




'''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_		:    Union[str, Any]									=		16
UpperCamelCase_		:    int									=		32





def 			__a			(     _UpperCamelCase:					Optional[int]      ,		_UpperCamelCase:					Optional[int] = 16					)		->				int:

     """simple docstring"""




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

     def tokenize_function(_UpperCamelCase:					Tuple					):
          # max_length=None => use the model max length (it's actually the default)
          _snake_case							=   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():
          _snake_case							=   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
     _snake_case							=   tokenized_datasets.rename_column("label"      ,		"labels"					)

     def collate_fn(_UpperCamelCase:					Optional[int]					):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _snake_case							=   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":
               _snake_case							=   16
          elif accelerator.mixed_precision != "no":
               _snake_case							=   8
          else:
               _snake_case							=   None

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

     # Instantiate dataloaders.
     _snake_case							=   DataLoader(
         tokenized_datasets["train"]      ,		shuffle=_UpperCamelCase      ,		collate_fn=_UpperCamelCase      ,		batch_size=_UpperCamelCase					)
     _snake_case							=   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_		:    Any									=		mocked_dataloaders  # noqa: F811





def 			__a			(     _UpperCamelCase:					Union[str, Any]      ,		_UpperCamelCase:					Tuple					)		->				Dict:

     """simple docstring"""




     if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,		_UpperCamelCase					) == "1":
          _snake_case							=   2
     # Initialize accelerator
     _snake_case							=   Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision					)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _snake_case							=   config["lr"]
     _snake_case							=   int(config["num_epochs"]					)
     _snake_case							=   int(config["seed"]					)
     _snake_case							=   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":
               _snake_case							=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _snake_case							=   int(args.checkpointing_steps					)
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""					)
     else:
          _snake_case							=   None

     set_seed(_UpperCamelCase					)

     _snake_case							,      _snake_case							=   get_dataloaders(_UpperCamelCase      ,		_UpperCamelCase					)
     _snake_case							=   evaluate.load("glue"      ,		"mrpc"					)

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

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

     # Instantiate optimizer
     _snake_case							=   AdamW(params=model.parameters()      ,		lr=_UpperCamelCase					)

     # Instantiate scheduler
     _snake_case							=   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.
     _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   accelerator.prepare(
         _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase					)

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     _snake_case							=   0
     # We also need to keep track of the stating epoch so files are named properly
     _snake_case							=   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					)
               _snake_case							=   os.path.basename(args.resume_from_checkpoint					)
          else:
               # Get the most recent checkpoint
               _snake_case							=   [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
               dirs.sort(key=os.path.getctime					)
               _snake_case							=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _snake_case							=   os.path.splitext(_UpperCamelCase					)[0]

          if "epoch" in training_difference:
               _snake_case							=   int(training_difference.replace("epoch_"      ,		""					)					) + 1
               _snake_case							=   None
          else:
               _snake_case							=   int(training_difference.replace("step_"      ,		""					)					)
               _snake_case							=   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
               _snake_case							=   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
               _snake_case							=   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					)
               _snake_case							=   model(**_UpperCamelCase					)
               _snake_case							=   outputs.loss
               _snake_case							=   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					):
                    _snake_case							=   F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _snake_case							=   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():
                    _snake_case							=   model(**_UpperCamelCase					)
               _snake_case							=   outputs.logits.argmax(dim=-1					)
               _snake_case							,      _snake_case							=   accelerator.gather_for_metrics((predictions, batch["labels"])					)
               metric.add_batch(
                   predictions=_UpperCamelCase      ,		references=_UpperCamelCase      ,		)

          _snake_case							=   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":
               _snake_case							=   F"""epoch_{epoch}"""
               if args.output_dir is not None:
                    _snake_case							=   os.path.join(args.output_dir      ,		_UpperCamelCase					)
               accelerator.save_state(_UpperCamelCase					)





def 			__a			(     )		->				List[str]:

     """simple docstring"""




     _snake_case							=   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."      ,		)
     _snake_case							=   parser.parse_args()
     _snake_case							=   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(_UpperCamelCase      ,		_UpperCamelCase					)


if __name__ == "__main__":
   main()








'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_		:    List[str]									=		500000

UpperCamelCase_			,			UpperCamelCase_		:    Dict									=		os.path.split(__file__)
UpperCamelCase_		:    int									=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.select(range(0      ,		len(_UpperCamelCase					)      ,		2					)					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.sort("numbers"					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.shuffle()





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				Any:

     """simple docstring"""




     _snake_case							=   dataset.train_test_split(0.1					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset      ,		_UpperCamelCase:					int=10					)		->				List[str]:

     """simple docstring"""




     for shard_id in range(_UpperCamelCase					):
          _snake_case							=   dataset.shard(_UpperCamelCase      ,		_UpperCamelCase					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   {"num examples": SPEED_TEST_N_EXAMPLES}
     _snake_case							=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset"					)
          _snake_case							=   datasets.Features({"text": datasets.Value("string"					), "numbers": datasets.Value("float32"					)}					)
          _snake_case							=   generate_example_dataset(
              os.path.join(_UpperCamelCase      ,		"dataset.arrow"					)      ,		_UpperCamelCase      ,		num_examples=_UpperCamelCase					)
          print("Functions"					)
          for func in functions:
               print(func.__name__					)
               _snake_case							=   func(_UpperCamelCase					)

     with open(_UpperCamelCase      ,		"wb"					) as f:
          f.write(json.dumps(_UpperCamelCase					).encode("utf-8"					)					)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()


