


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_  :    Union[str, Any]      =			16
a_  :    List[str]      =			32





def __snake_case (				UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 # 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_						:						Tuple		):
  # 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_						:						str		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowerCamelCase_								=  128 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=100 ,    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 __snake_case (				):
 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()







'''simple docstring'''

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





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCAmelCase_						:						Any		):
  lowerCamelCase_								=  set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 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 __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 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_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			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_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			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_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			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)



