
"""simple docstring"""

import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	GPTaTokenizer
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	GPTaTokenizerFast
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	True
      __SCREAMING_SNAKE_CASE   :							Any							      =	{"add_prefix_space": True}
      __SCREAMING_SNAKE_CASE   :							Any							      =	False




      def     a_      (							self							)						->					Optional[int]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase									=					[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          UpperCAmelCase									=					dict(zip(lowercase_   ,       range(len(lowercase_							)							)							)							)
          UpperCAmelCase									=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          UpperCAmelCase									=					{'unk_token': '<unk>'}

          UpperCAmelCase									=					os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase									=					os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['merges_file']							)
          with open(self.vocab_file   ,       'w'   ,       encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)
          with open(self.merges_file   ,       'w'   ,       encoding='utf-8'							) as fp:
              fp.write('\n'.join(lowercase_							)							)




      def     a_      (							self   ,       **lowercase_							)						->					List[str]:
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname   ,       **lowercase_							)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,       **lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					int:
          UpperCAmelCase									=					'lower newer'
          UpperCAmelCase									=					'lower newer'
          return input_text, output_text




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					GPTaTokenizer(self.vocab_file   ,       self.merges_file   ,       **self.special_tokens_map							)
          UpperCAmelCase									=					'lower newer'
          UpperCAmelCase									=					['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          UpperCAmelCase									=					tokenizer.tokenize(lowercase_   ,       add_prefix_space=lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          UpperCAmelCase									=					tokens + [tokenizer.unk_token]
          UpperCAmelCase									=					[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase									=					self.get_tokenizer()
          UpperCAmelCase									=					self.get_rust_tokenizer(add_prefix_space=lowercase_							)

          UpperCAmelCase									=					'lower newer'

          # Testing tokenization
          UpperCAmelCase									=					tokenizer.tokenize(lowercase_   ,       add_prefix_space=lowercase_							)
          UpperCAmelCase									=					rust_tokenizer.tokenize(lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          # Testing conversion to ids without special tokens
          UpperCAmelCase									=					tokenizer.encode(lowercase_   ,       add_special_tokens=lowercase_   ,       add_prefix_space=lowercase_							)
          UpperCAmelCase									=					rust_tokenizer.encode(lowercase_   ,       add_special_tokens=lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          # Testing conversion to ids with special tokens
          UpperCAmelCase									=					self.get_rust_tokenizer(add_prefix_space=lowercase_							)
          UpperCAmelCase									=					tokenizer.encode(lowercase_   ,       add_prefix_space=lowercase_							)
          UpperCAmelCase									=					rust_tokenizer.encode(lowercase_							)
          self.assertListEqual(lowercase_   ,       lowercase_							)

          # Testing the unknown token
          UpperCAmelCase									=					tokens + [rust_tokenizer.unk_token]
          UpperCAmelCase									=					[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase_							)   ,       lowercase_							)




      def     a_      (							self   ,       *lowercase_   ,       **lowercase_							)						->					int:
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass




      def     a_      (							self   ,       lowercase_=1_5							)						->					Optional[Any]:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
                  UpperCAmelCase									=					self.rust_tokenizer_class.from_pretrained(lowercase_   ,       **lowercase_							)

                  # Simple input
                  UpperCAmelCase									=					'This is a simple input'
                  UpperCAmelCase									=					['This is a simple input 1', 'This is a simple input 2']
                  UpperCAmelCase									=					('This is a simple input', 'This is a pair')
                  UpperCAmelCase									=					[
                      ('This is a simple input 1', 'This is a simple input 2'),
                      ('This is a simple pair 1', 'This is a simple pair 2'),
                  ]

                  # Simple input tests
                  self.assertRaises(lowercase_   ,       tokenizer_r.encode   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'							)

                  # Simple input
                  self.assertRaises(lowercase_   ,       tokenizer_r.encode_plus   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'							)

                  # Simple input
                  self.assertRaises(
                      lowercase_   ,       tokenizer_r.batch_encode_plus   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'   ,       )

                  # Pair input
                  self.assertRaises(lowercase_   ,       tokenizer_r.encode   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'							)

                  # Pair input
                  self.assertRaises(lowercase_   ,       tokenizer_r.encode_plus   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'							)

                  # Pair input
                  self.assertRaises(
                      lowercase_   ,       tokenizer_r.batch_encode_plus   ,       lowercase_   ,       max_length=lowercase_   ,       padding='max_length'   ,       )




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					GPTaTokenizer.from_pretrained(self.tmpdirname   ,       pad_token='<pad>'							)

          # Simple input
          UpperCAmelCase									=					'This is a simple input'
          UpperCAmelCase									=					['This is a simple input looooooooong', 'This is a simple input']
          UpperCAmelCase									=					('This is a simple input', 'This is a pair')
          UpperCAmelCase									=					[
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          UpperCAmelCase									=					tokenizer.pad_token_id

