



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_								=   16
a_								=   32





def 						a__     (						_UpperCamelCase     :	Any	,_UpperCamelCase     :	List[str]							):
   # Initialize accelerator
   if args.with_tracking:
      __lowerCamelCase  							=      Accelerator(
          cpu=args.cpu	,mixed_precision=args.mixed_precision	,log_with='''all'''	,project_dir=args.project_dir							)
   else:
      __lowerCamelCase  							=      Accelerator(cpu=args.cpu	,mixed_precision=args.mixed_precision							)

   if hasattr(args.checkpointing_steps	,'''isdigit'''							):
      if args.checkpointing_steps == "epoch":
         __lowerCamelCase  							=      args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         __lowerCamelCase  							=      int(args.checkpointing_steps							)
      else:
         raise ValueError(
             F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
   else:
      __lowerCamelCase  							=      None
   # 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''']							)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      __lowerCamelCase  							=      os.path.split(_UpperCamelCase							)[-1].split('''.'''							)[0]
      accelerator.init_trackers(_UpperCamelCase	,_UpperCamelCase							)

   __lowerCamelCase  							=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
   __lowerCamelCase  							=      load_dataset('''glue'''	,'''mrpc'''							)
   __lowerCamelCase  							=      evaluate.load('''glue'''	,'''mrpc'''							)

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      __lowerCamelCase  							=      datasets.map(
          _UpperCamelCase	,batched=_UpperCamelCase	,remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,)

   # 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'''							)

   # If the batch size is too big we use gradient accumulation
   __lowerCamelCase  							=      1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      __lowerCamelCase  							=      batch_size // MAX_GPU_BATCH_SIZE
      __lowerCamelCase  							=      MAX_GPU_BATCH_SIZE

   def collate_fn(_UpperCamelCase     :	Dict							):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __lowerCamelCase  							=      1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         __lowerCamelCase  							=      16
      elif accelerator.mixed_precision != "no":
         __lowerCamelCase  							=      8
      else:
         __lowerCamelCase  							=      None

      return tokenizer.pad(
          _UpperCamelCase	,padding='''longest'''	,max_length=_UpperCamelCase	,pad_to_multiple_of=_UpperCamelCase	,return_tensors='''pt'''	,)

   # Instantiate dataloaders.
   __lowerCamelCase  							=      DataLoader(
       tokenized_datasets['''train''']	,shuffle=_UpperCamelCase	,collate_fn=_UpperCamelCase	,batch_size=_UpperCamelCase							)
   __lowerCamelCase  							=      DataLoader(
       tokenized_datasets['''validation''']	,shuffle=_UpperCamelCase	,collate_fn=_UpperCamelCase	,batch_size=_UpperCamelCase							)

   set_seed(_UpperCamelCase							)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCamelCase  							=      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,return_dict=_UpperCamelCase							)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   __lowerCamelCase  							=      model.to(accelerator.device							)

   # Instantiate optimizer
   __lowerCamelCase  							=      AdamW(params=model.parameters()	,lr=_UpperCamelCase							)

   # Instantiate scheduler
   __lowerCamelCase  							=      get_linear_schedule_with_warmup(
       optimizer=_UpperCamelCase	,num_warmup_steps=1_00	,num_training_steps=(len(_UpperCamelCase							) * num_epochs) // gradient_accumulation_steps	,)

   # 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(
       _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   # 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

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
         accelerator.load_state(args.resume_from_checkpoint							)
         __lowerCamelCase  							=      os.path.basename(args.resume_from_checkpoint							)
      else:
         # Get the most recent checkpoint
         __lowerCamelCase  							=      [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
         dirs.sort(key=os.path.getctime							)
         __lowerCamelCase  							=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      __lowerCamelCase  							=      os.path.splitext(_UpperCamelCase							)[0]

      if "epoch" in training_difference:
         __lowerCamelCase  							=      int(training_difference.replace('''epoch_'''	,''''''							)							) + 1
         __lowerCamelCase  							=      None
      else:
         __lowerCamelCase  							=      int(training_difference.replace('''step_'''	,''''''							)							)
         __lowerCamelCase  							=      resume_step // len(_UpperCamelCase							)
         resume_step -= starting_epoch * len(_UpperCamelCase							)

    # Now we train the model
   for epoch in range(_UpperCamelCase	,_UpperCamelCase							):
      model.train()
      if args.with_tracking:
         __lowerCamelCase  							=      0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         __lowerCamelCase  							=      accelerator.skip_first_batches(_UpperCamelCase	,_UpperCamelCase							)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         __lowerCamelCase  							=      train_dataloader
      for step, batch in enumerate(_UpperCamelCase							):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device							)
         __lowerCamelCase  							=      model(**_UpperCamelCase							)
         __lowerCamelCase  							=      outputs.loss
         __lowerCamelCase  							=      loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(_UpperCamelCase							)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(_UpperCamelCase	,_UpperCamelCase							):
            __lowerCamelCase  							=      F"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  __lowerCamelCase  							=      os.path.join(args.output_dir	,_UpperCamelCase							)
               accelerator.save_state(_UpperCamelCase							)

      model.eval()
      for step, batch in enumerate(_UpperCamelCase							):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device							)
         with torch.no_grad():
            __lowerCamelCase  							=      model(**_UpperCamelCase							)
         __lowerCamelCase  							=      outputs.logits.argmax(dim=-1							)
         __lowerCamelCase							,__lowerCamelCase  							=      accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
         metric.add_batch(
             predictions=_UpperCamelCase	,references=_UpperCamelCase	,)

