


"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__      				=   get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCAmelCase__      				=   256_047
lowerCAmelCase__      				=   256_145


@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		lowercase__   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Dict   =NllbTokenizer
       a  :				Tuple   =NllbTokenizerFast
       a  :				str   =True
       a  :				Tuple   =True
       a  :				Optional[Any]   ={}

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase     :     int								=							NllbTokenizer(_a		,     keep_accents=_a						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							NllbTokenizer(_a		,     keep_accents=_a						)

         lowerCAmelCase     :     List[Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(_a		,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

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

         lowerCAmelCase     :     Dict								=							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
         self.assertListEqual(
             _a		,     [
                 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",
                 ".",
             ]		,     )
         lowerCAmelCase     :     List[str]								=							tokenizer.convert_tokens_to_ids(_a						)
         self.assertListEqual(
             _a		,     [
                 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]
             ]		,     )

         lowerCAmelCase     :     List[str]								=							tokenizer.convert_ids_to_tokens(_a						)
         self.assertListEqual(
             _a		,     [
                 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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase     :     Tuple								=							self.rust_tokenizer_class.from_pretrained(_a		,     **_a						)
             lowerCAmelCase     :     List[Any]								=							self.tokenizer_class.from_pretrained(_a		,     **_a						)

             lowerCAmelCase     :     Optional[int]								=							tempfile.mkdtemp()

             lowerCAmelCase     :     Optional[int]								=							tokenizer_r.save_pretrained(_a						)
             lowerCAmelCase     :     Tuple								=							tokenizer_p.save_pretrained(_a						)

             # 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						)						)
             lowerCAmelCase     :     Dict								=							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f						)
             self.assertSequenceEqual(_a		,     _a						)

             # Checks everything loads correctly in the same way
             lowerCAmelCase     :     Tuple								=							tokenizer_r.from_pretrained(_a						)
             lowerCAmelCase     :     int								=							tokenizer_p.from_pretrained(_a						)

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

             shutil.rmtree(_a						)

             # Save tokenizer rust, legacy_format=True
             lowerCAmelCase     :     Tuple								=							tempfile.mkdtemp()

             lowerCAmelCase     :     List[str]								=							tokenizer_r.save_pretrained(_a		,     legacy_format=_a						)
             lowerCAmelCase     :     str								=							tokenizer_p.save_pretrained(_a						)

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

             # Checks everything loads correctly in the same way
             lowerCAmelCase     :     Optional[Any]								=							tokenizer_r.from_pretrained(_a						)
             lowerCAmelCase     :     int								=							tokenizer_p.from_pretrained(_a						)

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

             shutil.rmtree(_a						)

             # Save tokenizer rust, legacy_format=False
             lowerCAmelCase     :     int								=							tempfile.mkdtemp()

             lowerCAmelCase     :     Tuple								=							tokenizer_r.save_pretrained(_a		,     legacy_format=_a						)
             lowerCAmelCase     :     Tuple								=							tokenizer_p.save_pretrained(_a						)

             # 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
             lowerCAmelCase     :     List[str]								=							tokenizer_r.from_pretrained(_a						)
             lowerCAmelCase     :     Optional[Any]								=							tokenizer_p.from_pretrained(_a						)

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

             shutil.rmtree(_a						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         if not self.test_seqaseq:
           return

         lowerCAmelCase     :     Dict								=							self.get_tokenizers()
         for tokenizer in tokenizers:
           with self.subTest(f"""{tokenizer.__class__.__name__}"""						):
             # Longer text that will definitely require truncation.
             lowerCAmelCase     :     Tuple								=							[
                 ' UN Chief Says There Is No Military Solution in Syria',
                 ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
                 ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
                 ' will only worsen the violence and misery for millions of people.',
             ]
             lowerCAmelCase     :     int								=							[
                 'Şeful ONU declară că nu există o soluţie militară în Siria',
                 'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
                 ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                 ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
             ]
             try:
               lowerCAmelCase     :     Dict								=							tokenizer.prepare_seqaseq_batch(
                   src_texts=_a		,     tgt_texts=_a		,     max_length=3		,     max_target_length=10		,     return_tensors="pt"		,     src_lang="eng_Latn"		,     tgt_lang="ron_Latn"		,     )
             except NotImplementedError:
               return
             self.assertEqual(batch.input_ids.shape[1]		,     3						)
             self.assertEqual(batch.labels.shape[1]		,     10						)
             # max_target_length will default to max_length if not specified
             lowerCAmelCase     :     Tuple								=							tokenizer.prepare_seqaseq_batch(
                 _a		,     tgt_texts=_a		,     max_length=3		,     return_tensors="pt"						)
             self.assertEqual(batch.input_ids.shape[1]		,     3						)
             self.assertEqual(batch.labels.shape[1]		,     3						)

