






'''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  UpperCAmelCase_       (		lowercase__       ,	lowercase__ = 1_6       ,	lowercase__ = "bert-base-cased"       ):


     '''simple docstring'''







     a_       		=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
     a_       		=load_dataset("glue"       ,	"mrpc"       )

     def tokenize_function(lowercase__       ):
          # 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
     a_       		=datasets.map(
         SCREAMING_SNAKE_CASE_       ,	batched=SCREAMING_SNAKE_CASE_       ,	remove_columns=["idx", "sentence1", "sentence2"]       ,	load_from_cache_file=SCREAMING_SNAKE_CASE_       )

     # 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"       )

     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(SCREAMING_SNAKE_CASE_       ,	padding="max_length"       ,	max_length=1_2_8       ,	return_tensors="pt"       )
          return tokenizer.pad(SCREAMING_SNAKE_CASE_       ,	padding="longest"       ,	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_       )

     return train_dataloader, eval_dataloader






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     model.eval()
     a_       		=0
     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       )
          # It is slightly faster to call this once, than multiple times
          a_       		=accelerator.gather(
              (predictions, batch["labels"])       )  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(SCREAMING_SNAKE_CASE_       ) - 1:
                    a_       		=predictions[: len(eval_dataloader.dataset       ) - samples_seen]
                    a_       		=references[: len(eval_dataloader.dataset       ) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=SCREAMING_SNAKE_CASE_       ,	references=SCREAMING_SNAKE_CASE_       ,	)

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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=Accelerator()

     # 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"]       )
     a_       		=args.model_name_or_path

     set_seed(SCREAMING_SNAKE_CASE_       )
     a_       		=get_dataloaders(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     a_       		=AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_       ,	return_dict=SCREAMING_SNAKE_CASE_       )

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

     if accelerator.state.deepspeed_plugin is not None:
          a_       		=accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          a_       		=1
     a_       		=(len(SCREAMING_SNAKE_CASE_       ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          a_       		=get_linear_schedule_with_warmup(
              optimizer=SCREAMING_SNAKE_CASE_       ,	num_warmup_steps=0       ,	num_training_steps=SCREAMING_SNAKE_CASE_       ,	)
     else:
          a_       		=DummyScheduler(SCREAMING_SNAKE_CASE_       ,	total_num_steps=SCREAMING_SNAKE_CASE_       ,	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.
     a_       		=accelerator.prepare(
         SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       )

     # We need to keep track of how many total steps we have iterated over
     a_       		=0
     # We also need to keep track of the stating epoch so files are named properly
     a_       		=0
     a_       		=evaluate.load("glue"       ,	"mrpc"       )
     a_       		=num_epochs

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

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

               overall_step += 1
          a_       		=F"""epoch_{epoch}"""
          a_       		=os.path.join(args.output_dir       ,	SCREAMING_SNAKE_CASE_       )
          accelerator.save_state(SCREAMING_SNAKE_CASE_       )
          a_       		=evaluation_loop(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       )
          a_       		=accuracy
          a_       		=lr_scheduler.get_lr()[0]
          a_       		=optimizer.param_groups[0]['lr']
          a_       		=epoch
          a_       		=overall_step
          accelerator.print(F"""epoch {epoch}:"""       ,	SCREAMING_SNAKE_CASE_       )

          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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       )






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."       )
     parser.add_argument(
         "--model_name_or_path"       ,	type=SCREAMING_SNAKE_CASE_       ,	default="bert-base-cased"       ,	help="Path to pretrained model or model identifier from huggingface.co/models."       ,	required=SCREAMING_SNAKE_CASE_       ,	)
     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(
         "--resume_from_checkpoint"       ,	type=SCREAMING_SNAKE_CASE_       ,	default=SCREAMING_SNAKE_CASE_       ,	help="If the training should continue from a checkpoint folder."       ,	)
     parser.add_argument(
         "--partial_train_epoch"       ,	type=SCREAMING_SNAKE_CASE_       ,	default=SCREAMING_SNAKE_CASE_       ,	help="If passed, the training will stop after this number of epochs."       ,	)
     parser.add_argument(
         "--num_epochs"       ,	type=SCREAMING_SNAKE_CASE_       ,	default=2       ,	help="Number of train epochs."       ,	)
     a_       		=parser.parse_args()
     a_       		={'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

     training_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  main()







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


