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


_lowercase							:      Optional[int]      							=get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[str]            =				XLMRobertaTokenizer
  lowercase		:     Dict            =				XLMRobertaTokenizerFast
  lowercase		:     str            =				True
  lowercase		:     Tuple            =				True







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

         # We have a SentencePiece fixture for testing
         A						:     List[str]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
         tokenizer.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[Any]:
         A						:     List[str]         ='<pad>'
         A						:     int         =1

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         A						:     List[str]         =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(SCREAMING_SNAKE_CASE__     )      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         self.assertEqual(self.get_tokenizer().vocab_size      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				str:
         A						:     Union[str, Any]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )

         A						:     str         =tokenizer.tokenize('This is a test'     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	['▁This', '▁is', '▁a', '▁t', 'est']     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )      ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,	)

         A						:     Any         =tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 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						:     Tuple         =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ]      ,	)

         A						:     Union[str, Any]         =tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 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 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Optional[int]:
         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						:     Any         =(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						:     List[Any]         =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

                       A						:     str         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Optional[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # 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						:     List[str]         =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f     )
                       self.assertSequenceEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

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

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=True
                       A						:     Optional[int]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

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

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Tuple         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

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

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=False
                       A						:     List[Any]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     str         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # 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						:     List[Any]         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

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

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )







  @cached_property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[int]:
         return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         with tempfile.NamedTemporaryFile() as f:
                shutil.copyfile(SCREAMING_SNAKE_CASE__      ,	f.name     )
                A						:     Optional[Any]         =XLMRobertaTokenizer(f.name      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
                A						:     int         =pickle.dumps(SCREAMING_SNAKE_CASE__     )
         pickle.loads(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Union[str, Any]:
         if not self.test_rust_tokenizer:
                return

         A						:     Union[str, Any]         =self.get_tokenizer()
         A						:     int         =self.get_rust_tokenizer()

         A						:     List[str]         ='I was born in 92000, and this is falsé.'

         A						:     Union[str, Any]         =tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Any         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Optional[Any]         =self.get_rust_tokenizer()
         A						:     int         =tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     Any         ='Hello World!'
         A						:     Optional[Any]         =[0, 3_53_78, 66_61, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				str:
         A						:     Any         =(
             '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						:     int         =[
             0,
             32_93,
             83,
             10,
             45_52,
             49_89,
             79_86,
             6_78,
             10,
             59_15,
             1_11,
             17_94_59,
             12_48_50,
             4,
             60_44,
             2_37,
             12,
             6,
             5,
             6,
             4,
             67_80,
             7_05,
             15,
             13_88,
             44,
             3_78,
             1_01_14,
             7_11,
             1_52,
             20,
             6,
             5,
             2_23_76,
             6_42,
             12_21,
             1_51_90,
             3_41_53,
             4_50,
             56_08,
             9_59,
             11_19,
             5_77_02,
             1_36,
             1_86,
             47,
             10_98,
             2_93_67,
             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,
             60_44,
             2_37,
             62_84,
             5_09_01,
             5_28,
             31,
             90,
             34,
             9_27,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Any:
         # fmt: off
         A						:     List[Any]         ={'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=SCREAMING_SNAKE_CASE__      ,	model_name='xlm-roberta-base'      ,	revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'      ,	)







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =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
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       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						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

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

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		warmup_num_steps=0					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

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

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

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

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

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

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



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

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






