


import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					int		,						_lowercase      :					Union[str, Any]=0.0_1		,						_lowercase      :					Tuple=10_00 ):




         """simple docstring"""







         UpperCAmelCase__										=     p_stop
         UpperCAmelCase__										=     max_length






  def __iter__(					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     0
         UpperCAmelCase__										=     False
         while not stop and count < self.max_length:
                yield count
                count += 1
                UpperCAmelCase__										=     random.random() < self.p_stop




class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					List[str]		,						_lowercase      :					int		,						_lowercase      :					List[str]=False		,						_lowercase      :					List[Any]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             BatchSamplerShard(_lowercase		,						2		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )
             for i in range(2 )
         ]
         UpperCAmelCase__										=     [list(_lowercase ) for batch_sampler_shard in batch_sampler_shards]
         if not split_batches:
                self.assertListEqual([len(_lowercase ) for shard in batch_sampler_shards]		,						[len(_lowercase ) for e in expected] )
         self.assertListEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=3		,						drop_last=_lowercase )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         UpperCAmelCase__										=     BatchSampler(range(20 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(20 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         # Check the shards when the dataset is very small.
         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=4		,						drop_last=_lowercase )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size.
         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         # Check the shards when the dataset is very small.
         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=3		,						drop_last=_lowercase )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         UpperCAmelCase__										=     BatchSampler(range(20 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(20 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is very small.
         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[[0, 1]], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=3		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						even_batches=_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(24 )		,						batch_size=4		,						drop_last=_lowercase )
         # Expected shouldn't change
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size.
         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(22 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(21 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         # Check the shards when the dataset is very small.
         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[[0, 1]], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )

         UpperCAmelCase__										=     BatchSampler(range(2 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     [[], []]
         self.check_batch_sampler_shards(_lowercase		,						_lowercase		,						split_batches=_lowercase		,						even_batches=_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         UpperCAmelCase__										=     [BatchSamplerShard(_lowercase		,						2		,						_lowercase		,						even_batches=_lowercase ) for i in range(2 )]

         self.assertEqual(len(batch_sampler_shards[0] )		,						3 )
         self.assertEqual(len(batch_sampler_shards[1] )		,						2 )

         self.assertListEqual(list(batch_sampler_shards[0] )		,						[[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
         self.assertListEqual(list(batch_sampler_shards[1] )		,						[[3, 4], [9, 10, 11]] )






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					str		,						_lowercase      :					Dict		,						_lowercase      :					List[str]=False		,						_lowercase      :					Tuple=2		,						_lowercase      :					Dict=False ):




         """simple docstring"""







         random.seed(_lowercase )
         UpperCAmelCase__										=     list(_lowercase )

         UpperCAmelCase__										=     [
             IterableDatasetShard(
                 _lowercase		,						batch_size=_lowercase		,						drop_last=_lowercase		,						num_processes=_lowercase		,						process_index=_lowercase		,						split_batches=_lowercase		,						)
             for i in range(_lowercase )
         ]
         UpperCAmelCase__										=     []
         for iterable_dataset_shard in iterable_dataset_shards:
                # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
                random.seed(_lowercase )
                iterable_dataset_lists.append(list(_lowercase ) )

         UpperCAmelCase__										=     batch_size // num_processes if split_batches else batch_size
         # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
         UpperCAmelCase__										=     iterable_dataset_lists[0]
         for l in iterable_dataset_lists[1:]:
                self.assertEqual(len(_lowercase )		,						len(_lowercase ) )
                self.assertTrue(len(_lowercase ) % shard_batch_size == 0 )

         UpperCAmelCase__										=     []
         for idx in range(0		,						len(_lowercase )		,						_lowercase ):
                for l in iterable_dataset_lists:
                       observed += l[idx : idx + shard_batch_size]

         if not drop_last:
                while len(_lowercase ) < len(_lowercase ):
                       reference += reference
         self.assertListEqual(_lowercase		,						reference[: len(_lowercase )] )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     42
         UpperCAmelCase__										=     RandomIterableDataset()

         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )

