






import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

_a    :   List[str]        =			logging.getLogger(__name__)







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    field(
          default='''tab_fact'''				,							metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
      A          =    field(
          default='''tab_fact'''				,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				,							)
      A          =    field(
          default=10_24				,							metadata={
              '''help''': (
                  '''The maximum total input sequence length after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}		)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={
              '''help''': (
                  '''Whether to pad all samples to `max_seq_length`. '''
                  '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
              )
          }				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
                  '''value if set.'''
              )
          }				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''A csv or a json file containing the training data.'''}		)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''A csv or a json file containing the validation data.'''}		)
      A          =    field(default=lowerCAmelCase__				,							metadata={'''help''': '''A csv or a json file containing the test data.'''}		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          if self.dataset_name is not None:
              pass
          elif self.train_file is None or self.validation_file is None:
              raise ValueError("Need either a GLUE task, a training/validation file or a dataset name."		)
          else:
              lowerCAmelCase__   :List[Any]      							=				self.train_file.split("."		)[-1]
              assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
              lowerCAmelCase__   :Optional[int]      							=				self.validation_file.split("."		)[-1]
              assert (
                  validation_extension == train_extension
              ), "`validation_file` should have the same extension (csv or json) as `train_file`."







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}				,							)
      A          =    field(
          default='''main'''				,							metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}				,							)
      A          =    field(
          default=lowerCAmelCase__				,							metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }				,							)





def 				snake_case__							(							):
    lowerCAmelCase__   :str      							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
    if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
    else:
        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :int      							=				parser.parse_args_into_dataclasses()

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,			datefmt="%m/%d/%Y %H:%M:%S"    ,			handlers=[logging.StreamHandler(sys.stdout       )]    ,			)

    lowerCAmelCase__   :Optional[Any]      							=				training_args.get_process_log_level()
    logger.setLevel(_A       )
    datasets.utils.logging.set_verbosity(_A       )
    transformers.utils.logging.set_verbosity(_A       )
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
        + F'''distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'''       )
    logger.info(F'''Training/evaluation parameters {training_args}'''       )

    # Detecting last checkpoint.
    lowerCAmelCase__   :Union[str, Any]      							=				None
    if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
        lowerCAmelCase__   :Union[str, Any]      							=				get_last_checkpoint(training_args.output_dir       )
        if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                "Use --overwrite_output_dir to overcome."       )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."       )

    # Set seed before initializing model.
    set_seed(training_args.seed       )

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        lowerCAmelCase__   :Union[str, Any]      							=				load_dataset(
            data_args.dataset_name    ,			data_args.dataset_config_name    ,			cache_dir=model_args.cache_dir       )
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        lowerCAmelCase__   :Dict      							=				{"train": data_args.train_file, "validation": data_args.validation_file}

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                lowerCAmelCase__   :List[str]      							=				data_args.train_file.split("."       )[-1]
                lowerCAmelCase__   :Dict      							=				data_args.test_file.split("."       )[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                lowerCAmelCase__   :List[Any]      							=				data_args.test_file
            else:
                raise ValueError("Need either a GLUE task or a test file for `do_predict`."       )

        for key in data_files.keys():
            logger.info(F'''load a local file for {key}: {data_files[key]}'''       )

        if data_args.train_file.endswith(".csv"       ):
            # Loading a dataset from local csv files
            lowerCAmelCase__   :Optional[int]      							=				load_dataset("csv"    ,			data_files=_A    ,			cache_dir=model_args.cache_dir       )
        else:
            # Loading a dataset from local json files
            lowerCAmelCase__   :int      							=				load_dataset("json"    ,			data_files=_A    ,			cache_dir=model_args.cache_dir       )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    lowerCAmelCase__   :List[str]      							=				raw_datasets["train"].features["label"].names
    lowerCAmelCase__   :Optional[int]      							=				len(_A       )

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    lowerCAmelCase__   :int      							=				AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,			num_labels=_A    ,			cache_dir=model_args.cache_dir    ,			revision=model_args.model_revision    ,			use_auth_token=True if model_args.use_auth_token else None    ,			)
    # load tapex tokenizer
    lowerCAmelCase__   :Dict      							=				TapexTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			use_fast=model_args.use_fast_tokenizer    ,			revision=model_args.model_revision    ,			use_auth_token=True if model_args.use_auth_token else None    ,			add_prefix_space=_A    ,			)
    lowerCAmelCase__   :Any      							=				BartForSequenceClassification.from_pretrained(
        model_args.model_name_or_path    ,			from_tf=bool(".ckpt" in model_args.model_name_or_path       )    ,			config=_A    ,			cache_dir=model_args.cache_dir    ,			revision=model_args.model_revision    ,			use_auth_token=True if model_args.use_auth_token else None    ,			)

