






'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     list[list[int]]							     =  []
   lowercase_						:     list[int]							     =  []
   lowercase_						:     str							     =  0
   lowercase_						:     List[str]							     =  sum(_UpperCamelCase				)
   create_state_space_tree(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   return result




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		):


   """simple docstring"""




   if sum(_UpperCamelCase				) > max_sum or (remaining_nums_sum + sum(_UpperCamelCase				)) < max_sum:
      return
   if sum(_UpperCamelCase				) == max_sum:
      result.append(_UpperCamelCase				)
      return
   for index in range(_UpperCamelCase    ,		len(_UpperCamelCase				)				):
      create_state_space_tree(
          _UpperCamelCase    ,		_UpperCamelCase    ,		index + 1    ,		[*path, nums[index]]    ,		_UpperCamelCase    ,		remaining_nums_sum - nums[index]    ,		)
UpperCamelCase__			=      [3, 34, 4, 12, 5, 2]
UpperCamelCase__			=      9
UpperCamelCase__			=      generate_sum_of_subsets_soln(nums, max_sum)





print(*result)












'''simple docstring'''
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16				):


   """simple docstring"""




   lowercase_						:     int							     =  AutoTokenizer.from_pretrained("bert-base-cased"				)
   lowercase_						:     Optional[Any]							     =  load_dataset("glue"    ,		"mrpc"				)

   def tokenize_function(_UpperCamelCase				):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_						:     Any							     =  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():
      lowercase_						:     str							     =  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
   lowercase_						:     str							     =  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.
      lowercase_						:     List[str]							     =  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_						:     List[str]							     =  16
      elif accelerator.mixed_precision != "no":
         lowercase_						:     Tuple							     =  8
      else:
         lowercase_						:     Dict							     =  None

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

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

 UpperCamelCase__			=      mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,		_UpperCamelCase				) == "1":
      lowercase_						:     Tuple							     =  2
   # Initialize accelerator
   lowercase_						:     Tuple							     =  Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_						:     str							     =  config["lr"]
   lowercase_						:     Optional[int]							     =  int(config["num_epochs"]				)
   lowercase_						:     List[str]							     =  int(config["seed"]				)
   lowercase_						:     List[Any]							     =  int(config["batch_size"]				)

   lowercase_						:     List[Any]							     =  evaluate.load("glue"    ,		"mrpc"				)

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=_UpperCamelCase				)
   def inner_training_loop(_UpperCamelCase				):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(_UpperCamelCase				)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase_						:     List[str]							     =  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).
      lowercase_						:     List[Any]							     =  model.to(accelerator.device				)

      # Instantiate optimizer
      lowercase_						:     Tuple							     =  AdamW(params=model.parameters()    ,		lr=_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     Any							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase				)

      # Instantiate scheduler
      lowercase_						:     Union[str, Any]							     =  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.
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  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				)
            lowercase_						:     Optional[Any]							     =  model(**_UpperCamelCase				)
            lowercase_						:     List[Any]							     =  outputs.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():
               lowercase_						:     Optional[int]							     =  model(**_UpperCamelCase				)
            lowercase_						:     Union[str, Any]							     =  outputs.logits.argmax(dim=-1				)
            lowercase_     ,		lowercase_						:     str							     =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=_UpperCamelCase    ,		references=_UpperCamelCase    ,		)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  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."				)
   lowercase_						:     int							     =  parser.parse_args()
   lowercase_						:     Optional[Any]							     =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()




