"""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


__SCREAMING_SNAKE_CASE								=16
__SCREAMING_SNAKE_CASE								=32



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Accelerator  ,    __SCREAMING_SNAKE_CASE							:  int = 16  ,    __SCREAMING_SNAKE_CASE							:  str = "bert-base-cased"   ):
       lowercase_			:       Tuple									=			AutoTokenizer.from_pretrained(UpperCamelCase__   )
       lowercase_			:       Dict									=			load_dataset('glue'  ,    'mrpc'   )

       def tokenize_function(__SCREAMING_SNAKE_CASE							:  int   ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase_			:       Tuple									=			tokenizer(examples['sentence1']  ,    examples['sentence2']  ,    truncation=UpperCamelCase__  ,    max_length=UpperCamelCase__   )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       lowercase_			:       List[Any]									=			datasets.map(
           UpperCamelCase__  ,    batched=UpperCamelCase__  ,    remove_columns=['idx', 'sentence1', 'sentence2']  ,    load_from_cache_file=UpperCamelCase__   )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase_			:       Union[str, Any]									=			tokenized_datasets.rename_column('label'  ,    'labels'   )

       def collate_fn(__SCREAMING_SNAKE_CASE							:  Dict   ):
              # 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(UpperCamelCase__  ,    padding='max_length'  ,    max_length=1_28  ,    return_tensors='pt'   )
              return tokenizer.pad(UpperCamelCase__  ,    padding='longest'  ,    return_tensors='pt'   )

       # Instantiate dataloaders.
       lowercase_			:       List[str]									=			DataLoader(
           tokenized_datasets['train']  ,    shuffle=UpperCamelCase__  ,    collate_fn=UpperCamelCase__  ,    batch_size=UpperCamelCase__   )
       lowercase_			:       List[Any]									=			DataLoader(
           tokenized_datasets['validation']  ,    shuffle=UpperCamelCase__  ,    collate_fn=UpperCamelCase__  ,    batch_size=UpperCamelCase__   )

       return train_dataloader, eval_dataloader



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
       model.eval()
       lowercase_			:       Union[str, Any]									=			0
       for step, batch in enumerate(UpperCamelCase__   ):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device   )
              with torch.no_grad():
                     lowercase_			:       int									=			model(**UpperCamelCase__   )
              lowercase_			:       List[Any]									=			outputs.logits.argmax(dim=-1   )
              # It is slightly faster to call this once, than multiple times
              lowercase_			:       List[str]									=			accelerator.gather(
                  (predictions, batch['labels'])   )  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(UpperCamelCase__   ) - 1:
                            lowercase_			:       Tuple									=			predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                            lowercase_			:       str									=			references[: len(eval_dataloader.dataset   ) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=UpperCamelCase__  ,    references=UpperCamelCase__  ,    )

       lowercase_			:       Tuple									=			metric.compute()
       return eval_metric["accuracy"]



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]   ):
       # Initialize accelerator
       lowercase_			:       str									=			Accelerator()

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

       set_seed(UpperCamelCase__   )
       lowercase_			:       List[Any]									=			get_dataloaders(UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__   )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase_			:       Union[str, Any]									=			AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__  ,    return_dict=UpperCamelCase__   )

       # Instantiate optimizer
       lowercase_			:       Optional[int]									=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase_			:       Dict									=			optimizer_cls(params=model.parameters()  ,    lr=UpperCamelCase__   )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase_			:       Tuple									=			accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              lowercase_			:       Any									=			1
       lowercase_			:       List[str]									=			(len(UpperCamelCase__   ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              lowercase_			:       Optional[Any]									=			get_linear_schedule_with_warmup(
                  optimizer=UpperCamelCase__  ,    num_warmup_steps=0  ,    num_training_steps=UpperCamelCase__  ,    )
       else:
              lowercase_			:       str									=			DummyScheduler(UpperCamelCase__  ,    total_num_steps=UpperCamelCase__  ,    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.
       lowercase_			:       Tuple									=			accelerator.prepare(
           UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__   )