         # Edge case with a very small dataset
         UpperCAmelCase__										=     RandomIterableDataset(max_length=2 )

         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )
         self.check_iterable_dataset_shards(_lowercase		,						_lowercase		,						batch_size=4		,						drop_last=_lowercase		,						split_batches=_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     BatchSampler(range(16 )		,						batch_size=4		,						drop_last=_lowercase )
         UpperCAmelCase__										=     SkipBatchSampler(_lowercase		,						2 )
         self.assertListEqual(list(_lowercase )		,						[[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     SkipDataLoader(list(range(16 ) )		,						batch_size=4		,						skip_batches=2 )
         self.assertListEqual([t.tolist() for t in dataloader]		,						[[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     DataLoader(list(range(16 ) )		,						batch_size=4 )
         UpperCAmelCase__										=     skip_first_batches(_lowercase		,						num_batches=2 )
         self.assertListEqual([t.tolist() for t in new_dataloader]		,						[[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     DataLoaderShard(list(range(16 ) )		,						batch_size=4 )
         for idx, _ in enumerate(_lowercase ):
                self.assertEqual(dataloader.end_of_dataloader		,						idx == 3 )

         # Test it also works on the second iteration
         for idx, _ in enumerate(_lowercase ):
                self.assertEqual(dataloader.end_of_dataloader		,						idx == 3 )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         Accelerator()
         UpperCAmelCase__										=     DataLoaderDispatcher(range(16 )		,						batch_size=4 )
         for idx, _ in enumerate(_lowercase ):
                self.assertEqual(dataloader.end_of_dataloader		,						idx == 3 )

         # Test it also works on the second iteration
         for idx, _ in enumerate(_lowercase ):
                self.assertEqual(dataloader.end_of_dataloader		,						idx == 3 )









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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A = 1_6			)						->					Tuple:



       '''simple docstring'''





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

       def tokenize_function(__A			):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase__										=     tokenizer(examples["sentence1"] ,						examples["sentence2"] ,						truncation=__A ,						max_length=__A			)
              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():
              UpperCAmelCase__										=     datasets.map(
                  __A ,						batched=__A ,						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
       UpperCAmelCase__										=     tokenized_datasets.rename_column("label" ,						"labels"			)

       def collate_fn(__A			):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase__										=     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":
                     UpperCAmelCase__										=     1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__										=     8
              else:
                     UpperCAmelCase__										=     None

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

       # Instantiate dataloaders.
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["train"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["validation"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)

       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      = mocked_dataloaders  # noqa: F811





def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						__A			) == "1":
              UpperCAmelCase__										=     2
       # New Code #
       UpperCAmelCase__										=     int(args.gradient_accumulation_steps			)
       # Initialize accelerator
       UpperCAmelCase__										=     Accelerator(
           cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						gradient_accumulation_steps=__A			)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"			)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase__										=     config["lr"]
       UpperCAmelCase__										=     int(config["num_epochs"]			)
       UpperCAmelCase__										=     int(config["seed"]			)
       UpperCAmelCase__										=     int(config["batch_size"]			)

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

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

       # 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).
       UpperCAmelCase__										=     model.to(accelerator.device			)

       # Instantiate optimizer
       UpperCAmelCase__										=     AdamW(params=model.parameters() ,						lr=__A			)

       # Instantiate scheduler
       UpperCAmelCase__										=     get_linear_schedule_with_warmup(
           optimizer=__A ,						num_warmup_steps=1_0_0 ,						num_training_steps=(len(__A			) * 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.
       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.prepare(
           __A ,						__A ,						__A ,						__A ,						__A			)

       # Now we train the model
       for epoch in range(__A			):
              model.train()
              for step, batch in enumerate(__A			):
                     # 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(__A			):
                            UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     output.loss
                            accelerator.backward(__A			)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              UpperCAmelCase__										=     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,						__A			)





def __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument(
           "--mixed_precision" ,						type=__A ,						default=__A ,						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=__A ,						default=1 ,						help="The number of minibatches to be ran before gradients are accumulated." ,						)
       parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU."			)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()