          UpperCAmelCase									=					tokenizer(lowercase_   ,       padding='max_length'   ,       max_length=3_0   ,       return_tensors='np'							)
          UpperCAmelCase									=					tokenizer(lowercase_   ,       padding=lowercase_   ,       truncate=lowercase_   ,       return_tensors='np'							)
          UpperCAmelCase									=					tokenizer(*lowercase_   ,       padding='max_length'   ,       max_length=6_0   ,       return_tensors='np'							)
          UpperCAmelCase									=					tokenizer(lowercase_   ,       padding=lowercase_   ,       truncate=lowercase_   ,       return_tensors='np'							)

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]   ,       3_0							)
          self.assertTrue(pad_token_id in out_s['input_ids']							)
          self.assertTrue(0 in out_s['attention_mask']							)

          # s2
          # test automatic padding
          self.assertEqual(out_sa['input_ids'].shape[-1]   ,       3_3							)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['input_ids'][0]							)
          self.assertFalse(0 in out_sa['attention_mask'][0]							)
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['input_ids'][1]							)
          self.assertTrue(0 in out_sa['attention_mask'][1]							)

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['input_ids'].shape[-1]   ,       6_0							)
          self.assertTrue(pad_token_id in out_p['input_ids']							)
          self.assertTrue(0 in out_p['attention_mask']							)

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['input_ids'].shape[-1]   ,       5_2							)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['input_ids'][0]							)
          self.assertFalse(0 in out_pa['attention_mask'][0]							)
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['input_ids'][1]							)
          self.assertTrue(0 in out_pa['attention_mask'][1]							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					'$$$'
          UpperCAmelCase									=					GPTaTokenizer.from_pretrained(self.tmpdirname   ,       bos_token=lowercase_   ,       add_bos_token=lowercase_							)

          UpperCAmelCase									=					'This is a simple input'
          UpperCAmelCase									=					['This is a simple input 1', 'This is a simple input 2']

          UpperCAmelCase									=					tokenizer.bos_token_id

          UpperCAmelCase									=					tokenizer(lowercase_							)
          UpperCAmelCase									=					tokenizer(lowercase_							)

          self.assertEqual(out_s.input_ids[0]   ,       lowercase_							)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

          UpperCAmelCase									=					tokenizer.decode(out_s.input_ids							)
          UpperCAmelCase									=					tokenizer.batch_decode(out_sa.input_ids							)

          self.assertEqual(decode_s.split()[0]   ,       lowercase_							)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)




      def     a_      (							self							)						->					Optional[Any]:
          pass




      def     a_      (							self							)						->					Tuple:
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          UpperCAmelCase									=					[self.get_tokenizer(do_lower_case=lowercase_   ,       add_bos_token=lowercase_							)]
          for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"							):
                  UpperCAmelCase									=					'Encode this.'
                  UpperCAmelCase									=					'This one too please.'
                  UpperCAmelCase									=					tokenizer.encode(lowercase_   ,       add_special_tokens=lowercase_							)
                  encoded_sequence += tokenizer.encode(lowercase_   ,       add_special_tokens=lowercase_							)
                  UpperCAmelCase									=					tokenizer.encode_plus(
                      lowercase_   ,       lowercase_   ,       add_special_tokens=lowercase_   ,       return_special_tokens_mask=lowercase_   ,       )
                  UpperCAmelCase									=					encoded_sequence_dict['input_ids']
                  UpperCAmelCase									=					encoded_sequence_dict['special_tokens_mask']
                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)

                  UpperCAmelCase									=					[
                      (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowercase_							)
                  ]
                  UpperCAmelCase									=					[x for x in filtered_sequence if x is not None]
                  self.assertEqual(lowercase_   ,       lowercase_							)

@require_tokenizers
class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self							)						->					Tuple:
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          UpperCAmelCase									=					AutoTokenizer.from_pretrained('facebook/opt-350m'   ,       from_slow=lowercase_							)
          UpperCAmelCase									=					'A photo of a cat'

          UpperCAmelCase									=					tokenizer.encode(
              lowercase_   ,       )
          self.assertEqual(lowercase_   ,       [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]							)
          tokenizer.save_pretrained('test_opt'							)

          UpperCAmelCase									=					AutoTokenizer.from_pretrained('./test_opt'							)
          UpperCAmelCase									=					tokenizer.encode(
              lowercase_   ,       )
          self.assertEqual(lowercase_   ,       [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]							)




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					AutoTokenizer.from_pretrained('facebook/opt-350m'   ,       use_slow=lowercase_							)
          UpperCAmelCase									=					'A photo of a cat'

