'''simple docstring'''




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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				:		str       			=		32



def 						__snake_case (       lowerCAmelCase       :     Accelerator      ,  lowerCAmelCase       :     int = 16			):
 __UpperCAmelCase         =					AutoTokenizer.from_pretrained('bert-base-cased'			)
 __UpperCAmelCase         =					load_dataset('glue'      ,  'mrpc'			)

 def tokenize_function(lowerCAmelCase       :     List[str]			):
  # max_length=None => use the model max length (it's actually the default)
  __UpperCAmelCase         =					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():
  __UpperCAmelCase         =					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
 __UpperCAmelCase         =					tokenized_datasets.rename_column('label'      ,  'labels'			)

 def collate_fn(lowerCAmelCase       :     List[str]			):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   __UpperCAmelCase         =					16
  elif accelerator.mixed_precision != "no":
   __UpperCAmelCase         =					8
  else:
   __UpperCAmelCase         =					None

  return tokenizer.pad(
      lowerCAmelCase      ,  padding='longest'      ,  pad_to_multiple_of=lowerCAmelCase      ,  return_tensors='pt'      ,  )

 # Instantiate dataloaders.
 __UpperCAmelCase         =					DataLoader(
     tokenized_datasets['train']      ,  shuffle=lowerCAmelCase      ,  collate_fn=lowerCAmelCase      ,  batch_size=lowerCAmelCase			)
 __UpperCAmelCase         =					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

     _UpperCamelCase				:		int       			=		mocked_dataloaders  # noqa: F811



def 						__snake_case (       lowerCAmelCase       :     int      ,  lowerCAmelCase       :     Dict			):
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,  lowerCAmelCase			) == "1":
  __UpperCAmelCase         =					2
 # Initialize accelerator
 __UpperCAmelCase         =					Accelerator(cpu=args.cpu      ,  mixed_precision=args.mixed_precision			)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __UpperCAmelCase         =					config['lr']
 __UpperCAmelCase         =					int(config['num_epochs']			)
 __UpperCAmelCase         =					int(config['seed']			)
 __UpperCAmelCase         =					int(config['batch_size']			)

 __UpperCAmelCase         =					evaluate.load('glue'      ,  'mrpc'			)

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase			)			)
 def inner_training_loop(lowerCAmelCase       :     Tuple			):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  __UpperCAmelCase         =					observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  __UpperCAmelCase         =					num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(lowerCAmelCase			)

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

  # Instantiate optimizer
  __UpperCAmelCase         =					AdamW(params=model.parameters()      ,  lr=lowerCAmelCase			)
  __UpperCAmelCase  ,			__UpperCAmelCase         =					get_dataloaders(lowerCAmelCase      ,  lowerCAmelCase			)

  # Instantiate scheduler
  __UpperCAmelCase         =					get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase      ,  num_warmup_steps=100      ,  num_training_steps=(len(lowerCAmelCase			) * num_epochs)      ,  )

  # 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         =					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			):
    # And perform gradient accumulation
    with accelerator.accumulate(lowerCAmelCase			):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device			)
     __UpperCAmelCase         =					model(**lowerCAmelCase			)
     __UpperCAmelCase         =					outputs.loss
     accelerator.backward(lowerCAmelCase			)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

   model.eval()
   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():
     __UpperCAmelCase         =					model(**lowerCAmelCase			)
    __UpperCAmelCase         =					outputs.logits.argmax(dim=-1			)
    __UpperCAmelCase  ,			__UpperCAmelCase         =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
    metric.add_batch(
        predictions=lowerCAmelCase      ,  references=lowerCAmelCase      ,  )

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()



def 						__snake_case (       ):
 __UpperCAmelCase         =					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.'			)
 __UpperCAmelCase         =					parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 __UpperCAmelCase         =					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
 training_function(lowerCAmelCase      ,  lowerCAmelCase			)


if __name__ == "__main__":
     main()




'''simple docstring'''




from typing import TYPE_CHECKING

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


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



