





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
#
########################################################################


_lowerCAmelCase				:   Union[str, Any]						       =						16
_lowerCAmelCase				:   int						       =						32

def   __snake_case    (       _lowerCAmelCase			:			Accelerator					,					_lowerCAmelCase			:			int = 16						)			->    Any:
   A_       :					str        =      AutoTokenizer.from_pretrained("bert-base-cased"						)
   A_       :					List[Any]        =      load_dataset("glue"					,					"mrpc"						)

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

   def collate_fn(_lowerCAmelCase			:			Any						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_       :					Tuple        =      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_       :					Optional[Any]        =      16
      elif accelerator.mixed_precision != "no":
         A_       :					Any        =      8
      else:
         A_       :					Union[str, Any]        =      None

      return tokenizer.pad(
          _lowerCAmelCase					,					padding="longest"					,					max_length=_lowerCAmelCase					,					pad_to_multiple_of=_lowerCAmelCase					,					return_tensors="pt"					,					)

   # Instantiate dataloaders.
   A_       :					Optional[int]        =      DataLoader(
       tokenized_datasets["train"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=_lowerCAmelCase						)
   A_       :					str        =      DataLoader(
       tokenized_datasets["validation"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=_lowerCAmelCase						)

   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

 _lowerCAmelCase				:   Optional[Any]						       =						mocked_dataloaders  # noqa: F811

def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[str]						)			->    Union[str, Any]:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"					,					_lowerCAmelCase						) == "1":
      A_       :					str        =      2
   # Initialize accelerator
   A_       :					List[str]        =      Accelerator(cpu=args.cpu					,					mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_       :					Any        =      config["lr"]
   A_       :					List[str]        =      int(config["num_epochs"]						)
   A_       :					Optional[Any]        =      int(config["seed"]						)
   A_       :					Dict        =      int(config["batch_size"]						)

   A_       :					Any        =      evaluate.load("glue"					,					"mrpc"						)

   # If the batch size is too big we use gradient accumulation
   A_       :					List[Any]        =      1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_       :					List[Any]        =      batch_size // MAX_GPU_BATCH_SIZE
      A_       :					Optional[Any]        =      MAX_GPU_BATCH_SIZE

   set_seed(_lowerCAmelCase						)
   A_   ,    A_       :					Dict        =      get_dataloaders(_lowerCAmelCase					,					_lowerCAmelCase						)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_       :					str        =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,					return_dict=_lowerCAmelCase						)

   # 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_       :					Union[str, Any]        =      model.to(accelerator.device						)

   # Instantiate optimizer
   A_       :					int        =      AdamW(params=model.parameters()					,					lr=_lowerCAmelCase						)

   # Instantiate scheduler
   A_       :					Any        =      get_linear_schedule_with_warmup(
       optimizer=_lowerCAmelCase					,					num_warmup_steps=100					,					num_training_steps=(len(_lowerCAmelCase						) * 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.
   A_   ,    A_   ,    A_   ,    A_   ,    A_       :					Any        =      accelerator.prepare(
       _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

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

      model.eval()
      A_       :					Optional[Any]        =      0
      for step, batch in enumerate(_lowerCAmelCase						):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device						)
         with torch.no_grad():
            A_       :					List[Any]        =      model(**_lowerCAmelCase						)
         A_       :					List[str]        =      outputs.logits.argmax(dim=-1						)
         A_   ,    A_       :					Tuple        =      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(_lowerCAmelCase						) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               A_       :					Union[str, Any]        =      predictions[: len(eval_dataloader.dataset						) - samples_seen]
               A_       :					Tuple        =      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=_lowerCAmelCase					,					references=_lowerCAmelCase					,					)

      A_       :					Union[str, Any]        =      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"					,					_lowerCAmelCase						)

def   __snake_case    (       )			->    Any:
   A_       :					List[str]        =      argparse.ArgumentParser(description="Simple example of training script."						)
   parser.add_argument(
       "--mixed_precision"					,					type=_lowerCAmelCase					,					default=_lowerCAmelCase					,					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."						)
   A_       :					Optional[Any]        =      parser.parse_args()
   A_       :					Any        =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_lowerCAmelCase					,					_lowerCAmelCase						)


if __name__ == "__main__":
 main()







from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