          UpperCAmelCase									=					tokenizer.encode(
              lowercase_   ,       )
          # Same as above
          self.assertEqual(lowercase_   ,       [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]							)




      @unittest.skip('This test is failing because of a bug in the fast tokenizer'							)
      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					AutoTokenizer.from_pretrained('facebook/opt-350m'   ,       from_slow=lowercase_							)

          UpperCAmelCase									=					'bos'
          UpperCAmelCase									=					tokenizer.get_vocab()['bos']

          UpperCAmelCase									=					'A photo of a cat'
          UpperCAmelCase									=					tokenizer.encode(
              lowercase_   ,       )
          # We changed the bos token
          self.assertEqual(lowercase_   ,       [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]							)
          tokenizer.save_pretrained('./tok'							)
          UpperCAmelCase									=					AutoTokenizer.from_pretrained('./tok'							)
          self.assertTrue(tokenizer.is_fast							)
          UpperCAmelCase									=					tokenizer.encode(
              lowercase_   ,       )
          self.assertEqual(lowercase_   ,       [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]							)





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


SCREAMING_SNAKE_CASE_    					=						16
SCREAMING_SNAKE_CASE_    					=						32




def     lowercase__						(					lowerCAmelCase				:					Accelerator ,				lowerCAmelCase				:					int = 16 ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					AutoTokenizer.from_pretrained('bert-base-cased' )
    UpperCAmelCase									=					load_dataset('glue' ,				'mrpc' )

    def tokenize_function(lowerCAmelCase				:					int ):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase									=					tokenizer(examples['sentence1'] ,				examples['sentence2'] ,				truncation=lowerCAmelCase ,				max_length=lowerCAmelCase )
        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(
            lowerCAmelCase ,				batched=lowerCAmelCase ,				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(lowerCAmelCase				:					Union[str, Any] ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCAmelCase									=					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":
            UpperCAmelCase									=					16
        elif accelerator.mixed_precision != "no":
            UpperCAmelCase									=					8
        else:
            UpperCAmelCase									=					None

        return tokenizer.pad(
            lowerCAmelCase ,				padding='longest' ,				max_length=lowerCAmelCase ,				pad_to_multiple_of=lowerCAmelCase ,				return_tensors='pt' ,				)

    # Instantiate dataloaders.
    UpperCAmelCase									=					DataLoader(
        tokenized_datasets['train'] ,				shuffle=lowerCAmelCase ,				collate_fn=lowerCAmelCase ,				batch_size=lowerCAmelCase )
    UpperCAmelCase									=					DataLoader(
        tokenized_datasets['validation'] ,				shuffle=lowerCAmelCase ,				collate_fn=lowerCAmelCase ,				batch_size=lowerCAmelCase )

    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

   SCREAMING_SNAKE_CASE_    					=						mocked_dataloaders  # noqa: F811




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    if os.environ.get('TESTING_MOCKED_DATALOADERS' ,				lowerCAmelCase ) == "1":
        UpperCAmelCase									=					2
    # New Code #
    UpperCAmelCase									=					int(args.gradient_accumulation_steps )
    UpperCAmelCase									=					int(args.local_sgd_steps )
    # Initialize accelerator
    UpperCAmelCase									=					Accelerator(
        cpu=args.cpu ,				mixed_precision=args.mixed_precision ,				gradient_accumulation_steps=lowerCAmelCase )
    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
    UpperCAmelCase									=					config['lr']
    UpperCAmelCase									=					int(config['num_epochs'] )
    UpperCAmelCase									=					int(config['seed'] )
    UpperCAmelCase									=					int(config['batch_size'] )

    UpperCAmelCase									=					evaluate.load('glue' ,				'mrpc' )

    set_seed(lowerCAmelCase )
    UpperCAmelCase			,							UpperCAmelCase									=					get_dataloaders(lowerCAmelCase ,				lowerCAmelCase )
    # 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=lowerCAmelCase )

    # 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=lowerCAmelCase )

    # Instantiate scheduler
    UpperCAmelCase									=					get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase ,				num_warmup_steps=100 ,				num_training_steps=(len(lowerCAmelCase ) * 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(
        lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )

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

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

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




def     lowercase__						(					) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					argparse.ArgumentParser(description='Simple example of training script.' )
    parser.add_argument(
        '--mixed_precision' ,				type=lowerCAmelCase ,				default=lowerCAmelCase ,				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=lowerCAmelCase ,				default=1 ,				help='The number of minibatches to be ran before gradients are accumulated.' ,				)
    parser.add_argument(
        '--local_sgd_steps' ,				type=lowerCAmelCase ,				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.' )
    UpperCAmelCase									=					parser.parse_args()
    UpperCAmelCase									=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCAmelCase ,				lowerCAmelCase )


if __name__ == "__main__":
   main()



