



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
# and perform gradient accumulation
#
# 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)
#
# 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_      :      str    =     16
a_      :      Optional[Any]    =     32






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = 16       ):
 a__												= AutoTokenizer.from_pretrained('''bert-base-cased'''       )
 a__												= load_dataset('''glue'''							,  '''mrpc'''       )

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

 def collate_fn(__UpperCAmelCase       ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  a__												= 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":
   a__												= 16
  elif accelerator.mixed_precision != "no":
   a__												= 8
  else:
   a__												= None

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

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

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     a_      :      Dict    =     mocked_dataloaders  # noqa: F811






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,  __UpperCAmelCase       ) == "1":
  a__												= 2
 # New Code #
 a__												= int(args.gradient_accumulation_steps       )
 # Initialize accelerator
 a__												= Accelerator(
     cpu=args.cpu							,  mixed_precision=args.mixed_precision							,  gradient_accumulation_steps=__UpperCAmelCase       )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''       )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 a__												= config['''lr''']
 a__												= int(config['''num_epochs''']       )
 a__												= int(config['''seed''']       )
 a__												= int(config['''batch_size''']       )

 a__												= evaluate.load('''glue'''							,  '''mrpc'''       )

 set_seed(__UpperCAmelCase       )
 a__							,			a__												= get_dataloaders(__UpperCAmelCase							,  __UpperCAmelCase       )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 a__												= 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).
 a__												= model.to(accelerator.device       )

 # Instantiate optimizer
 a__												= AdamW(params=model.parameters()							,  lr=__UpperCAmelCase       )

 # Instantiate scheduler
 a__												= get_linear_schedule_with_warmup(
     optimizer=__UpperCAmelCase							,  num_warmup_steps=100							,  num_training_steps=(len(__UpperCAmelCase       ) * num_epochs)							,  )

 # 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__							,			a__							,			a__							,			a__							,			a__												= accelerator.prepare(
     __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       )

 # Now we train the model
 for epoch in range(__UpperCAmelCase       ):
  model.train()
  for step, batch in enumerate(__UpperCAmelCase       ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device       )
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(__UpperCAmelCase       ):
    a__												= model(**__UpperCAmelCase       )
    a__												= output.loss
    accelerator.backward(__UpperCAmelCase       )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  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():
    a__												= model(**__UpperCAmelCase       )
   a__												= outputs.logits.argmax(dim=-1       )
   a__							,			a__												= accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
   metric.add_batch(
       predictions=__UpperCAmelCase							,  references=__UpperCAmelCase							,  )

  a__												= metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:"							,  __UpperCAmelCase       )






def     __a					(		):
 a__												= 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.'''							,  )
 # New Code #
 parser.add_argument(
     '''--gradient_accumulation_steps'''							,  type=__UpperCAmelCase							,  default=1							,  help='''The number of minibatches to be ran before gradients are accumulated.'''							,  )
 parser.add_argument('''--cpu'''							,  action='''store_true'''							,  help='''If passed, will train on the CPU.'''       )
 a__												= parser.parse_args()
 a__												= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCAmelCase							,  __UpperCAmelCase       )


if __name__ == "__main__":
     main()










import requests
from bsa import BeautifulSoup






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= BeautifulSoup(requests.get(__UpperCAmelCase							,  params=__UpperCAmelCase       ).content							,  '''html.parser'''       )
 a__												= soup.find('''div'''							,  attrs={'''class''': '''gs_ri'''}       )
 a__												= div.find('''div'''							,  attrs={'''class''': '''gs_fl'''}       ).find_all('''a'''       )
 return anchors[2].get_text()


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         'title': (
             'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
             'capacitance, volumetric capacitance, and energy density'
         ),
         'journal': 'Chem. Mater.',
         'volume': 30,
         'pages': '3979-3990',
         'year': 20_18,
         'hl': 'en',
     }
     print(get_citation('https://scholar.google.com/scholar_lookup', params=params))