    # Padding strategy
    if data_args.pad_to_max_length:
        lowerCAmelCase__   :str      							=				"max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        lowerCAmelCase__   :int      							=				False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    lowerCAmelCase__   :Optional[Any]      							=				{"Refused": 0, "Entailed": 1}
    lowerCAmelCase__   :Tuple      							=				{0: "Refused", 1: "Entailed"}

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
            F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''       )
    lowerCAmelCase__   :str      							=				min(data_args.max_seq_length    ,			tokenizer.model_max_length       )

    def preprocess_tabfact_function(UpperCAmelCase							:  Optional[Any]       ):
        # Tokenize the texts
        def _convert_table_text_to_pandas(UpperCAmelCase							:  Any       ):
            lowerCAmelCase__   :Dict      							=				[_table_row.split("#"       ) for _table_row in _table_text.strip("\n"       ).split("\n"       )]
            lowerCAmelCase__   :Optional[Any]      							=				pd.DataFrame.from_records(_table_content[1:]    ,			columns=_table_content[0]       )
            return _table_pd

        lowerCAmelCase__   :Optional[Any]      							=				examples["statement"]
        lowerCAmelCase__   :Tuple      							=				list(map(_convert_table_text_to_pandas    ,			examples["table_text"]       )       )
        lowerCAmelCase__   :int      							=				tokenizer(_A    ,			_A    ,			padding=_A    ,			max_length=_A    ,			truncation=_A       )

        lowerCAmelCase__   :Any      							=				examples["label"]
        return result

    with training_args.main_process_first(desc="dataset map pre-processing"       ):
        lowerCAmelCase__   :Tuple      							=				raw_datasets.map(
            _A    ,			batched=_A    ,			load_from_cache_file=not data_args.overwrite_cache    ,			desc="Running tokenizer on dataset"    ,			)
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset"       )
        lowerCAmelCase__   :str      							=				raw_datasets["train"]
        if data_args.max_train_samples is not None:
            lowerCAmelCase__   :List[Any]      							=				train_dataset.select(range(data_args.max_train_samples       )       )

    if training_args.do_eval:
        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset"       )
        lowerCAmelCase__   :str      							=				raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            lowerCAmelCase__   :int      							=				eval_dataset.select(range(data_args.max_eval_samples       )       )

    if training_args.do_predict or data_args.test_file is not None:
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset"       )
        lowerCAmelCase__   :int      							=				raw_datasets["test"]
        if data_args.max_predict_samples is not None:
            lowerCAmelCase__   :Tuple      							=				predict_dataset.select(range(data_args.max_predict_samples       )       )

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(_A       )       )    ,			3       ):
            logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''       )

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(UpperCAmelCase							:  EvalPrediction       ):
        lowerCAmelCase__   :str      							=				p.predictions[0] if isinstance(p.predictions    ,			_A       ) else p.predictions
        lowerCAmelCase__   :Union[str, Any]      							=				np.argmax(_A    ,			axis=1       )
        return {"accuracy": (preds == p.label_ids).astype(np.floataa       ).mean().item()}

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        lowerCAmelCase__   :int      							=				default_data_collator
    elif training_args.fpaa:
        lowerCAmelCase__   :Optional[int]      							=				DataCollatorWithPadding(_A    ,			pad_to_multiple_of=8       )
    else:
        lowerCAmelCase__   :str      							=				None

    # Initialize our Trainer
    lowerCAmelCase__   :Any      							=				Trainer(
        model=_A    ,			args=_A    ,			train_dataset=train_dataset if training_args.do_train else None    ,			eval_dataset=eval_dataset if training_args.do_eval else None    ,			compute_metrics=_A    ,			tokenizer=_A    ,			data_collator=_A    ,			)

    # Training
    if training_args.do_train:
        lowerCAmelCase__   :List[str]      							=				None
        if training_args.resume_from_checkpoint is not None:
            lowerCAmelCase__   :Dict      							=				training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            lowerCAmelCase__   :Optional[Any]      							=				last_checkpoint
        lowerCAmelCase__   :Optional[int]      							=				trainer.train(resume_from_checkpoint=_A       )
        lowerCAmelCase__   :Optional[Any]      							=				train_result.metrics
        lowerCAmelCase__   :Union[str, Any]      							=				(
            data_args.max_train_samples if data_args.max_train_samples is not None else len(_A       )
        )
        lowerCAmelCase__   :Tuple      							=				min(_A    ,			len(_A       )       )

        trainer.save_model()  # Saves the tokenizer too for easy upload

        trainer.log_metrics("train"    ,			_A       )
        trainer.save_metrics("train"    ,			_A       )
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***"       )

        lowerCAmelCase__   :Optional[Any]      							=				trainer.evaluate(eval_dataset=_A       )
        lowerCAmelCase__   :str      							=				data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A       )
        lowerCAmelCase__   :str      							=				min(_A    ,			len(_A       )       )

        trainer.log_metrics("eval"    ,			_A       )
        trainer.save_metrics("eval"    ,			_A       )

    if training_args.do_predict:
        logger.info("*** Predict ***"       )

