






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__a   :							List[str]						= get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class    __lowercase   (						lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					XLMRobertaTokenizer
     SCREAMING_SNAKE_CASE     =					XLMRobertaTokenizerFast
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					True




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         super().setUp()

         # We have a SentencePiece fixture for testing
         __A       =						XLMRobertaTokenizer(UpperCamelCase_			,						keep_accents=UpperCamelCase_					)
         tokenizer.save_pretrained(self.tmpdirname					)




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						"""<pad>"""
         __A       =						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_					)			,						UpperCamelCase_					)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_					)			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						list(self.get_tokenizer().get_vocab().keys()					)

         self.assertEqual(vocab_keys[0]			,						"""<s>"""					)
         self.assertEqual(vocab_keys[1]			,						"""<pad>"""					)
         self.assertEqual(vocab_keys[-1]			,						"""<mask>"""					)
         self.assertEqual(len(UpperCamelCase_					)			,						1_002					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size			,						1_002					)




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						XLMRobertaTokenizer(UpperCamelCase_			,						keep_accents=UpperCamelCase_					)

         __A       =						tokenizer.tokenize("""This is a test"""					)
         self.assertListEqual(UpperCamelCase_			,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(UpperCamelCase_					)			,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,						)

         __A       =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
         self.assertListEqual(
             UpperCamelCase_			,						[
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """9""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """é""",
                 """.""",
             ]			,						)
         __A       =						tokenizer.convert_tokens_to_ids(UpperCamelCase_					)
         self.assertListEqual(
             UpperCamelCase_			,						[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ]			,						)

         __A       =						tokenizer.convert_ids_to_tokens(UpperCamelCase_					)
         self.assertListEqual(
             UpperCamelCase_			,						[
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """<unk>""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """<unk>""",
                 """.""",
             ]			,						)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         if not self.test_slow_tokenizer:
             # as we don't have a slow version, we can't compare the outputs between slow and fast versions
             return

         __A       =						(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                 __A       =						self.rust_tokenizer_class.from_pretrained(UpperCamelCase_			,						**UpperCamelCase_					)
                 __A       =						self.tokenizer_class.from_pretrained(UpperCamelCase_			,						**UpperCamelCase_					)

                 __A       =						tempfile.mkdtemp()

                 __A       =						tokenizer_r.save_pretrained(UpperCamelCase_					)
                 __A       =						tokenizer_p.save_pretrained(UpperCamelCase_					)

                 # Checks it save with the same files + the tokenizer.json file for the fast one
                 self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files					)					)
                 __A       =						tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f					)
                 self.assertSequenceEqual(UpperCamelCase_			,						UpperCamelCase_					)

                 # Checks everything loads correctly in the same way
                 __A       =						tokenizer_r.from_pretrained(UpperCamelCase_					)
                 __A       =						tokenizer_p.from_pretrained(UpperCamelCase_					)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(UpperCamelCase_			,						UpperCamelCase_					)					)
                     # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                     # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                 shutil.rmtree(UpperCamelCase_					)

                 # Save tokenizer rust, legacy_format=True
                 __A       =						tempfile.mkdtemp()

                 __A       =						tokenizer_r.save_pretrained(UpperCamelCase_			,						legacy_format=UpperCamelCase_					)
                 __A       =						tokenizer_p.save_pretrained(UpperCamelCase_					)

                 # Checks it save with the same files
                 self.assertSequenceEqual(UpperCamelCase_			,						UpperCamelCase_					)

                 # Checks everything loads correctly in the same way
                 __A       =						tokenizer_r.from_pretrained(UpperCamelCase_					)
                 __A       =						tokenizer_p.from_pretrained(UpperCamelCase_					)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(UpperCamelCase_			,						UpperCamelCase_					)					)

                 shutil.rmtree(UpperCamelCase_					)

