






'''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


__A							:							Union[str, Any]  							=		16
__A							:							Tuple  							=		32






def    lowerCamelCase_     (    lowercase__				,     lowercase__ = 16				,     lowercase__ = "bert-base-cased"):
 lowerCamelCase__  =     AutoTokenizer.from_pretrained(lowercase__)
 lowerCamelCase__  =     load_dataset("glue"				,     "mrpc")

 def tokenize_function(lowercase__):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase__  =     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__  =     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__  =     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__  =     DataLoader(
     tokenized_datasets["train"]				,     shuffle=lowercase__				,     collate_fn=lowercase__				,     batch_size=lowercase__)
 lowerCamelCase__  =     DataLoader(
     tokenized_datasets["validation"]				,     shuffle=lowercase__				,     collate_fn=lowercase__				,     batch_size=lowercase__)

 return train_dataloader, eval_dataloader






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

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 # Initialize accelerator
 lowerCamelCase__  =     Accelerator()

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

 set_seed(lowercase__)
 lowerCamelCase__  ,	lowerCamelCase__  =     get_dataloaders(lowercase__				,     lowercase__				,     lowercase__)

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

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

 if accelerator.state.deepspeed_plugin is not None:
  lowerCamelCase__  =     accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCamelCase__  =     1
 lowerCamelCase__  =     (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__  =     get_linear_schedule_with_warmup(
      optimizer=lowercase__				,     num_warmup_steps=0				,     num_training_steps=lowercase__				,     )
 else:
  lowerCamelCase__  =     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__  =     accelerator.prepare(
     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__)

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

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

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint)
  lowerCamelCase__  =     args.resume_from_checkpoint.split("epoch_")[1]
  lowerCamelCase__  =     ""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  lowerCamelCase__  =     int(lowercase__) + 1
  lowerCamelCase__  =     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__  =     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__  =     {}
 for epoch in range(lowercase__				,     lowercase__):
  model.train()
  for step, batch in enumerate(lowercase__):
   lowerCamelCase__  =     model(**lowercase__)
   lowerCamelCase__  =     outputs.loss
   lowerCamelCase__  =     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__  =     F'''epoch_{epoch}'''
  lowerCamelCase__  =     os.path.join(args.output_dir				,     lowercase__)
  accelerator.save_state(lowercase__)
  lowerCamelCase__  =     evaluation_loop(lowercase__				,     lowercase__				,     lowercase__				,     lowercase__)
  lowerCamelCase__  =     accuracy
  lowerCamelCase__  =     lr_scheduler.get_lr()[0]
  lowerCamelCase__  =     optimizer.param_groups[0]["lr"]
  lowerCamelCase__  =     epoch
  lowerCamelCase__  =     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    lowerCamelCase_     (    ):
 lowerCamelCase__  =     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__  =     parser.parse_args()
 lowerCamelCase__  =     {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

 training_function(lowercase__				,     lowercase__)


if __name__ == "__main__":
 main()









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float(moles / volume) * nfactor)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (volume)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (pressure)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((pressure * volume) / (0.0_821 * moles)))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