             lowerCAmelCase     :     Union[str, Any]								=							tokenizer.prepare_seqaseq_batch(
                 src_texts=_a		,     max_length=3		,     max_target_length=10		,     return_tensors="pt"						)
             self.assertEqual(batch_encoder_only.input_ids.shape[1]		,     3						)
             self.assertEqual(batch_encoder_only.attention_mask.shape[1]		,     3						)
             self.assertNotIn("decoder_input_ids"		,     _a						)

       @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass







       def        lowercase__							(						self						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase     :     int								=							[AddedToken("<special>"		,     lstrip=_a						)]

             lowerCAmelCase     :     Optional[Any]								=							self.rust_tokenizer_class.from_pretrained(
                 _a		,     additional_special_tokens=_a		,     **_a						)
             lowerCAmelCase     :     Any								=							tokenizer_r.encode("Hey this is a <special> token"						)

             lowerCAmelCase     :     Union[str, Any]								=							tokenizer_r.encode("<special>"		,     add_special_tokens=_a						)[0]

             self.assertTrue(special_token_id in r_output						)

             if self.test_slow_tokenizer:
               lowerCAmelCase     :     Union[str, Any]								=							self.rust_tokenizer_class.from_pretrained(
                   _a		,     additional_special_tokens=_a		,     **_a		,     )
               lowerCAmelCase     :     str								=							self.tokenizer_class.from_pretrained(
                   _a		,     additional_special_tokens=_a		,     **_a						)

               lowerCAmelCase     :     List[str]								=							tokenizer_p.encode("Hey this is a <special> token"						)

               lowerCAmelCase     :     Union[str, Any]								=							tokenizer_cr.encode("Hey this is a <special> token"						)

               self.assertEqual(_a		,     _a						)
               self.assertEqual(_a		,     _a						)
               self.assertTrue(special_token_id in p_output						)
               self.assertTrue(special_token_id in cr_output						)


@require_torch
@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				Tuple   ='''facebook/nllb-200-distilled-600M'''
       a  :				Optional[Any]   =[
           ''' UN Chief Says There Is No Military Solution in Syria''',
           ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
       ]
       a  :				Optional[Any]   =[
           '''Şeful ONU declară că nu există o soluţie militară în Siria''',
           '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
           ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
           ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
       ]
       a  :				List[Any]   =[
           25_60_47,
           1_62_97,
           13_44_08,
           81_65,
           24_80_66,
           1_47_34,
           9_50,
           11_35,
           10_57_21,
           35_73,
           83,
           2_73_52,
           1_08,
           4_94_86,
           2,
       ]

       @classmethod
       def        lowercase__							(						cls						):






         """simple docstring"""





         lowerCAmelCase     :     NllbTokenizer								=							NllbTokenizer.from_pretrained(
             cls.checkpoint_name		,     src_lang="eng_Latn"		,     tgt_lang="ron_Latn"						)
         lowerCAmelCase     :     str								=							1
         return cls

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]		,     256_001						)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]		,     256_002						)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]		,     256_057						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
         self.assertListEqual(self.expected_src_tokens		,     _a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertIn(_a		,     self.tokenizer.all_special_ids						)
         # fmt: off
         lowerCAmelCase     :     Optional[int]								=							[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
         # fmt: on