      __lowerCamelCase  							=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,_UpperCamelCase							)
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(_UpperCamelCase							),
                 '''epoch''': epoch,
             }	,step=_UpperCamelCase	,)

      if checkpointing_steps == "epoch":
         __lowerCamelCase  							=      F"""epoch_{epoch}"""
         if args.output_dir is not None:
            __lowerCamelCase  							=      os.path.join(args.output_dir	,_UpperCamelCase							)
         accelerator.save_state(_UpperCamelCase							)

   if args.with_tracking:
      accelerator.end_training()





def 						a__     (						):
   __lowerCamelCase  							=      argparse.ArgumentParser(description='''Simple example of training script.'''							)
   parser.add_argument(
       '''--mixed_precision'''	,type=_UpperCamelCase	,default=_UpperCamelCase	,choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''	,)
   parser.add_argument('''--cpu'''	,action='''store_true'''	,help='''If passed, will train on the CPU.'''							)
   parser.add_argument(
       '''--checkpointing_steps'''	,type=_UpperCamelCase	,default=_UpperCamelCase	,help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,)
   parser.add_argument(
       '''--resume_from_checkpoint'''	,type=_UpperCamelCase	,default=_UpperCamelCase	,help='''If the training should continue from a checkpoint folder.'''	,)
   parser.add_argument(
       '''--with_tracking'''	,action='''store_true'''	,help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,)
   parser.add_argument(
       '''--output_dir'''	,type=_UpperCamelCase	,default='''.'''	,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,)
   parser.add_argument(
       '''--project_dir'''	,type=_UpperCamelCase	,default='''logs'''	,help='''Location on where to store experiment tracking logs` and relevent project information'''	,)
   __lowerCamelCase  							=      parser.parse_args()
   __lowerCamelCase  							=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_UpperCamelCase	,_UpperCamelCase							)


if __name__ == "__main__":
  main()











import argparse
import json
import re
import string
import sys
from collections import Counter





def 						a__     (						_UpperCamelCase     :	Tuple							):

   def remove_articles(_UpperCamelCase     :	int							):
      return re.sub(R'''\b(a|an|the)\b'''	,''' '''	,_UpperCamelCase							)

   def white_space_fix(_UpperCamelCase     :	int							):
      return " ".join(text.split()							)

   def remove_punc(_UpperCamelCase     :	int							):
      __lowerCamelCase  							=      set(string.punctuation							)
      return "".join(ch for ch in text if ch not in exclude							)

   def lower(_UpperCamelCase     :	str							):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase							)							)							)							)





def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	List[str]							):
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      Counter(_UpperCamelCase							) & Counter(_UpperCamelCase							)
   __lowerCamelCase  							=      sum(common.values()							)
   if num_same == 0:
      return 0
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      (2 * precision * recall) / (precision + recall)
   return fa





def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	str							):
   return normalize_answer(_UpperCamelCase							) == normalize_answer(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Dict							):
   __lowerCamelCase  							=      []
   for ground_truth in ground_truths:
      __lowerCamelCase  							=      metric_fn(_UpperCamelCase	,_UpperCamelCase							)
      scores_for_ground_truths.append(_UpperCamelCase							)
   return max(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Optional[Any]							):
   __lowerCamelCase  							=      __lowerCamelCase  							=      __lowerCamelCase  							=      0
   __lowerCamelCase  							=      []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            __lowerCamelCase  							=      F"""Unanswered question {qa["id"]} will receive score 0."""
            print(_UpperCamelCase	,file=sys.stderr							)
            continue

         __lowerCamelCase  							=      [x['''text'''] for x in qa['''answers''']]
         __lowerCamelCase  							=      predictions[qa['''id''']]

         __lowerCamelCase  							=      metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)
         if int(_exact_match							) == 1:
            correct_ids.append(qa['''id''']							)
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   __lowerCamelCase  							=      exact_match / total
   __lowerCamelCase  							=      fa / total

   return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  a_								=   """1.0"""
  a_								=   argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
  parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
  parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
  parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
  a_								=   parser.parse_args()

  with open(args.data_file) as data_file:
    a_								=   json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
          file=sys.stderr,
      )
    a_								=   dataset_json["""data"""]

  with open(args.pred_file) as pred_file:
    a_								=   json.load(pred_file)

  a_						,  a_								=   evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f"Output {len(correct_ids)} correctly answered question IDs.")
    with open("""correct_ids.json""", """w""") as f:
      json.dump(correct_ids, f)






