



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


_lowerCAmelCase							:   Dict				=				16
_lowerCAmelCase							:   Union[str, Any]				=				32

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Accelerator		,    SCREAMING_SNAKE_CASE__      :		int = 16							)    ->  Dict:





     '''simple docstring'''

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

     def tokenize_function(SCREAMING_SNAKE_CASE__      :		Union[str, Any]							):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCAmelCase     :      List[str]														=       tokenizer(examples["sentence1"]		,    examples["sentence2"]		,    truncation=SCREAMING_SNAKE_CASE__		,    max_length=SCREAMING_SNAKE_CASE__							)
          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():
          _UpperCAmelCase     :      Any														=       datasets.map(
              SCREAMING_SNAKE_CASE__		,    batched=SCREAMING_SNAKE_CASE__		,    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
     _UpperCAmelCase     :      Union[str, Any]														=       tokenized_datasets.rename_column("label"		,    "labels"							)

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

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

     # Instantiate dataloaders.
     _UpperCAmelCase     :      Optional[int]														=       DataLoader(
         tokenized_datasets["train"]		,    shuffle=SCREAMING_SNAKE_CASE__		,    collate_fn=SCREAMING_SNAKE_CASE__		,    batch_size=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       DataLoader(
         tokenized_datasets["validation"]		,    shuffle=SCREAMING_SNAKE_CASE__		,    collate_fn=SCREAMING_SNAKE_CASE__		,    batch_size=SCREAMING_SNAKE_CASE__							)

     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

 _lowerCAmelCase							:   Union[str, Any]				=				mocked_dataloaders  # noqa: F811

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  int:





     '''simple docstring'''

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

     _UpperCAmelCase     :      Dict														=       evaluate.load("glue"		,    "mrpc"							)

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

     set_seed(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       get_dataloaders(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCAmelCase     :      Optional[Any]														=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,    return_dict=SCREAMING_SNAKE_CASE__							)

     # 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).
     _UpperCAmelCase     :      Optional[int]														=       model.to(accelerator.device							)

     # Instantiate optimizer
     _UpperCAmelCase     :      Tuple														=       AdamW(params=model.parameters()		,    lr=SCREAMING_SNAKE_CASE__							)

     # Instantiate scheduler
     _UpperCAmelCase     :      List[Any]														=       get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE__		,    num_warmup_steps=100		,    num_training_steps=(len(SCREAMING_SNAKE_CASE__							) * 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.
     _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       accelerator.prepare(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE__							):
          model.train()
          for step, batch in enumerate(SCREAMING_SNAKE_CASE__							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               _UpperCAmelCase     :      Optional[int]														=       model(**SCREAMING_SNAKE_CASE__							)
               _UpperCAmelCase     :      Dict														=       outputs.loss
               _UpperCAmelCase     :      Tuple														=       loss / gradient_accumulation_steps
               accelerator.backward(SCREAMING_SNAKE_CASE__							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          _UpperCAmelCase     :      Optional[int]														=       0
          for step, batch in enumerate(SCREAMING_SNAKE_CASE__							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               with torch.no_grad():
                    _UpperCAmelCase     :      List[str]														=       model(**SCREAMING_SNAKE_CASE__							)
               _UpperCAmelCase     :      List[str]														=       outputs.logits.argmax(dim=-1							)
               _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       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(SCREAMING_SNAKE_CASE__							) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         _UpperCAmelCase     :      int														=       predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         _UpperCAmelCase     :      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=SCREAMING_SNAKE_CASE__		,    references=SCREAMING_SNAKE_CASE__		,    )

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

def 		__snake_case		( )    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"		,    type=SCREAMING_SNAKE_CASE__		,    default=SCREAMING_SNAKE_CASE__		,    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."							)
     _UpperCAmelCase     :      str														=       parser.parse_args()
     _UpperCAmelCase     :      Union[str, Any]														=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 main()





"""simple docstring"""




# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           if not self.step_with_optimizer:
                # No link between scheduler and optimizer -> just step
                self.scheduler.step(*A	,      **A		)
                return

           # Otherwise, first make sure the optimizer was stepped.
           if not self.gradient_state.sync_gradients:
                if self.gradient_state.adjust_scheduler:
                     self.scheduler._step_count += 1
                return

           for opt in self.optimizers:
                if opt.step_was_skipped:
                     return
           if self.split_batches:
                # Split batches -> the training dataloader batch size is not changed so one step per training step
                self.scheduler.step(*A	,      **A		)
           else:
                # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                # num_processes steps per training step
                _UpperCAmelCase     :      int														=       AcceleratorState().num_processes
                for _ in range(A		):
                     # Special case when using OneCycle and `drop_last` was not used
                     if hasattr(self.scheduler	,      "total_steps"		):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                               self.scheduler.step(*A	,      **A		)
                     else:
                          self.scheduler.step(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
