





'''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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_lowercase       						=			16
_lowercase       						=			32

def    __UpperCamelCase	(	a    : Accelerator					,   a    : int = 16     ) ->str:
 snake_case				=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 snake_case				=			load_dataset('''glue'''					,   '''mrpc'''     )

 def tokenize_function(a    : Union[str, Any]     ):
  # max_length=None => use the model max length (it's actually the default)
  snake_case				=			tokenizer(examples['''sentence1''']					,   examples['''sentence2''']					,   truncation=a					,   max_length=a     )
  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(
      a					,   batched=a					,   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(a    : 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(
      a					,   padding='''longest'''					,   max_length=a					,   pad_to_multiple_of=a					,   return_tensors='''pt'''					,   )

 # Instantiate dataloaders.
 snake_case				=			DataLoader(
     tokenized_datasets['''train''']					,   shuffle=a					,   collate_fn=a					,   batch_size=a     )
 snake_case				=			DataLoader(
     tokenized_datasets['''validation''']					,   shuffle=a					,   collate_fn=a					,   batch_size=a     )

 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

   _lowercase       						=			mocked_dataloaders  # noqa: F811

def    __UpperCamelCase	(	a    : List[str]					,   a    : int     ) ->int:
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,   a     ) == "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'''     )

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=a     )
 def inner_training_loop(a    : Tuple     ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(a     )

  # 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=a     )

  # 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=a     )
  snake_case				,		snake_case				=			get_dataloaders(a					,   a     )

  # Instantiate scheduler
  snake_case				=			get_linear_schedule_with_warmup(
      optimizer=a					,   num_warmup_steps=100					,   num_training_steps=(len(a     ) * 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.
  snake_case				,		snake_case				,		snake_case				,		snake_case				,		snake_case				=			accelerator.prepare(
      a					,   a					,   a					,   a					,   a     )

  # Now we train the model
  for epoch in range(a     ):
   model.train()
   for step, batch in enumerate(a     ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device     )
    snake_case				=			model(**a     )
    snake_case				=			outputs.loss
    accelerator.backward(a     )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   model.eval()
   for step, batch in enumerate(a     ):
    # 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(**a     )
    snake_case				=			outputs.logits.argmax(dim=-1     )
    snake_case				,		snake_case				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
    metric.add_batch(
        predictions=a					,   references=a					,   )

   snake_case				=			metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"""epoch {epoch}:"""					,   a     )

    # 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    __UpperCamelCase	(	) ->Any:
 snake_case				=			argparse.ArgumentParser(description='''Simple example of training script.'''     )
 parser.add_argument(
     '''--mixed_precision'''					,   type=a					,   default=a					,   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(a					,   a     )


if __name__ == "__main__":
   main()








'''simple docstring'''







import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase       						=			[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def    __UpperCamelCase	(	a    : Dict=True     ) ->str:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a					)					)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	str:
      with TemporaryDirectory() as tmp_dir:
       snake_case				=			dataset_module_factory(A__    ,   cache_dir=A__    )

       snake_case				=			import_main_class(dataset_module.module_path    ,   dataset=A__    )

       snake_case				=			builder_cls(
           cache_dir=A__    ,   config_name=A__    ,   hash=dataset_module.hash    ,   )

       snake_case				=			'''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=A__    ).replace(os.sep    ,   '''/'''    ),
               config.DATASET_INFO_FILENAME,
           ]    )
       snake_case				=			cached_path(A__    ,   cache_dir=A__    )
       self.assertTrue(os.path.exists(A__    )    )




@pytest.mark.integration
def    __UpperCamelCase	(	a    : List[str]     ) ->Any:
 snake_case				=			tmp_path_factory.mktemp('''test_hf_gcp'''     ) / '''test_wikipedia_simple'''
 snake_case				=			dataset_module_factory('''wikipedia'''					,   cache_dir=a     )
 snake_case				=			import_main_class(dataset_module.module_path     )
 snake_case				=			builder_cls(
     cache_dir=a					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 snake_case				=			None
 builder_instance.download_and_prepare()
 snake_case				=			builder_instance.as_dataset()
 assert ds






@pytest.mark.integration
def    __UpperCamelCase	(	a    : Any     ) ->Union[str, Any]:
 snake_case				=			dataset_module_factory('''wikipedia'''					,   cache_dir=a     )
 snake_case				=			import_main_class(dataset_module.module_path					,   dataset=a     )
 snake_case				=			builder_cls(
     cache_dir=a					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
 snake_case				=			builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(a					,   a     )
 assert "train" in ds
 assert isinstance(ds['''train''']					,   a     )
 assert next(iter(ds['''train''']     )     )

