"""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 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
#
########################################################################


_a      :		int           =   16
_a      :		str           =   32





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

  def tokenize_function(_lowerCamelCase						:       Optional[Any]	):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase						:       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():
    _lowerCAmelCase						:       Tuple  			=     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
  _lowerCAmelCase						:       Tuple  			=     tokenized_datasets.rename_column("""label"""			,"""labels"""	)

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

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

  # Instantiate dataloaders.
  _lowerCAmelCase						:       List[str]  			=     DataLoader(
      tokenized_datasets["""train"""]			,shuffle=_lowerCamelCase			,collate_fn=_lowerCamelCase			,batch_size=_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     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

     _a      :		Any           =   mocked_dataloaders  # noqa: F811





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Any	)       ->      List[str]:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,_lowerCamelCase	) == "1":
    _lowerCAmelCase						:       str  			=     2
  # Initialize accelerator
  _lowerCAmelCase						:       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
  _lowerCAmelCase						:       List[str]  			=     config["""lr"""]
  _lowerCAmelCase						:       Tuple  			=     int(config["""num_epochs"""]	)
  _lowerCAmelCase						:       List[str]  			=     int(config["""seed"""]	)
  _lowerCAmelCase						:       Union[str, Any]  			=     int(config["""batch_size"""]	)

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

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

  set_seed(_lowerCamelCase	)
  _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     get_dataloaders(_lowerCamelCase			,_lowerCamelCase	)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase						:       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).
  _lowerCAmelCase						:       Dict  			=     model.to(accelerator.device	)

  # Instantiate optimizer
  _lowerCAmelCase						:       List[str]  			=     AdamW(params=model.parameters()			,lr=_lowerCamelCase	)

  # Instantiate scheduler
  _lowerCAmelCase						:       Union[str, 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.
  _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     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	)
      _lowerCAmelCase						:       Optional[int]  			=     model(**_lowerCamelCase	)
      _lowerCAmelCase						:       Optional[Any]  			=     outputs.loss
      _lowerCAmelCase						:       Optional[int]  			=     loss / gradient_accumulation_steps
      accelerator.backward(_lowerCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    _lowerCAmelCase						:       List[str]  			=     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():
        _lowerCAmelCase						:       Union[str, Any]  			=     model(**_lowerCamelCase	)
      _lowerCAmelCase						:       Optional[Any]  			=     outputs.logits.argmax(dim=-1	)
      _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     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
          _lowerCAmelCase						:       List[str]  			=     predictions[: len(eval_dataloader.dataset	) - samples_seen]
          _lowerCAmelCase						:       int  			=     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			,)

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





def 							SCREAMING_SNAKE_CASE     (			)       ->      str:
  _lowerCAmelCase						:       List[Any]  			=     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."""	)
  _lowerCAmelCase						:       Union[str, Any]  			=     parser.parse_args()
  _lowerCAmelCase						:       Optional[int]  			=     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(_lowerCamelCase			,_lowerCamelCase	)


if __name__ == "__main__":
     main()





"""simple docstring"""




from math import ceil





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     list(range(0			,_lowerCamelCase	)	)

  _lowerCAmelCase						:       Tuple  			=     [item for sublist in list(device_map.values()	) for item in sublist]

  # Duplicate check
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  for i in device_map_blocks:
    if device_map_blocks.count(_lowerCamelCase	) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_lowerCamelCase	)
    # Missing blocks
  _lowerCAmelCase						:       int  			=     [i for i in blocks if i not in device_map_blocks]
  _lowerCAmelCase						:       List[Any]  			=     [i for i in device_map_blocks if i not in blocks]

  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
        """ These attention blocks were specified more than once: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """There are attention blocks for this model that are not specified in the device_map. Add these attention """
        """blocks to a device on the device_map: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
        + str(_lowerCamelCase	)	)






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(range(_lowerCamelCase	)	)
  _lowerCAmelCase						:       Optional[Any]  			=     int(ceil(n_layers / len(_lowerCamelCase	)	)	)
  _lowerCAmelCase						:       Optional[int]  			=     [layers[i : i + n_blocks] for i in range(0			,_lowerCamelCase			,_lowerCamelCase	)]

  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)




