


import argparse

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)
#
# 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
#
########################################################################


UpperCAmelCase__   	=							16
UpperCAmelCase__   	=							32
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case = 16						)     ->			Any:







 """simple docstring"""


 _lowercase							      =AutoTokenizer.from_pretrained('''bert-base-cased'''						)
 _lowercase							      =load_dataset('''glue'''     ,						'''mrpc'''						)

 def tokenize_function(__snake_case						):
  # max_length=None => use the model max length (it's actually the default)
  _lowercase							      =tokenizer(examples['''sentence1''']     ,						examples['''sentence2''']     ,						truncation=__snake_case     ,						max_length=__snake_case						)
  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():
  _lowercase							      =datasets.map(
      __snake_case     ,						batched=__snake_case     ,						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
 _lowercase							      =tokenized_datasets.rename_column('''label'''     ,						'''labels'''						)

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

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

 # Instantiate dataloaders.
 _lowercase							      =DataLoader(
     tokenized_datasets['''train''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__snake_case     ,						drop_last=__snake_case						)
 _lowercase							      =DataLoader(
     tokenized_datasets['''validation''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__snake_case     ,						drop_last=(accelerator.mixed_precision == '''fp8''')     ,						)

 return train_dataloader, eval_dataloader
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision						)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _lowercase							      =config['''lr''']
 _lowercase							      =int(config['''num_epochs''']						)
 _lowercase							      =int(config['''seed''']						)
 _lowercase							      =int(config['''batch_size''']						)

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

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

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

 # 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).
 _lowercase							      =model.to(accelerator.device						)
 # Instantiate optimizer
 _lowercase							      =AdamW(params=model.parameters()     ,						lr=__snake_case						)

 # Instantiate scheduler
 _lowercase							      =get_linear_schedule_with_warmup(
     optimizer=__snake_case     ,						num_warmup_steps=100     ,						num_training_steps=(len(__snake_case						) * 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.

 _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =accelerator.prepare(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)

 # Now we train the model
 for epoch in range(__snake_case						):
  model.train()
  for step, batch in enumerate(__snake_case						):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device						)
   _lowercase							      =model(**__snake_case						)
   _lowercase							      =outputs.loss
   _lowercase							      =loss / gradient_accumulation_steps
   accelerator.backward(__snake_case						)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  _lowercase							      =metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"     ,						__snake_case						)
def 				UpperCAmelCase_					(							)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser(description='''Simple example of training script.'''						)
 parser.add_argument(
     '''--mixed_precision'''     ,						type=__snake_case     ,						default=__snake_case     ,						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.'''						)
 _lowercase							      =parser.parse_args()
 _lowercase							      ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  main()





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

