'''simple docstring'''




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 the experiment tracking capability,
# 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												=1_6
__UpperCAmelCase												=3_2






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__ = 16    )     ->   Union[str, Any]:
    __lowerCamelCase          =  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    __lowerCamelCase          =  load_dataset('''glue'''    ,  '''mrpc'''    )

    def tokenize_function(UpperCamelCase__    ):
        # max_length=None => use the model max length (it's actually the default)
        __lowerCamelCase          =  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():
        __lowerCamelCase          =  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
    __lowerCamelCase          =  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.
        __lowerCamelCase          =  1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __lowerCamelCase          =  16
        elif accelerator.mixed_precision != "no":
            __lowerCamelCase          =  8
        else:
            __lowerCamelCase          =  None

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

    # Instantiate dataloaders.
    __lowerCamelCase          =  DataLoader(
        tokenized_datasets['''train''']    ,  shuffle=UpperCamelCase__    ,  collate_fn=UpperCamelCase__    ,  batch_size=UpperCamelCase__    )
    __lowerCamelCase          =  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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Optional[Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,  UpperCamelCase__    ) == "1":
        __lowerCamelCase          =  2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        __lowerCamelCase          =  Accelerator(
            cpu=args.cpu    ,  mixed_precision=args.mixed_precision    ,  log_with='''all'''    ,  project_dir=args.project_dir    )
    else:
        __lowerCamelCase          =  Accelerator(cpu=args.cpu    ,  mixed_precision=args.mixed_precision    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowerCamelCase          =  config['''lr''']
    __lowerCamelCase          =  int(config['''num_epochs''']    )
    __lowerCamelCase          =  int(config['''seed''']    )
    __lowerCamelCase          =  int(config['''batch_size''']    )
    set_seed(UpperCamelCase__    )

    __lowerCamelCase     ,  __lowerCamelCase          =  get_dataloaders(UpperCamelCase__    ,  UpperCamelCase__    )
    __lowerCamelCase          =  evaluate.load('''glue'''    ,  '''mrpc'''    )

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __lowerCamelCase          =  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).
    __lowerCamelCase          =  model.to(accelerator.device    )

    # Instantiate optimizer
    __lowerCamelCase          =  AdamW(params=model.parameters()    ,  lr=UpperCamelCase__    )

    # Instantiate scheduler
    __lowerCamelCase          =  get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__    ,  num_warmup_steps=1_00    ,  num_training_steps=(len(UpperCamelCase__    ) * 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.
    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  accelerator.prepare(
        UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        __lowerCamelCase          =  os.path.split(UpperCamelCase__    )[-1].split('''.'''    )[0]
        accelerator.init_trackers(UpperCamelCase__    ,  UpperCamelCase__    )

    # Now we train the model
    for epoch in range(UpperCamelCase__    ):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            __lowerCamelCase          =  0
        for step, batch in enumerate(UpperCamelCase__    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            __lowerCamelCase          =  model(**UpperCamelCase__    )
            __lowerCamelCase          =  outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            __lowerCamelCase          =  loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase__    )
            if step % gradient_accumulation_steps == 0:
                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` (the default).
            batch.to(accelerator.device    )
            with torch.no_grad():
                __lowerCamelCase          =  model(**UpperCamelCase__    )
            __lowerCamelCase          =  outputs.logits.argmax(dim=-1    )
            __lowerCamelCase     ,  __lowerCamelCase          =  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
            metric.add_batch(
                predictions=UpperCamelCase__    ,  references=UpperCamelCase__    ,  )

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

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(UpperCamelCase__    ),
                    '''epoch''': epoch,
                }    ,  step=UpperCamelCase__    ,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()






def __lowerCAmelCase			(							)     ->   str:
    __lowerCamelCase          =  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.'''    )
    parser.add_argument(
        '''--with_tracking'''    ,  action='''store_true'''    ,  help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,  )
    parser.add_argument(
        '''--project_dir'''    ,  type=UpperCamelCase__    ,  default='''logs'''    ,  help='''Location on where to store experiment tracking logs` and relevent project information'''    ,  )
    __lowerCamelCase          =  parser.parse_args()
    __lowerCamelCase          =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__    ,  UpperCamelCase__    )


if __name__ == "__main__":
  main()

'''simple docstring'''




from __future__ import annotations






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   list[list[int]]:
    __lowerCamelCase          =  []
    create_all_state(1    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  []    ,  UpperCamelCase__    )
    return result






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  )     ->   None:
    if level == 0:
        total_list.append(current_list[:]    )
        return

    for i in range(UpperCamelCase__    ,  total_number - level + 2    ):
        current_list.append(UpperCamelCase__    )
        create_all_state(i + 1    ,  UpperCamelCase__    ,  level - 1    ,  UpperCamelCase__    ,  UpperCamelCase__    )
        current_list.pop()






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   None:
    for i in total_list:
        print(*UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=4
  __UpperCAmelCase												=2
  __UpperCAmelCase												=generate_all_combinations(n, k)
  print_all_state(total_list)
