





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


_snake_case           =			16
_snake_case           =			32


def _A       (   __magic_name__  , __magic_name__ = 16  , __magic_name__ = "bert-base-cased"	):
   lowercase__			       =  AutoTokenizer.from_pretrained(lowerCAmelCase__	)
   lowercase__			       =  load_dataset("glue"  , "mrpc"	)

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

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

   return train_dataloader, eval_dataloader


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   model.eval()
   lowercase__			       =  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():
         lowercase__			       =  model(**lowerCAmelCase__	)
      lowercase__			       =  outputs.logits.argmax(dim=-1	)
      # It is slightly faster to call this once, than multiple times
      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(lowerCAmelCase__	) - 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=lowerCAmelCase__  , references=lowerCAmelCase__  , )

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


def _A       (   __magic_name__  , __magic_name__	):
   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(lowerCAmelCase__	)
   lowercase__			       =  get_dataloaders(lowerCAmelCase__  , lowerCAmelCase__  , lowerCAmelCase__	)

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

   # 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=lowerCAmelCase__	)

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

   # 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(lowerCAmelCase__	) + 1
      lowercase__			       =  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:
         lowercase__			       =  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
   lowercase__			       =  {}
   for epoch in range(lowerCAmelCase__  , lowerCAmelCase__	):
      model.train()
      for step, batch in enumerate(lowerCAmelCase__	):
         lowercase__			       =  model(**lowerCAmelCase__	)
         lowercase__			       =  outputs.loss
         lowercase__			       =  loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase__	)
         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  , lowerCAmelCase__	)
      accelerator.save_state(lowerCAmelCase__	)
      lowercase__			       =  evaluation_loop(lowerCAmelCase__  , lowerCAmelCase__  , lowerCAmelCase__  , lowerCAmelCase__	)
      lowercase__			       =  accuracy
      lowercase__			       =  lr_scheduler.get_lr()[0]
      lowercase__			       =  optimizer.param_groups[0]["""lr"""]
      lowercase__			       =  epoch
      lowercase__			       =  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       (   ):
   lowercase__			       =  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."  , )
   lowercase__			       =  parser.parse_args()
   lowercase__			       =  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

   training_function(lowerCAmelCase__  , lowerCAmelCase__	)


if __name__ == "__main__":
       main()













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case           =			{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  _snake_case           =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