       # We need to keep track of how many total steps we have iterated over
       lowercase_			:       Optional[int]									=			0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase_			:       int									=			0
       lowercase_			:       int									=			evaluate.load('glue'  ,    'mrpc'   )
       lowercase_			:       int									=			num_epochs

       if args.partial_train_epoch is not None:
              lowercase_			:       Dict									=			args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint   )
              lowercase_			:       str									=			args.resume_from_checkpoint.split('epoch_'   )[1]
              lowercase_			:       Optional[int]									=			""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              lowercase_			:       List[str]									=			int(UpperCamelCase__   ) + 1
              lowercase_			:       Any									=			evaluation_loop(UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__   )
              accelerator.print('resumed checkpoint performance:'  ,    UpperCamelCase__   )
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:'  ,    lr_scheduler.get_lr()[0]   )
              accelerator.print('resumed optimizers\'s lr:'  ,    optimizer.param_groups[0]['lr']   )
              with open(os.path.join(args.output_dir  ,    F'''state_{starting_epoch-1}.json'''   )  ,    'r'   ) as f:
                     lowercase_			:       Dict									=			json.load(UpperCamelCase__   )
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       lowercase_			:       int									=			{}
       for epoch in range(UpperCamelCase__  ,    UpperCamelCase__   ):
              model.train()
              for step, batch in enumerate(UpperCamelCase__   ):
                     lowercase_			:       Tuple									=			model(**UpperCamelCase__   )
                     lowercase_			:       List[str]									=			outputs.loss
                     lowercase_			:       List[str]									=			loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase__   )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              lowercase_			:       List[Any]									=			F'''epoch_{epoch}'''
              lowercase_			:       Dict									=			os.path.join(args.output_dir  ,    UpperCamelCase__   )
              accelerator.save_state(UpperCamelCase__   )
              lowercase_			:       List[Any]									=			evaluation_loop(UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__   )
              lowercase_			:       Any									=			accuracy
              lowercase_			:       Optional[Any]									=			lr_scheduler.get_lr()[0]
              lowercase_			:       Union[str, Any]									=			optimizer.param_groups[0]["""lr"""]
              lowercase_			:       Tuple									=			epoch
              lowercase_			:       Optional[Any]									=			overall_step
              accelerator.print(F'''epoch {epoch}:'''  ,    UpperCamelCase__   )

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir  ,    F'''state_{epoch}.json'''   )  ,    'w'   ) as f:
                            json.dump(UpperCamelCase__  ,    UpperCamelCase__   )



def     lowercase__( ):
       lowercase_			:       str									=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
       parser.add_argument(
           '--model_name_or_path'  ,    type=UpperCamelCase__  ,    default='bert-base-cased'  ,    help='Path to pretrained model or model identifier from huggingface.co/models.'  ,    required=UpperCamelCase__  ,    )
       parser.add_argument(
           '--output_dir'  ,    type=UpperCamelCase__  ,    default='.'  ,    help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,    )
       parser.add_argument(
           '--resume_from_checkpoint'  ,    type=UpperCamelCase__  ,    default=UpperCamelCase__  ,    help='If the training should continue from a checkpoint folder.'  ,    )
       parser.add_argument(
           '--partial_train_epoch'  ,    type=UpperCamelCase__  ,    default=UpperCamelCase__  ,    help='If passed, the training will stop after this number of epochs.'  ,    )
       parser.add_argument(
           '--num_epochs'  ,    type=UpperCamelCase__  ,    default=2  ,    help='Number of train epochs.'  ,    )
       lowercase_			:       Optional[int]									=			parser.parse_args()
       lowercase_			:       str									=			{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

       training_function(UpperCamelCase__  ,    UpperCamelCase__   )


if __name__ == "__main__":
       main()


"""simple docstring"""







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__SCREAMING_SNAKE_CASE								="2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
       raise ImportWarning(
           "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
       )

if version.parse(pyarrow.__version__).major < 8:
       raise ImportWarning(
           "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
           "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
       )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

