)
			    and hasattr(module._hf_hook ,  """execution_device"""	)
			    and module._hf_hook.execution_device is not None
			):
				return torch.device(module._hf_hook.execution_device	)
		return self.device





	@torch.no_grad()
	def __call__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Union[str, List[str]] ,  __lowerCamelCase				:					Union[torch.FloatTensor, PIL.Image.Image] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 50 ,  __lowerCamelCase				:					float = 7.5 ,  __lowerCamelCase				:					Optional[Union[str, List[str]]] = None ,  __lowerCamelCase				:					Optional[int] = 1 ,  __lowerCamelCase				:					float = 0.0 ,  __lowerCamelCase				:					Optional[torch.Generator] = None ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[str] = "pil" ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,  __lowerCamelCase				:					int = 1 ,  **__lowerCamelCase				:					List[Any] ,  ):
		UpperCamelCase     :Any          =	self.segmentation_processor(
		    text=[text] ,  images=[image] ,  padding="""max_length""" ,  return_tensors="""pt"""	).to(self.device	)
		UpperCamelCase     :Any          =	self.segmentation_model(**__lowerCamelCase	)
		UpperCamelCase     :Dict          =	torch.sigmoid(outputs.logits	).cpu().detach().unsqueeze(-1	).numpy()
		UpperCamelCase     :Optional[int]          =	self.numpy_to_pil(__lowerCamelCase	)[0].resize(image.size	)

		# Run inpainting pipeline with the generated mask
		UpperCamelCase     :Tuple          =	StableDiffusionInpaintPipeline(
		    vae=self.vae ,  text_encoder=self.text_encoder ,  tokenizer=self.tokenizer ,  unet=self.unet ,  scheduler=self.scheduler ,  safety_checker=self.safety_checker ,  feature_extractor=self.feature_extractor ,  )
		return inpainting_pipeline(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  mask_image=__lowerCamelCase ,  height=__lowerCamelCase ,  width=__lowerCamelCase ,  num_inference_steps=__lowerCamelCase ,  guidance_scale=__lowerCamelCase ,  negative_prompt=__lowerCamelCase ,  num_images_per_prompt=__lowerCamelCase ,  eta=__lowerCamelCase ,  generator=__lowerCamelCase ,  latents=__lowerCamelCase ,  output_type=__lowerCamelCase ,  return_dict=__lowerCamelCase ,  callback=__lowerCamelCase ,  callback_steps=__lowerCamelCase ,  )








import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_ :						Union[str, Any]      =      16
UpperCAmelCase_ :						int      =      32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  int = 16	,			__magic_name__					:  str = "bert-base-cased"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	AutoTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	load_dataset("""glue"""	,			"""mrpc"""					)

	def tokenize_function(__magic_name__					:  Tuple					):
		# max_length=None => use the model max length (it's actually the default)
		UpperCamelCase     :List[Any]          =	tokenizer(examples["""sentence1"""]	,			examples["""sentence2"""]	,			truncation=__magic_name__	,			max_length=__magic_name__					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	UpperCamelCase     :List[Any]          =	datasets.map(
	    __magic_name__	,			batched=__magic_name__	,			remove_columns=["""idx""", """sentence1""", """sentence2"""]	,			load_from_cache_file=__magic_name__					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCamelCase     :Optional[Any]          =	tokenized_datasets.rename_column("""label"""	,			"""labels"""					)

	def collate_fn(__magic_name__					:  Any					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)

	# Instantiate dataloaders.
	UpperCamelCase     :List[str]          =	DataLoader(
	    tokenized_datasets["""train"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)
	UpperCamelCase     :List[Any]          =	DataLoader(
	    tokenized_datasets["""validation"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)

	return train_dataloader, eval_dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[Any]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCamelCase     :Union[str, Any]          =	config["""lr"""]
	UpperCamelCase     :List[str]          =	int(config["""num_epochs"""]					)
	UpperCamelCase     :str          =	int(config["""seed"""]					)
	UpperCamelCase     :Dict          =	int(config["""batch_size"""]					)
	UpperCamelCase     :Union[str, Any]          =	args.model_name_or_path

	set_seed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	get_dataloaders(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCamelCase     :List[str]          =	AutoModelForSequenceClassification.from_pretrained(__magic_name__	,			return_dict=__magic_name__					)

