




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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								=	16
UpperCamelCase								=	32



def 				_A  (		lowerCAmelCase_	:   Accelerator  ,							lowerCAmelCase_	:   int = 16     ):
     """simple docstring"""

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

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

     def collate_fn(lowerCAmelCase_	:   Optional[int]     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowerCAmelCase__            =					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":
               lowerCAmelCase__            =					16
          elif accelerator.mixed_precision != "no":
               lowerCAmelCase__            =					8
          else:
               lowerCAmelCase__            =					None

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

     # Instantiate dataloaders.
     lowerCAmelCase__            =					DataLoader(
         tokenized_datasets["train"]  ,							shuffle=lowerCAmelCase_  ,							collate_fn=lowerCAmelCase_  ,							batch_size=lowerCAmelCase_     )
     lowerCAmelCase__            =					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								=	mocked_dataloaders  # noqa: F811



def 				_A  (		lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   Dict     ):
     """simple docstring"""

     if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,							lowerCAmelCase_     ) == "1":
          lowerCAmelCase__            =					2
     # New Code #
     lowerCAmelCase__            =					int(args.gradient_accumulation_steps     )
     lowerCAmelCase__            =					int(args.local_sgd_steps     )
     # Initialize accelerator
     lowerCAmelCase__            =					Accelerator(
         cpu=args.cpu  ,							mixed_precision=args.mixed_precision  ,							gradient_accumulation_steps=lowerCAmelCase_     )
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"     )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase__            =					config["lr"]
     lowerCAmelCase__            =					int(config["num_epochs"]     )
     lowerCAmelCase__            =					int(config["seed"]     )
     lowerCAmelCase__            =					int(config["batch_size"]     )

     lowerCAmelCase__            =					evaluate.load("glue"  ,							"mrpc"     )

     set_seed(lowerCAmelCase_     )
     lowerCAmelCase__      ,						lowerCAmelCase__            =					get_dataloaders(lowerCAmelCase_  ,							lowerCAmelCase_     )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase__            =					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).
     lowerCAmelCase__            =					model.to(accelerator.device     )

     # Instantiate optimizer
     lowerCAmelCase__            =					AdamW(params=model.parameters()  ,							lr=lowerCAmelCase_     )

     # Instantiate scheduler
     lowerCAmelCase__            =					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.
     lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					accelerator.prepare(
         lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

     # Now we train the model
     for epoch in range(lowerCAmelCase_     ):
          model.train()
          with LocalSGD(
              accelerator=lowerCAmelCase_  ,							model=lowerCAmelCase_  ,							local_sgd_steps=lowerCAmelCase_  ,							enabled=local_sgd_steps is not None     ) as local_sgd:
               for step, batch in enumerate(lowerCAmelCase_     ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device     )
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(lowerCAmelCase_     ):
                         lowerCAmelCase__            =					model(**lowerCAmelCase_     )
                         lowerCAmelCase__            =					output.loss
                         accelerator.backward(lowerCAmelCase_     )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          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():
                    lowerCAmelCase__            =					model(**lowerCAmelCase_     )
               lowerCAmelCase__            =					outputs.logits.argmax(dim=-1     )
               lowerCAmelCase__      ,						lowerCAmelCase__            =					accelerator.gather_for_metrics((predictions, batch["labels"])     )
               metric.add_batch(
                   predictions=lowerCAmelCase_  ,							references=lowerCAmelCase_  ,							)

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



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					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."  ,							)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"  ,							type=lowerCAmelCase_  ,							default=1  ,							help="The number of minibatches to be ran before gradients are accumulated."  ,							)
     parser.add_argument(
         "--local_sgd_steps"  ,							type=lowerCAmelCase_  ,							default=8  ,							help="Number of local SGD steps or None to disable local SGD"     )
     parser.add_argument("--cpu"  ,							action="store_true"  ,							help="If passed, will train on the CPU."     )
     lowerCAmelCase__            =					parser.parse_args()
     lowerCAmelCase__            =					{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCAmelCase_  ,							lowerCAmelCase_     )


if __name__ == "__main__":
     main()







import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A							:			List[Any]         =			"http://www.mocksite.com/file1.txt"
__A							:			List[Any]         =			"\"text\": [\"foo\", \"foo\"]"
__A							:			Dict         =			"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"







class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			200
     __magic_name__   =			{'Content-Length': '100'}
     __magic_name__   =			{}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return [bytes(snake_case_				,     'utf-8'							)]





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return MockResponse()





@pytest.mark.parametrize('urls_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    import requests

    monkeypatch.setattr(_SCREAMING_SNAKE_CASE							,     'request'							,     _SCREAMING_SNAKE_CASE				)

    _A     =							URL
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							url
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[url]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': url}
    _A     =							'dummy'
    _A     =							'downloads'
    _A     =							tmp_path
    _A     =							DownloadConfig(
        cache_dir=os.path.join(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.download(_SCREAMING_SNAKE_CASE				)
    _A     =							urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[downloaded_paths]
            _A     =							[urls]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in downloaded_paths.keys()
            _A     =							downloaded_paths.values()
            _A     =							urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            _A     =							downloaded_path.read_text()
            assert content == CONTENT
            _A     =							downloaded_path.with_suffix('.json'				)
            assert metadata_downloaded_path.exists()
            _A     =							json.loads(metadata_downloaded_path.read_text()				)
            assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize('paths_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				)
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							filename
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[filename]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': filename}
    _A     =							'dummy'
    _A     =							xz_file.parent
    _A     =							'extracted'
    _A     =							DownloadConfig(
        cache_dir=_SCREAMING_SNAKE_CASE							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.extract(_SCREAMING_SNAKE_CASE				)
    _A     =							paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[extracted_paths]
            _A     =							[paths]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in extracted_paths.keys()
            _A     =							extracted_paths.values()
            _A     =							paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_SCREAMING_SNAKE_CASE							,     etag=_SCREAMING_SNAKE_CASE				)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            _A     =							extracted_path.read_text()
            _A     =							text_file.read_text()
            assert extracted_file_content == expected_file_content





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    assert path.endswith('.jsonl'				)
    for num_items, line in enumerate(_SCREAMING_SNAKE_CASE							,     start=1				):
        _A     =							json.loads(line.decode('utf-8'				)				)
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4





@pytest.mark.parametrize('archive_jsonl'							,     ['tar_jsonl_path', 'zip_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl'							,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
            _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_tar == 1
    assert num_jsonl == 2





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        assert os.path.basename(_SCREAMING_SNAKE_CASE				) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2





