

"""simple docstring"""



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case         =					"""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

__snake_case         =					"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

__snake_case         =					"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION			)
class _lowerCAmelCase       (   datasets.Metric			):







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION		,			citation=_CITATION		,			inputs_description=_KWARGS_DESCRIPTION		,			features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("string"		,			id="token"		)		,			id="sequence"		),
                   "references": datasets.Sequence(
                       datasets.Sequence(datasets.Value("string"		,			id="token"		)		,			id="sequence"		)		,			id="references"		),
               }		)		,			codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]		,			reference_urls=[
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=4		,			UpperCamelCase__=False		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					compute_bleu(
           reference_corpus=UpperCamelCase__		,			translation_corpus=UpperCamelCase__		,			max_order=UpperCamelCase__		,			smooth=UpperCamelCase__		)
       (snake_case)					:   Optional[Any]         =					score
       return {
           "bleu": bleu,
           "precisions": precisions,
           "brevity_penalty": bp,
           "length_ratio": ratio,
           "translation_length": translation_length,
           "reference_length": reference_length,
       }









"""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
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
#
########################################################################


__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Any:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   Optional[int]         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						int				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowercase		,							max_length=lowercase				)
        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					:   Tuple         =					datasets.map(
            lowercase		,							batched=lowercase		,							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					:   Optional[Any]         =					tokenized_datasets.rename_column("label"		,							"labels"				)

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

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

    # Instantiate dataloaders.
    snake_case					:   Dict         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   str         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Any				)			->		int:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Optional[Any]         =					2
    # New Code #
    snake_case					:   Tuple         =					int(args.gradient_accumulation_steps				)
    snake_case					:   Any         =					int(args.local_sgd_steps				)
    # Initialize accelerator
    snake_case					:   Optional[int]         =					Accelerator(
        cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=lowercase				)
    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
    snake_case					:   List[str]         =					config["lr"]
    snake_case					:   Tuple         =					int(config["num_epochs"]				)
    snake_case					:   List[str]         =					int(config["seed"]				)
    snake_case					:   Optional[int]         =					int(config["batch_size"]				)

    snake_case					:   Optional[int]         =					evaluate.load("glue"		,							"mrpc"				)

    set_seed(lowercase				)
    snake_case      ,snake_case					:   Optional[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   Optional[Any]         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

    # 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					:   Optional[int]         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Any         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * 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					:   int         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        with LocalSGD(
            accelerator=lowercase		,							model=lowercase		,							local_sgd_steps=lowercase		,							enabled=local_sgd_steps is not None				) as local_sgd:
            for step, batch in enumerate(lowercase				):
                # 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(lowercase				):
                    snake_case					:   Optional[int]         =					model(**lowercase				)
                    snake_case					:   List[Any]         =					output.loss
                    accelerator.backward(lowercase				)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        model.eval()
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                snake_case					:   List[str]         =					model(**lowercase				)
            snake_case					:   str         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   Dict         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

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







def 						__lowerCAmelCase		(		)			->		int:


    """simple docstring"""





    snake_case					:   Optional[int]         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							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=lowercase		,							default=1		,							help="The number of minibatches to be ran before gradients are accumulated."		,							)
    parser.add_argument(
        "--local_sgd_steps"		,							type=lowercase		,							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."				)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[Any]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