	# Instantiate optimizer
	UpperCamelCase     :Union[str, Any]          =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCamelCase     :Optional[Any]          =	optimizer_cls(params=model.parameters()	,			lr=__magic_name__					)

	if accelerator.state.deepspeed_plugin is not None:
		UpperCamelCase     :Any          =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		UpperCamelCase     :Any          =	1
	UpperCamelCase     :Dict          =	(len(__magic_name__					) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		UpperCamelCase     :List[Any]          =	get_linear_schedule_with_warmup(
		    optimizer=__magic_name__	,			num_warmup_steps=0	,			num_training_steps=__magic_name__	,			)
	else:
		UpperCamelCase     :Any          =	DummyScheduler(__magic_name__	,			total_num_steps=__magic_name__	,			warmup_num_steps=0					)

	# Prepare everything
	# 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.
	UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :str          =	accelerator.prepare(
	    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# We need to keep track of how many total steps we have iterated over
	UpperCamelCase     :int          =	0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCamelCase     :Tuple          =	0

	# Now we train the model
	UpperCamelCase     :Any          =	evaluate.load("""glue"""	,			"""mrpc"""					)
	UpperCamelCase     :Tuple          =	0
	UpperCamelCase     :List[Any]          =	{}
	for epoch in range(__magic_name__	,			__magic_name__					):
		model.train()
		for step, batch in enumerate(__magic_name__					):
			UpperCamelCase     :List[str]          =	model(**__magic_name__					)
			UpperCamelCase     :Dict          =	outputs.loss
			UpperCamelCase     :Optional[int]          =	loss / gradient_accumulation_steps
			accelerator.backward(__magic_name__					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		UpperCamelCase     :str          =	0
		for step, batch in enumerate(__magic_name__					):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device					)
			with torch.no_grad():
				UpperCamelCase     :Optional[int]          =	model(**__magic_name__					)
			UpperCamelCase     :List[Any]          =	outputs.logits.argmax(dim=-1					)
			# It is slightly faster to call this once, than multiple times
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	accelerator.gather(
			    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(__magic_name__					) - 1:
					UpperCamelCase     :Dict          =	predictions[: len(eval_dataloader.dataset					) - samples_seen]
					UpperCamelCase     :List[str]          =	references[: len(eval_dataloader.dataset					) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=__magic_name__	,			references=__magic_name__	,			)

		UpperCamelCase     :List[str]          =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,			__magic_name__					)
		UpperCamelCase     :Dict          =	eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			UpperCamelCase     :str          =	eval_metric["""accuracy"""]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,			"""all_results.json"""					)	,			"""w"""					) as f:
			json.dump(__magic_name__	,			__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""	,			type=__magic_name__	,			default="""bert-base-cased"""	,			help="""Path to pretrained model or model identifier from huggingface.co/models."""	,			required=__magic_name__	,			)
	parser.add_argument(
	    """--output_dir"""	,			type=__magic_name__	,			default="""."""	,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,			)
	parser.add_argument(
	    """--performance_lower_bound"""	,			type=__magic_name__	,			default=__magic_name__	,			help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""	,			)
	parser.add_argument(
	    """--num_epochs"""	,			type=__magic_name__	,			default=3	,			help="""Number of train epochs."""	,			)
	UpperCamelCase     :str          =	parser.parse_args()
	UpperCamelCase     :Any          =	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(__magic_name__	,			__magic_name__					)


if __name__ == "__main__":
	main()
