



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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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 							A     (					_UpperCAmelCase    :       Accelerator							,       _UpperCAmelCase    :       int = 16   )    ->			Tuple:







   '''simple docstring'''
   _UpperCAmelCase			=       AutoTokenizer.from_pretrained('bert-base-cased'   )
   _UpperCAmelCase			=       load_dataset('glue'							,       'mrpc'   )

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

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

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

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

   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

       UpperCAmelCase__       =	mocked_dataloaders  # noqa: F811





def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       Dict   )    ->			List[str]:







   '''simple docstring'''






   if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       _a   ) == "1":
      _UpperCAmelCase			=       2
   # Initialize accelerator
   _UpperCAmelCase			=       Accelerator(cpu=args.cpu							,       mixed_precision=args.mixed_precision   )
   # 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			=       evaluate.load('glue'							,       'mrpc'   )

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

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

   # 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).
   _UpperCAmelCase			=       model.to(accelerator.device   )

   # Instantiate optimizer
   _UpperCAmelCase			=       AdamW(params=model.parameters()							,       lr=_a   )

   # Instantiate scheduler
   _UpperCAmelCase			=       get_linear_schedule_with_warmup(
       optimizer=_a							,       num_warmup_steps=100							,       num_training_steps=(len(_a   ) * 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.
   _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       accelerator.prepare(
       _a							,       _a							,       _a							,       _a							,       _a   )

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

      model.eval()
      _UpperCAmelCase			=       0
      for step, batch in enumerate(_a   ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device   )
         with torch.no_grad():
            _UpperCAmelCase			=       model(**_a   )
         _UpperCAmelCase			=       outputs.logits.argmax(dim=-1   )
         _UpperCAmelCase	,					_UpperCAmelCase			=       accelerator.gather((predictions, batch['labels'])   )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(_a   ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               _UpperCAmelCase			=       predictions[: len(eval_dataloader.dataset   ) - samples_seen]
               _UpperCAmelCase			=       references[: len(eval_dataloader.dataset   ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=_a							,       references=_a							,       )

      _UpperCAmelCase			=       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"							,       _a   )





def 							A     (					)    ->			Optional[Any]:







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


if __name__ == "__main__":
       main()





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat





def 							A     (					)    ->			tuple[list[int], int]:







			'''simple docstring'''
			_UpperCAmelCase			=       [randint(-1_000							,       1_000   ) for i in range(10   )]
			_UpperCAmelCase			=       randint(-5_000							,       5_000   )
			return (arr, r)


UpperCAmelCase__       =	make_dataset()





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, ...]:







			'''simple docstring'''
			for triplet in permutations(_UpperCAmelCase							,       3   ):
						if sum(_UpperCAmelCase   ) == target:
									return tuple(sorted(_UpperCAmelCase   )   )
			return (0, 0, 0)





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, int, int]:







			'''simple docstring'''
			arr.sort()
			_UpperCAmelCase			=       len(_UpperCAmelCase   )
			for i in range(n - 1   ):
						_UpperCAmelCase	,					_UpperCAmelCase			=       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)





def 							A     (					)    ->			tuple[float, float]:







			'''simple docstring'''
			_UpperCAmelCase			=       '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			_UpperCAmelCase			=       '\ntriplet_sum1(*dataset)\n'
			_UpperCAmelCase			=       '\ntriplet_sum2(*dataset)\n'
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			return (min(_UpperCAmelCase   ), min(_UpperCAmelCase   ))


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCAmelCase__       =	solution_times()
							print(f"""The time for naive implementation is {times[0]}.""")
							print(f"""The time for optimized implementation is {times[1]}.""")
