


"""simple docstring"""



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case             =   16
__snake_case             =   32







def   __lowerCAmelCase						(						lowercase  :  Accelerator						,							lowercase  :  int = 16						,							lowercase  :  str = "bert-base-cased"			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   AutoTokenizer.from_pretrained(lowercase			)
    snake_case   :				Union[str, Any]				 =   load_dataset("glue"						,							"mrpc"			)

    def tokenize_function(lowercase  :  Dict			):
        # max_length=None => use the model max length (it's actually the default)
        snake_case   :				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
    snake_case   :				Any				 =   datasets.map(
        lowercase						,							batched=lowercase						,							remove_columns=["idx", "sentence1", "sentence2"]						,							load_from_cache_file=lowercase			)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case   :				int				 =   tokenized_datasets.rename_column("label"						,							"labels"			)

    def collate_fn(lowercase  :  Tuple			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(lowercase						,							padding="max_length"						,							max_length=128						,							return_tensors="pt"			)
        return tokenizer.pad(lowercase						,							padding="longest"						,							return_tensors="pt"			)

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

    return train_dataloader, eval_dataloader







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


    """simple docstring"""





    snake_case   :				Dict				 =   Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case   :				int				 =   config["lr"]
    snake_case   :				List[Any]				 =   int(config["num_epochs"]			)
    snake_case   :				Union[str, Any]				 =   int(config["seed"]			)
    snake_case   :				Union[str, Any]				 =   int(config["batch_size"]			)
    snake_case   :				int				 =   args.model_name_or_path

    set_seed(lowercase			)
    snake_case ,snake_case   :				Any				 =   get_dataloaders(lowercase						,							lowercase						,							lowercase			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case   :				Any				 =   AutoModelForSequenceClassification.from_pretrained(lowercase						,							return_dict=lowercase			)

    # Instantiate optimizer
    snake_case   :				Union[str, Any]				 =   (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case   :				Optional[Any]				 =   optimizer_cls(params=model.parameters()						,							lr=lowercase			)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case   :				Union[str, Any]				 =   accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        snake_case   :				Dict				 =   1
    snake_case   :				Optional[Any]				 =   (len(lowercase			) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case   :				List[str]				 =   get_linear_schedule_with_warmup(
            optimizer=lowercase						,							num_warmup_steps=0						,							num_training_steps=lowercase						,							)
    else:
        snake_case   :				List[Any]				 =   DummyScheduler(lowercase						,							total_num_steps=lowercase						,							warmup_num_steps=0			)

    # 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   :				Optional[int]				 =   accelerator.prepare(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase			)

    # We need to keep track of how many total steps we have iterated over
    snake_case   :				Optional[int]				 =   0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case   :				Optional[Any]				 =   0

    # Now we train the model
    snake_case   :				List[Any]				 =   evaluate.load("glue"						,							"mrpc"			)
    snake_case   :				int				 =   0
    snake_case   :				Any				 =   {}
    for epoch in range(lowercase						,							lowercase			):
        model.train()
        for step, batch in enumerate(lowercase			):
            snake_case   :				Any				 =   model(**lowercase			)
            snake_case   :				List[Any]				 =   outputs.loss
            snake_case   :				List[str]				 =   loss / gradient_accumulation_steps
            accelerator.backward(lowercase			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        snake_case   :				Optional[int]				 =   0
        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   :				Optional[Any]				 =   model(**lowercase			)
            snake_case   :				Optional[Any]				 =   outputs.logits.argmax(dim=-1			)
            # It is slightly faster to call this once, than multiple times
            snake_case ,snake_case   :				int				 =   accelerator.gather(
                (predictions, batch["labels"])			)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(lowercase			) - 1:
                    snake_case   :				Any				 =   predictions[: len(eval_dataloader.dataset			) - samples_seen]
                    snake_case   :				Optional[Any]				 =   references[: len(eval_dataloader.dataset			) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=lowercase						,							references=lowercase						,							)

        snake_case   :				Any				 =   metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'						,							lowercase			)
        snake_case   :				Optional[Any]				 =   eval_metric["accuracy"]

        if best_performance < eval_metric["accuracy"]:
            snake_case   :				str				 =   eval_metric["accuracy"]

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir						,							"all_results.json"			)						,							"w"			) as f:
            json.dump(lowercase						,							lowercase			)







def   __lowerCAmelCase						(						)     ->						Tuple:


    """simple docstring"""





    snake_case   :				List[Any]				 =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."			)
    parser.add_argument(
        "--model_name_or_path"						,							type=lowercase						,							default="bert-base-cased"						,							help="Path to pretrained model or model identifier from huggingface.co/models."						,							required=lowercase						,							)
    parser.add_argument(
        "--output_dir"						,							type=lowercase						,							default="."						,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."						,							)
    parser.add_argument(
        "--performance_lower_bound"						,							type=lowercase						,							default=lowercase						,							help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."						,							)
    parser.add_argument(
        "--num_epochs"						,							type=lowercase						,							default=3						,							help="Number of train epochs."						,							)
    snake_case   :				Optional[int]				 =   parser.parse_args()
    snake_case   :				List[Any]				 =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
    training_function(lowercase						,							lowercase			)


if __name__ == "__main__":
  main()









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





