


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


A	:		Optional[Any]    =       1_6
A	:		int    =       3_2






def 			UpperCamelCase (     __magic_name__					:							Accelerator   ,   __magic_name__					:							int = 16   ,   __magic_name__					:							str = "bert-base-cased"					)       ->  List[str]:
		"""simple docstring"""




		lowercase__					      =		AutoTokenizer.from_pretrained(__magic_name__					)
		lowercase__					      =		load_dataset("""glue"""   ,   """mrpc"""					)

		def tokenize_function(__magic_name__					:							List[Any]					):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__					      =		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
		lowercase__					      =		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
		lowercase__					      =		tokenized_datasets.rename_column("""label"""   ,   """labels"""					)

		def collate_fn(__magic_name__					:							Dict					):
				# 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.
		lowercase__					      =		DataLoader(
		    tokenized_datasets["""train"""]   ,   shuffle=__magic_name__   ,   collate_fn=__magic_name__   ,   batch_size=__magic_name__					)
		lowercase__					      =		DataLoader(
		    tokenized_datasets["""validation"""]   ,   shuffle=__magic_name__   ,   collate_fn=__magic_name__   ,   batch_size=__magic_name__					)

		return train_dataloader, eval_dataloader






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							Tuple					)       ->  List[str]:
		"""simple docstring"""




		model.eval()
		lowercase__					      =		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():
						lowercase__					      =		model(**__magic_name__					)
				lowercase__					      =		outputs.logits.argmax(dim=-1					)
				# It is slightly faster to call this once, than multiple times
				lowercase__      ,     lowercase__					      =		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:
								lowercase__					      =		predictions[: len(eval_dataloader.dataset					) - samples_seen]
								lowercase__					      =		references[: len(eval_dataloader.dataset					) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=__magic_name__   ,   references=__magic_name__   ,   )

		lowercase__					      =		metric.compute()
		return eval_metric["accuracy"]






def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Union[str, Any]					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__					      =		config["""lr"""]
		lowercase__					      =		int(config["""num_epochs"""]					)
		lowercase__					      =		int(config["""seed"""]					)
		lowercase__					      =		int(config["""batch_size"""]					)
		lowercase__					      =		args.model_name_or_path

		set_seed(__magic_name__					)
		lowercase__      ,     lowercase__					      =		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)
		lowercase__					      =		AutoModelForSequenceClassification.from_pretrained(__magic_name__   ,   return_dict=__magic_name__					)

		# Instantiate optimizer
		lowercase__					      =		(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowercase__					      =		optimizer_cls(params=model.parameters()   ,   lr=__magic_name__					)

		if accelerator.state.deepspeed_plugin is not None:
				lowercase__					      =		accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				lowercase__					      =		1
		lowercase__					      =		(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
		):
				lowercase__					      =		get_linear_schedule_with_warmup(
				    optimizer=__magic_name__   ,   num_warmup_steps=0   ,   num_training_steps=__magic_name__   ,   )
		else:
				lowercase__					      =		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.
		lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__					      =		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
		lowercase__					      =		0
		# We also need to keep track of the stating epoch so files are named properly
		lowercase__					      =		0
		lowercase__					      =		evaluate.load("""glue"""   ,   """mrpc"""					)
		lowercase__					      =		num_epochs

		if args.partial_train_epoch is not None:
				lowercase__					      =		args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint					)
				lowercase__					      =		args.resume_from_checkpoint.split("""epoch_"""					)[1]
				lowercase__					      =		""""""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowercase__					      =		int(__magic_name__					) + 1
				lowercase__					      =		evaluation_loop(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
				accelerator.print("""resumed checkpoint performance:"""   ,   __magic_name__					)
				accelerator.print("""resumed checkpoint's scheduler's lr:"""   ,   lr_scheduler.get_lr()[0]					)
				accelerator.print("""resumed optimizers's lr:"""   ,   optimizer.param_groups[0]["""lr"""]					)
				with open(os.path.join(args.output_dir   ,   f'''state_{starting_epoch-1}.json'''					)   ,   """r"""					) as f:
						lowercase__					      =		json.load(__magic_name__					)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowercase__					      =		{}
		for epoch in range(__magic_name__   ,   __magic_name__					):
				model.train()
				for step, batch in enumerate(__magic_name__					):
						lowercase__					      =		model(**__magic_name__					)
						lowercase__					      =		outputs.loss
						lowercase__					      =		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
				lowercase__					      =		f'''epoch_{epoch}'''
				lowercase__					      =		os.path.join(args.output_dir   ,   __magic_name__					)
				accelerator.save_state(__magic_name__					)
				lowercase__					      =		evaluation_loop(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
				lowercase__					      =		accuracy
				lowercase__					      =		lr_scheduler.get_lr()[0]
				lowercase__					      =		optimizer.param_groups[0]["""lr"""]
				lowercase__					      =		epoch
				lowercase__					      =		overall_step
				accelerator.print(f'''epoch {epoch}:'''   ,   __magic_name__					)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir   ,   f'''state_{epoch}.json'''					)   ,   """w"""					) as f:
								json.dump(__magic_name__   ,   __magic_name__					)






def 			UpperCamelCase (     )       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		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(
		    """--resume_from_checkpoint"""   ,   type=__magic_name__   ,   default=__magic_name__   ,   help="""If the training should continue from a checkpoint folder."""   ,   )
		parser.add_argument(
		    """--partial_train_epoch"""   ,   type=__magic_name__   ,   default=__magic_name__   ,   help="""If passed, the training will stop after this number of epochs."""   ,   )
		parser.add_argument(
		    """--num_epochs"""   ,   type=__magic_name__   ,   default=2   ,   help="""Number of train epochs."""   ,   )
		lowercase__					      =		parser.parse_args()
		lowercase__					      =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

		training_function(__magic_name__   ,   __magic_name__					)


if __name__ == "__main__":
	main()





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

