




'''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
#
########################################################################


UpperCamelCase_						:      Optional[Any]       =						16
UpperCamelCase_						:      int       =						32





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   int = 16   )						->			List[str]:

     """simple docstring"""




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

     def tokenize_function(_UpperCamelCase:   Optional[int]   ):
          # 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:   Dict   ):
          # 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_						:      Tuple       =						mocked_dataloaders  # noqa: F811





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   List[Any]   )						->			int:

     """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"]   )

     _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

     set_seed(_UpperCAmelCase   )
     _snake_case							=				get_dataloaders(_UpperCAmelCase ,						_UpperCAmelCase   )
     # 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							=				accelerator.prepare(
         _UpperCAmelCase ,						_UpperCAmelCase ,						_UpperCAmelCase ,						_UpperCAmelCase ,						_UpperCAmelCase   )

     # Now we train the model
     for epoch in range(_UpperCAmelCase   ):
          model.train()
          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()

          model.eval()
          _snake_case							=				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   )
               with torch.no_grad():
                    _snake_case							=				model(**_UpperCAmelCase   )
               _snake_case							=				outputs.logits.argmax(dim=-1   )
               _snake_case							=				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(_UpperCAmelCase   ) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         _snake_case							=				predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                         _snake_case							=				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=_UpperCAmelCase ,						references=_UpperCAmelCase ,						)

          _snake_case							=				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"""epoch {epoch}:""" ,						_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."   )
     _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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


