



"""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


lowerCamelCase            =		16
lowerCamelCase            =		32
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = 16			,    lowerCAmelCase__ = "bert-base-cased"   ):
      UpperCAmelCase_    				=							AutoTokenizer.from_pretrained(lowerCAmelCase__   )
      UpperCAmelCase_    				=							load_dataset("glue"			,    "mrpc"   )

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

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

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

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

      # Instantiate dataloaders.
      UpperCAmelCase_    				=							DataLoader(
          tokenized_datasets["train"]			,    shuffle=lowerCAmelCase__			,    collate_fn=lowerCAmelCase__			,    batch_size=lowerCAmelCase__   )
      UpperCAmelCase_    				=							DataLoader(
          tokenized_datasets["validation"]			,    shuffle=lowerCAmelCase__			,    collate_fn=lowerCAmelCase__			,    batch_size=lowerCAmelCase__   )

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

      UpperCAmelCase_    				=							metric.compute()
      return eval_metric["accuracy"]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # Initialize accelerator
      UpperCAmelCase_    				=							Accelerator()

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

      set_seed(lowerCAmelCase__   )
      UpperCAmelCase_				,					UpperCAmelCase_    				=							get_dataloaders(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase_    				=							accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCAmelCase_    				=							1
      UpperCAmelCase_    				=							(len(lowerCAmelCase__   ) * 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_    				=							get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__			,    num_warmup_steps=0			,    num_training_steps=lowerCAmelCase__			,    )
      else:
            UpperCAmelCase_    				=							DummyScheduler(lowerCAmelCase__			,    total_num_steps=lowerCAmelCase__			,    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_    				=							accelerator.prepare(
          lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

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

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

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

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

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

      training_function(lowerCAmelCase__			,    lowerCAmelCase__   )


if __name__ == "__main__":
  main()









"""simple docstring"""





def      a__			(   lowerCAmelCase__   ):
      if not head:
            return True
      # split the list to two parts
      UpperCAmelCase_				,					UpperCAmelCase_    				=							head.next, head
      while fast and fast.next:
            UpperCAmelCase_    				=							fast.next.next
            UpperCAmelCase_    				=							slow.next
      UpperCAmelCase_    				=							slow.next
      UpperCAmelCase_    				=							None  # Don't forget here! But forget still works!
      # reverse the second part
      UpperCAmelCase_    				=							None
      while second:
            UpperCAmelCase_    				=							second.next
            UpperCAmelCase_    				=							node
            UpperCAmelCase_    				=							second
            UpperCAmelCase_    				=							nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            UpperCAmelCase_    				=							node.next
            UpperCAmelCase_    				=							head.next
      return True
def      a__			(   lowerCAmelCase__   ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      UpperCAmelCase_    				=							UpperCAmelCase_    				=							UpperCAmelCase_    				=							head
      while fast and fast.next:
            UpperCAmelCase_				,					UpperCAmelCase_    				=							fast.next.next, slow.next

      # 2. Push the second half into the stack
      UpperCAmelCase_    				=							[slow.val]
      while slow.next:
            UpperCAmelCase_    				=							slow.next
            stack.append(slow.val   )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            UpperCAmelCase_    				=							cur.next

      return True


def      a__			(   lowerCAmelCase__   ):
      if not head or not head.next:
            return True
      UpperCAmelCase_    				=							{}
      UpperCAmelCase_    				=							0
      while head:
            if head.val in d:
                  d[head.val].append(lowerCAmelCase__   )
            else:
                  UpperCAmelCase_    				=							[pos]
            UpperCAmelCase_    				=							head.next
            pos += 1
      UpperCAmelCase_    				=							pos - 1
      UpperCAmelCase_    				=							0
      for v in d.values():
            if len(lowerCAmelCase__   ) % 2 != 0:
                  middle += 1
            else:
                  UpperCAmelCase_    				=							0
                  for i in range(0			,    len(lowerCAmelCase__   )   ):
                        if v[i] + v[len(lowerCAmelCase__   ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True