        # Removing the `label` columns because it contains -1 and Trainer won't like that.
        lowerCAmelCase__   :List[str]      							=				predict_dataset.remove_columns("label"       )
        lowerCAmelCase__   :int      							=				trainer.predict(_A    ,			metric_key_prefix="predict"       ).predictions
        lowerCAmelCase__   :Optional[Any]      							=				np.argmax(_A    ,			axis=1       )

        lowerCAmelCase__   :List[Any]      							=				os.path.join(training_args.output_dir    ,			"predict_results_tabfact.txt"       )
        if trainer.is_world_process_zero():
            with open(_A    ,			"w"       ) as writer:
                logger.info("***** Predict Results *****"       )
                writer.write("index\tprediction\n"       )
                for index, item in enumerate(_A       ):
                    lowerCAmelCase__   :Dict      							=				label_list[item]
                    writer.write(F'''{index}\t{item}\n'''       )

    lowerCAmelCase__   :Union[str, Any]      							=				{"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

    if training_args.push_to_hub:
        trainer.push_to_hub(**_A       )
    else:
        trainer.create_model_card(**_A       )


def 				snake_case__							(							UpperCAmelCase							:  List[str]       ):
    main()


if __name__ == "__main__":
 main()











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
#
########################################################################


_a    :   List[Any]        =			16
_a    :   Tuple        =			32


def 				snake_case__							(							UpperCAmelCase							:  Accelerator    ,			UpperCAmelCase							:  int = 1_6       ):
    lowerCAmelCase__   :Tuple      							=				AutoTokenizer.from_pretrained("bert-base-cased"       )
    lowerCAmelCase__   :Any      							=				load_dataset("glue"    ,			"mrpc"       )

    def tokenize_function(UpperCAmelCase							:  Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__   :Any      							=				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
    # starting with the main process first:
    with accelerator.main_process_first():
        lowerCAmelCase__   :Any      							=				datasets.map(
            UpperCAmelCase    ,			batched=UpperCAmelCase    ,			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__   :List[str]      							=				tokenized_datasets.rename_column("label"    ,			"labels"       )

    def collate_fn(UpperCAmelCase							:  Any       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase__   :Optional[int]      							=				1_2_8 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__   :int      							=				1_6
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__   :Optional[int]      							=				8
        else:
            lowerCAmelCase__   :List[Any]      							=				None

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

    # Instantiate dataloaders.
    lowerCAmelCase__   :Dict      							=				DataLoader(
        tokenized_datasets["train"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				DataLoader(
        tokenized_datasets["validation"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )

    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

 _a    :   List[Any]        =			mocked_dataloaders  # noqa: F811


def 				snake_case__							(							UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  str       ):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,			UpperCAmelCase       ) == "1":
        lowerCAmelCase__   :Tuple      							=				2
    # New Code #
    lowerCAmelCase__   :Tuple      							=				int(args.gradient_accumulation_steps       )
    lowerCAmelCase__   :str      							=				int(args.local_sgd_steps       )
    # Initialize accelerator
    lowerCAmelCase__   :Optional[Any]      							=				Accelerator(
        cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			gradient_accumulation_steps=UpperCAmelCase       )
    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__   :Union[str, Any]      							=				config["lr"]
    lowerCAmelCase__   :int      							=				int(config["num_epochs"]       )
    lowerCAmelCase__   :List[Any]      							=				int(config["seed"]       )
    lowerCAmelCase__   :int      							=				int(config["batch_size"]       )

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

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

    # 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__   :Any      							=				model.to(accelerator.device       )

    # Instantiate optimizer
    lowerCAmelCase__   :Optional[int]      							=				AdamW(params=model.parameters()    ,			lr=UpperCAmelCase       )

    # Instantiate scheduler
    lowerCAmelCase__   :List[Any]      							=				get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase    ,			num_warmup_steps=1_0_0    ,			num_training_steps=(len(UpperCAmelCase       ) * 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__   :Tuple      							=				accelerator.prepare(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )

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

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

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


def 				snake_case__							(							):
    lowerCAmelCase__   :Union[str, Any]      							=				argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision"    ,			type=UpperCAmelCase    ,			default=UpperCAmelCase    ,			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=UpperCAmelCase    ,			default=1    ,			help="The number of minibatches to be ran before gradients are accumulated."    ,			)
    parser.add_argument(
        "--local_sgd_steps"    ,			type=UpperCAmelCase    ,			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__   :Tuple      							=				parser.parse_args()
    lowerCAmelCase__   :Any      							=				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(UpperCAmelCase    ,			UpperCAmelCase       )


if __name__ == "__main__":
 main()



