
"""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


SCREAMING_SNAKE_CASE_												= 16
SCREAMING_SNAKE_CASE_												= 32







def   A__ ( A__      ,      A__ = 16      ,      A__ = "bert-base-cased"		)		->			List[str]:





   '''simple docstring'''




   _UpperCAmelCase         =      AutoTokenizer.from_pretrained(_lowercase		)
   _UpperCAmelCase         =      load_dataset("glue"      ,      "mrpc"		)

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

   def collate_fn(A__		):
      # 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.
   _UpperCAmelCase         =      DataLoader(
       tokenized_datasets["train"]      ,      shuffle=_lowercase      ,      collate_fn=_lowercase      ,      batch_size=_lowercase		)
   _UpperCAmelCase         =      DataLoader(
       tokenized_datasets["validation"]      ,      shuffle=_lowercase      ,      collate_fn=_lowercase      ,      batch_size=_lowercase		)

   return train_dataloader, eval_dataloader







def   A__ ( A__      ,      A__      ,      A__      ,      A__		)		->			Optional[int]:





   '''simple docstring'''




   model.eval()
   _UpperCAmelCase         =      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():
         _UpperCAmelCase         =      model(**_lowercase		)
      _UpperCAmelCase         =      outputs.logits.argmax(dim=-1		)
      # It is slightly faster to call this once, than multiple times
      _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(_lowercase		) - 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=_lowercase      ,      references=_lowercase      ,      )

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







def   A__ ( A__      ,      A__		)		->			List[Any]:





   '''simple docstring'''




   _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(_lowercase		)
   _UpperCAmelCase         =      get_dataloaders(_lowercase      ,      _lowercase      ,      _lowercase		)

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

   # 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=_lowercase		)

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

   # 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(_lowercase		) + 1
      _UpperCAmelCase         =      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:
         _UpperCAmelCase         =      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
   _UpperCAmelCase         =      {}
   for epoch in range(_lowercase      ,      _lowercase		):
      model.train()
      for step, batch in enumerate(_lowercase		):
         _UpperCAmelCase         =      model(**_lowercase		)
         _UpperCAmelCase         =      outputs.loss
         _UpperCAmelCase         =      loss / gradient_accumulation_steps
         accelerator.backward(_lowercase		)
         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      ,      _lowercase		)
      accelerator.save_state(_lowercase		)
      _UpperCAmelCase         =      evaluation_loop(_lowercase      ,      _lowercase      ,      _lowercase      ,      _lowercase		)
      _UpperCAmelCase         =      accuracy
      _UpperCAmelCase         =      lr_scheduler.get_lr()[0]
      _UpperCAmelCase         =      optimizer.param_groups[0]['''lr''']
      _UpperCAmelCase         =      epoch
      _UpperCAmelCase         =      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   A__ ( )		->			List[Any]:





   '''simple docstring'''




   _UpperCAmelCase         =      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."      ,      )
   _UpperCAmelCase         =      parser.parse_args()
   _UpperCAmelCase         =      {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

   training_function(_lowercase      ,      _lowercase		)


if __name__ == "__main__":
 main()







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     while a != 0:
          UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     b % a, a
     return b







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     if gcd(_lowercase					,		_lowercase ) != 1:
          UpperCAmelCase_     :				int  =     f'''mod inverse of {a!r} and {m!r} does not exist'''
          raise ValueError(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     1, 0, a
     UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     0, 1, m
     while va != 0:
          UpperCAmelCase_     :				List[Any]  =     ua // va
          UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m