
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					(    lowerCAmelCase__							:    Accelerator  ,       lowerCAmelCase__							:    int = 1_6  ,       lowerCAmelCase__							:    str = "bert-base-cased"  ):
 __a			:  Optional[int]		    =  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 __a			:  List[Any]		    =  load_dataset('''glue'''  ,       '''mrpc'''  )

 def tokenize_function(lowerCAmelCase__							:    str  ):
  # max_length=None => use the model max length (it's actually the default)
  __a			:  List[str]		    =  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
 __a			:  Optional[int]		    =  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
 __a			:  List[str]		    =  tokenized_datasets.rename_column('''label'''  ,       '''labels'''  )

 def collate_fn(lowerCAmelCase__							:    Tuple  ):
  # 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=1_2_8  ,       return_tensors='''pt'''  )
  return tokenizer.pad(lowerCAmelCase__  ,       padding='''longest'''  ,       return_tensors='''pt'''  )

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

 return train_dataloader, eval_dataloader




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

 __a			:  Union[str, Any]		    =  metric.compute()
 return eval_metric["accuracy"]




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  List[str]		    =  Accelerator()

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

 set_seed(lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  get_dataloaders(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  __a			:  Tuple		    =  accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  __a			:  Optional[int]		    =  1
 __a			:  Tuple		    =  (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
 ):
  __a			:  List[str]		    =  get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__  ,       num_warmup_steps=0  ,       num_training_steps=lowerCAmelCase__  ,       )
 else:
  __a			:  int		    =  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.
 __a			:  int		    =  accelerator.prepare(
     lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

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

 if args.partial_train_epoch is not None:
  __a			:  Optional[int]		    =  args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint  )
  __a			:  Tuple		    =  args.resume_from_checkpoint.split('''epoch_'''  )[1]
  __a			:  str		    =  """"""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  __a			:  List[Any]		    =  int(lowerCAmelCase__  ) + 1
  __a			:  Any		    =  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:
   __a			:  Any		    =  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
 __a			:  List[str]		    =  {}
 for epoch in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  model.train()
  for step, batch in enumerate(lowerCAmelCase__  ):
   __a			:  List[Any]		    =  model(**lowerCAmelCase__  )
   __a			:  int		    =  outputs.loss
   __a			:  Optional[Any]		    =  loss / gradient_accumulation_steps
   accelerator.backward(lowerCAmelCase__  )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  __a			:  Tuple		    =  f"epoch_{epoch}"
  __a			:  int		    =  os.path.join(args.output_dir  ,       lowerCAmelCase__  )
  accelerator.save_state(lowerCAmelCase__  )
  __a			:  str		    =  evaluation_loop(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Any		    =  accuracy
  __a			:  Dict		    =  lr_scheduler.get_lr()[0]
  __a			:  Dict		    =  optimizer.param_groups[0]["""lr"""]
  __a			:  Tuple		    =  epoch
  __a			:  Optional[Any]		    =  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        __UpperCamelCase					(    ):
 __a			:  Any		    =  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.'''  ,       )
 __a			:  Tuple		    =  parser.parse_args()
 __a			:  List[str]		    =  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

 training_function(lowerCAmelCase__  ,       lowerCAmelCase__  )


if __name__ == "__main__":
      main()


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


if __name__ == "__main__":
      print(F"""{solution() = }""")
