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

lowerCAmelCase_			:			List[str]			   =     32







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ = 16       ):
     _UpperCAmelCase					:  Optional[int]         =    AutoTokenizer.from_pretrained("""bert-base-cased"""       )
     _UpperCAmelCase					:  str         =    load_dataset("""glue""" ,       """mrpc"""       )

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

     def collate_fn(lowerCAmelCase_       ):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               _UpperCAmelCase					:  str         =    16
          elif accelerator.mixed_precision != "no":
               _UpperCAmelCase					:  Tuple         =    8
          else:
               _UpperCAmelCase					:  Dict         =    None

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

     # Instantiate dataloaders.
     _UpperCAmelCase					:  List[Any]         =    DataLoader(
         tokenized_datasets["""train"""] ,       shuffle=lowerCAmelCase_ ,       collate_fn=lowerCAmelCase_ ,       batch_size=lowerCAmelCase_       )
     _UpperCAmelCase					:  List[Any]         =    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

   lowerCAmelCase_			:			int			   =     mocked_dataloaders  # noqa: F811







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,       lowerCAmelCase_       ) == "1":
          _UpperCAmelCase					:  List[str]         =    2
     # Initialize accelerator
     _UpperCAmelCase					:  str         =    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					:  Any         =    int(config["""num_epochs"""]       )
     _UpperCAmelCase					:  Union[str, Any]         =    int(config["""seed"""]       )
     _UpperCAmelCase					:  List[str]         =    int(config["""batch_size"""]       )

     _UpperCAmelCase					:  str         =    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_       ):
          # 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					:  Optional[Any]         =    observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          _UpperCAmelCase					:  Union[str, Any]         =    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					:  Dict         =    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					:  Optional[int]         =    model.to(accelerator.device       )

          # Instantiate optimizer
          _UpperCAmelCase					:  Union[str, Any]         =    AdamW(params=model.parameters() ,       lr=lowerCAmelCase_       )
          _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    get_dataloaders(lowerCAmelCase_ ,       lowerCAmelCase_       )

          # Instantiate scheduler
          _UpperCAmelCase					:  int         =    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					:  int         =    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					:  Any         =    model(**lowerCAmelCase_       )
                         _UpperCAmelCase					:  Optional[int]         =    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					:  Dict         =    model(**lowerCAmelCase_       )
                    _UpperCAmelCase					:  Any         =    outputs.logits.argmax(dim=-1       )
                    _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
                    metric.add_batch(
                        predictions=lowerCAmelCase_ ,       references=lowerCAmelCase_ ,       )

               _UpperCAmelCase					:  List[str]         =    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 __A  (    ):
     _UpperCAmelCase					:  Optional[int]         =    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					:  List[Any]         =    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					:  List[Any]         =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
     training_function(lowerCAmelCase_ ,       lowerCAmelCase_       )


if __name__ == "__main__":
   main()




'''simple docstring'''

import io
import json
import subprocess


lowerCAmelCase_			:			Tuple			   =     [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

lowerCAmelCase_			:			str			   =     8







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Union[str, Any]         =    {}
     for src, tgt in pairs:
          _UpperCAmelCase					:  Union[str, Any]         =    f"{src}-{tgt}"
          _UpperCAmelCase					:  List[Any]         =    f"sacrebleu -t wmt19 -l {pair} --echo src".split()
          _UpperCAmelCase					:  List[str]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  str         =    f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
          _UpperCAmelCase					:  List[Any]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  int         =    {"""src""": src_lines[:n_objs], """tgt""": tgt_lines[:n_objs]}
     return text


lowerCAmelCase_			:			Optional[int]			   =     get_all_data(pairs, n_objs)
lowerCAmelCase_			:			str			   =     '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   lowerCAmelCase_			:			Tuple			   =     json.dump(text, f, indent=2, ensure_ascii=False)


