
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowercase__				:				Union[str, Any]						=      TypeVar("DatasetType", Dataset, IterableDataset)




def 						A_			(				snake_case				: DatasetType			,   snake_case				: int			,   snake_case				: int			)   ->     DatasetType:



						'''simple docstring'''

						if isinstance(snake_case			,   snake_case			):
												return _split_by_node_map_style_dataset(snake_case			,   rank=snake_case			,   world_size=snake_case			)
						else:
												return _split_by_node_iterable_dataset(snake_case			,   rank=snake_case			,   world_size=snake_case			)












"""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 checkpointing 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											=     16
UpperCamelCase											=     32






def 							lowerCAmelCase			(   UpperCamelCase_:				Accelerator  ,			UpperCamelCase_:				int = 16       )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						AutoTokenizer.from_pretrained("bert-base-cased"       )
  _a					      =						load_dataset("glue"  ,			"mrpc"       )

  def tokenize_function(UpperCamelCase_:				str       ):
    # max_length=None => use the model max length (it's actually the default)
    _a					      =						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():
    _a					      =						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
  _a					      =						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.
    _a					      =						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					      =						16
    elif accelerator.mixed_precision != "no":
      _a					      =						8
    else:
      _a					      =						None

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

  # Instantiate dataloaders.
  _a					      =						DataLoader(
      tokenized_datasets["train"]  ,			shuffle=UpperCamelCase_  ,			collate_fn=UpperCamelCase_  ,			batch_size=UpperCamelCase_       )
  _a					      =						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_:				int  ,			UpperCamelCase_:				Optional[int]       )							->				Optional[Any]:







  '''simple docstring'''







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

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps  ,			"isdigit"       ):
    if args.checkpointing_steps == "epoch":
      _a					      =						args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _a					      =						int(args.checkpointing_steps       )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
  else:
    _a					      =						None

  set_seed(UpperCamelCase_       )

  _a      ,						_a					      =						get_dataloaders(UpperCamelCase_  ,			UpperCamelCase_       )
  _a					      =						evaluate.load("glue"  ,			"mrpc"       )

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

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

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

  # Instantiate scheduler
  _a					      =						get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase_  ,			num_warmup_steps=100  ,			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.
  _a      ,						_a      ,						_a      ,						_a      ,						_a					      =						accelerator.prepare(
      UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  _a					      =						0
  # We also need to keep track of the stating epoch so files are named properly
  _a					      =						0

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''       )
      accelerator.load_state(args.resume_from_checkpoint       )
      _a					      =						os.path.basename(args.resume_from_checkpoint       )
    else:
      # Get the most recent checkpoint
      _a					      =						[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
      dirs.sort(key=os.path.getctime       )
      _a					      =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _a					      =						os.path.splitext(UpperCamelCase_       )[0]

    if "epoch" in training_difference:
      _a					      =						int(training_difference.replace("epoch_"  ,			""       )       ) + 1
      _a					      =						None
    else:
      _a					      =						int(training_difference.replace("step_"  ,			""       )       )
      _a					      =						resume_step // len(UpperCamelCase_       )
      resume_step -= starting_epoch * len(UpperCamelCase_       )

    # Now we train the model
  for epoch in range(UpperCamelCase_  ,			UpperCamelCase_       ):
    model.train()
    # New Code #
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _a					      =						accelerator.skip_first_batches(UpperCamelCase_  ,			UpperCamelCase_       )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _a					      =						train_dataloader
    for step, batch in enumerate(UpperCamelCase_       ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device       )
      _a					      =						model(**UpperCamelCase_       )
      _a					      =						outputs.loss
      _a					      =						loss / gradient_accumulation_steps
      accelerator.backward(UpperCamelCase_       )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(UpperCamelCase_  ,			UpperCamelCase_       ):
        _a					      =						f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _a					      =						os.path.join(args.output_dir  ,			UpperCamelCase_       )
          accelerator.save_state(UpperCamelCase_       )

    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():
        _a					      =						model(**UpperCamelCase_       )
      _a					      =						outputs.logits.argmax(dim=-1       )
      _a      ,						_a					      =						accelerator.gather_for_metrics((predictions, batch["labels"])       )
      metric.add_batch(
          predictions=UpperCamelCase_  ,			references=UpperCamelCase_  ,			)

    _a					      =						metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''  ,			UpperCamelCase_       )

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      _a					      =						f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _a					      =						os.path.join(args.output_dir  ,			UpperCamelCase_       )
      accelerator.save_state(UpperCamelCase_       )






def 							lowerCAmelCase			(   )							->				Tuple:







  '''simple docstring'''







  _a					      =						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(
      "--checkpointing_steps"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."  ,			)
  parser.add_argument(
      "--output_dir"  ,			type=UpperCamelCase_  ,			default="."  ,			help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."  ,			)
  parser.add_argument(
      "--resume_from_checkpoint"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="If the training should continue from a checkpoint folder."  ,			)
  _a					      =						parser.parse_args()
  _a					      =						{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(UpperCamelCase_  ,			UpperCamelCase_       )


if __name__ == "__main__":
     main()