                 # Save tokenizer rust, legacy_format=False
                 __A       =						tempfile.mkdtemp()

                 __A       =						tokenizer_r.save_pretrained(UpperCamelCase_			,						legacy_format=UpperCamelCase_					)
                 __A       =						tokenizer_p.save_pretrained(UpperCamelCase_					)

                 # Checks it saved the tokenizer.json file
                 self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files					)					)

                 # Checks everything loads correctly in the same way
                 __A       =						tokenizer_r.from_pretrained(UpperCamelCase_					)
                 __A       =						tokenizer_p.from_pretrained(UpperCamelCase_					)

                 # Check special tokens are set accordingly on Rust and Python
                 for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(UpperCamelCase_			,						UpperCamelCase_					)					)

                 shutil.rmtree(UpperCamelCase_					)




     @cached_property
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(UpperCamelCase_			,						f.name					)
             __A       =						XLMRobertaTokenizer(f.name			,						keep_accents=UpperCamelCase_					)
             __A       =						pickle.dumps(UpperCamelCase_					)
         pickle.loads(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         if not self.test_rust_tokenizer:
             return

         __A       =						self.get_tokenizer()
         __A       =						self.get_rust_tokenizer()

         __A       =						"""I was born in 92000, and this is falsé."""

         __A       =						tokenizer.tokenize(UpperCamelCase_					)
         __A       =						rust_tokenizer.tokenize(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						self.get_rust_tokenizer()
         __A       =						tokenizer.encode(UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)




     @slow
     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						"""Hello World!"""
         __A       =						[0, 35_378, 6_661, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(UpperCamelCase_			,						self.big_tokenizer.encode(UpperCamelCase_					)					)




     @slow
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						(
             """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
             """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
         )
         __A       =						[
             0,
             3_293,
             83,
             10,
             4_552,
             4_989,
             7_986,
             678,
             10,
             5_915,
             111,
             179_459,
             124_850,
             4,
             6_044,
             237,
             12,
             6,
             5,
             6,
             4,
             6_780,
             705,
             15,
             1_388,
             44,
             378,
             10_114,
             711,
             152,
             20,
             6,
             5,
             22_376,
             642,
             1_221,
             15_190,
             34_153,
             450,
             5_608,
             959,
             1_119,
             57_702,
             136,
             186,
             47,
             1_098,
             29_367,
             47,
             # 4426, # What fairseq tokenizes from "<unk>": "_<"
             # 3678, # What fairseq tokenizes from "<unk>": "unk"
             # 2740, # What fairseq tokenizes from "<unk>": ">"
             3,  # What we tokenize from "<unk>": "<unk>"
             6,  # Residue from the tokenization: an extra sentencepiece underline
             4,
             6_044,
             237,
             6_284,
             50_901,
             528,
             31,
             90,
             34,
             927,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(UpperCamelCase_			,						self.big_tokenizer.encode(UpperCamelCase_					)					)





     @slow
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=UpperCamelCase_			,						model_name="""xlm-roberta-base"""			,						revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""			,						)












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   :							int						= 16
__a   :							str						= 32



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Accelerator      ,					__lowercase :      int = 1_6     )    ->   Optional[Any]:





    """simple docstring"""

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

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        __A       =						datasets.map(
            __lowercase      ,					batched=__lowercase      ,					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
    __A       =						tokenized_datasets.rename_column("""label"""      ,					"""labels"""     )

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

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

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

    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   :							int						= mocked_dataloaders  # noqa: F811



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      List[Any]     )    ->   Tuple:





    """simple docstring"""

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

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

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

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

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

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

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

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

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



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    __A       =						argparse.ArgumentParser(description="""Simple example of training script."""     )
    parser.add_argument(
        """--mixed_precision"""      ,					type=__lowercase      ,					default=__lowercase      ,					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=__lowercase      ,					default=1      ,					help="""The number of minibatches to be ran before gradients are accumulated."""      ,					)
    parser.add_argument(
        """--local_sgd_steps"""      ,					type=__lowercase      ,					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."""     )
    __A       =						parser.parse_args()
    __A       =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
    training_function(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()




