




"""simple docstring"""






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


lowercase__			=		16
lowercase__			=		32







def 	_snake_case     (							lowercase__				,       lowercase__ = 16				,       lowercase__ = "bert-base-cased"						):
				_lowerCamelCase	:     Optional[int]       =       AutoTokenizer.from_pretrained(lowercase__						)
				_lowerCamelCase	:     str       =       load_dataset('glue'				,       'mrpc'						)

				def tokenize_function(lowercase__						):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase	:     Dict       =       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
				_lowerCamelCase	:     List[Any]       =       datasets.map(
				    lowercase__				,       batched=lowercase__				,       remove_columns=['idx', 'sentence1', 'sentence2']				,       load_from_cache_file=lowercase__						)

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

				def collate_fn(lowercase__						):
								# 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(lowercase__				,       padding='max_length'				,       max_length=128				,       return_tensors='pt'						)
								return tokenizer.pad(lowercase__				,       padding='longest'				,       return_tensors='pt'						)

				# Instantiate dataloaders.
				_lowerCamelCase	:     Union[str, Any]       =       DataLoader(
				    tokenized_datasets['train']				,       shuffle=lowercase__				,       collate_fn=lowercase__				,       batch_size=lowercase__						)
				_lowerCamelCase	:     Union[str, Any]       =       DataLoader(
				    tokenized_datasets['validation']				,       shuffle=lowercase__				,       collate_fn=lowercase__				,       batch_size=lowercase__						)

				return train_dataloader, eval_dataloader







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

				_lowerCamelCase	:     Optional[int]       =       metric.compute()
				return eval_metric["accuracy"]







def 	_snake_case     (							lowercase__				,       lowercase__						):
				# Initialize accelerator
				_lowerCamelCase	:     Any       =       Accelerator()

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

				set_seed(lowercase__						)
				_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       get_dataloaders(lowercase__				,       lowercase__				,       lowercase__						)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_lowerCamelCase	:     Union[str, Any]       =       AutoModelForSequenceClassification.from_pretrained(lowercase__				,       return_dict=lowercase__						)

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

				if accelerator.state.deepspeed_plugin is not None:
								_lowerCamelCase	:     Any       =       accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								_lowerCamelCase	:     Any       =       1
				_lowerCamelCase	:     str       =       (len(lowercase__						) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_lowerCamelCase	:     Any       =       get_linear_schedule_with_warmup(
								    optimizer=lowercase__				,       num_warmup_steps=0				,       num_training_steps=lowercase__				,       )
				else:
								_lowerCamelCase	:     Union[str, Any]       =       DummyScheduler(lowercase__				,       total_num_steps=lowercase__				,       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.
				_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     int       =       accelerator.prepare(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)

				# We need to keep track of how many total steps we have iterated over
				_lowerCamelCase	:     List[str]       =       0
				# We also need to keep track of the stating epoch so files are named properly
				_lowerCamelCase	:     Optional[Any]       =       0
				_lowerCamelCase	:     Union[str, Any]       =       evaluate.load('glue'				,       'mrpc'						)
				_lowerCamelCase	:     int       =       num_epochs

				if args.partial_train_epoch is not None:
								_lowerCamelCase	:     Optional[Any]       =       args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint						)
								_lowerCamelCase	:     Optional[int]       =       args.resume_from_checkpoint.split('epoch_'						)[1]
								_lowerCamelCase	:     Union[str, Any]       =       ''
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_lowerCamelCase	:     Optional[int]       =       int(lowercase__						) + 1
								_lowerCamelCase	:     int       =       evaluation_loop(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
								accelerator.print('resumed checkpoint performance:'				,       lowercase__						)
								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:
												_lowerCamelCase	:     List[str]       =       json.load(lowercase__						)
												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
				_lowerCamelCase	:     Any       =       {}
				for epoch in range(lowercase__				,       lowercase__						):
								model.train()
								for step, batch in enumerate(lowercase__						):
												_lowerCamelCase	:     int       =       model(**lowercase__						)
												_lowerCamelCase	:     Dict       =       outputs.loss
												_lowerCamelCase	:     List[str]       =       loss / gradient_accumulation_steps
												accelerator.backward(lowercase__						)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_lowerCamelCase	:     Tuple       =       f'''epoch_{epoch}'''
								_lowerCamelCase	:     int       =       os.path.join(args.output_dir				,       lowercase__						)
								accelerator.save_state(lowercase__						)
								_lowerCamelCase	:     List[str]       =       evaluation_loop(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
								_lowerCamelCase	:     Tuple       =       accuracy
								_lowerCamelCase	:     str       =       lr_scheduler.get_lr()[0]
								_lowerCamelCase	:     List[Any]       =       optimizer.param_groups[0]['lr']
								_lowerCamelCase	:     List[str]       =       epoch
								_lowerCamelCase	:     str       =       overall_step
								accelerator.print(f'''epoch {epoch}:'''				,       lowercase__						)

								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(lowercase__				,       lowercase__						)







def 	_snake_case     (							):
				_lowerCamelCase	:     Optional[int]       =       argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
				parser.add_argument(
				    '--model_name_or_path'				,       type=lowercase__				,       default='bert-base-cased'				,       help='Path to pretrained model or model identifier from huggingface.co/models.'				,       required=lowercase__				,       )
				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(
				    '--resume_from_checkpoint'				,       type=lowercase__				,       default=lowercase__				,       help='If the training should continue from a checkpoint folder.'				,       )
				parser.add_argument(
				    '--partial_train_epoch'				,       type=lowercase__				,       default=lowercase__				,       help='If passed, the training will stop after this number of epochs.'				,       )
				parser.add_argument(
				    '--num_epochs'				,       type=lowercase__				,       default=2				,       help='Number of train epochs.'				,       )
				_lowerCamelCase	:     int       =       parser.parse_args()
				_lowerCamelCase	:     str       =       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

				training_function(lowercase__				,       lowercase__						)


if __name__ == "__main__":
			main()





"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
lowercase__			=		parser.parse_args()


lowercase__			=		"""cpu"""
lowercase__			=		"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

lowercase__			=		"""path-to-your-trained-model"""
lowercase__			=		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
			lowercase__			=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__			=		pipe.to(device)

# to channels last
lowercase__			=		pipe.unet.to(memory_format=torch.channels_last)
lowercase__			=		pipe.vae.to(memory_format=torch.channels_last)
lowercase__			=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
			lowercase__			=		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__			=		torch.randn(2, 4, 64, 64)
lowercase__			=		torch.rand(1) * 999
lowercase__			=		torch.randn(2, 77, 768)
lowercase__			=		(sample, timestep, encoder_hidden_status)
try:
			lowercase__			=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
			lowercase__			=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__			=		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__			=		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
			lowercase__			=		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__			=		666
lowercase__			=		torch.Generator(device).manual_seed(seed)
lowercase__			=		{"""generator""": generator}
if args.steps is not None:
			lowercase__			=		args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
			lowercase__			=		pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")