         lowerCAmelCase     :     List[str]								=							self.tokenizer.decode(_a		,     skip_special_tokens=_a						)
         lowerCAmelCase     :     Optional[int]								=							self.tokenizer.decode(generated_ids[1:]		,     skip_special_tokens=_a						)
         self.assertEqual(_a		,     _a						)
         self.assertNotIn(self.tokenizer.eos_token		,     _a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							['this is gunna be a long sentence ' * 20]
         assert isinstance(src_text[0]		,     _a						)
         lowerCAmelCase     :     Any								=							10
         lowerCAmelCase     :     Optional[Any]								=							self.tokenizer(_a		,     max_length=_a		,     truncation=_a						).input_ids[0]
         self.assertEqual(ids[-1]		,     2						)
         self.assertEqual(ids[0]		,     _a						)
         self.assertEqual(len(_a						)		,     _a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]						)		,     [256_203, 3]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							tempfile.mkdtemp()
         lowerCAmelCase     :     int								=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_a						)
         lowerCAmelCase     :     List[Any]								=							NllbTokenizer.from_pretrained(_a						)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,     _a						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.tokenizer(
             self.src_text		,     text_target=self.tgt_text		,     padding=_a		,     truncation=_a		,     max_length=len(self.expected_src_tokens						)		,     return_tensors="pt"		,     )
         lowerCAmelCase     :     Optional[Any]								=							shift_tokens_right(
             batch["labels"]		,     self.tokenizer.pad_token_id		,     self.tokenizer.lang_code_to_id["ron_Latn"]						)

         self.assertIsInstance(_a		,     _a						)

         self.assertEqual((2, 15)		,     batch.input_ids.shape						)
         self.assertEqual((2, 15)		,     batch.attention_mask.shape						)
         lowerCAmelCase     :     Any								=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens		,     _a						)
         self.assertEqual(_a		,     batch.decoder_input_ids[0, 0]						)  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens		,     [EN_CODE]						)
         self.assertEqual(self.tokenizer.suffix_tokens		,     [self.tokenizer.eos_token_id]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							self.tokenizer(self.src_text		,     padding=_a		,     truncation=_a		,     max_length=3		,     return_tensors="pt"						)
         lowerCAmelCase     :     Optional[int]								=							self.tokenizer(
             text_target=self.tgt_text		,     padding=_a		,     truncation=_a		,     max_length=10		,     return_tensors="pt"						)
         lowerCAmelCase     :     Optional[int]								=							targets['input_ids']
         lowerCAmelCase     :     List[str]								=							shift_tokens_right(
             _a		,     self.tokenizer.pad_token_id		,     decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]		,     )

         self.assertEqual(batch.input_ids.shape[1]		,     3						)
         self.assertEqual(batch.decoder_input_ids.shape[1]		,     10						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.tokenizer._build_translation_inputs(
             "A test"		,     return_tensors="pt"		,     src_lang="eng_Latn"		,     tgt_lang="fra_Latn"						)

         self.assertEqual(
             nested_simplify(_a						)		,     {
                 # A, test, EOS, en_XX
                 "input_ids": [[256_047, 70, 7_356, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 256_057,
             }		,     )







       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							True
         lowerCAmelCase     :     Tuple								=							self.tokenizer(
             "UN Chief says there is no military solution in Syria"		,     src_lang="eng_Latn"		,     tgt_lang="fra_Latn"						)
         self.assertEqual(
             inputs.input_ids		,     [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]						)

         lowerCAmelCase     :     Tuple								=							False
         lowerCAmelCase     :     Dict								=							self.tokenizer(
             "UN Chief says there is no military solution in Syria"		,     src_lang="eng_Latn"		,     tgt_lang="fra_Latn"						)
         self.assertEqual(
             inputs.input_ids		,     [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]						)







'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


__lowerCAmelCase         =    1_6
__lowerCAmelCase         =    3_2





def 					UpperCAmelCase_		(__a      :    Accelerator ,	__a      :    DatasetDict ,	__a      :    List[int] ,	__a      :    List[int] ,	__a      :    int = 1_6 ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   AutoTokenizer.from_pretrained('bert-base-cased' )
   _a				:	str			=   DatasetDict(
       {
           'train': dataset['train'].select(__a ),
           'validation': dataset['train'].select(__a ),
           'test': dataset['validation'],
       } )

   def tokenize_function(__a      :    List[Any] ):
      # max_length=None => use the model max length (it's actually the default)
      _a				:	Optional[int]			=   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():
      _a				:	List[str]			=   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
   _a				:	List[Any]			=   tokenized_datasets.rename_column('label' ,	'labels' )

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

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

   # Instantiate dataloaders.
   _a				:	Any			=   DataLoader(
       tokenized_datasets['train'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )
   _a				:	Optional[int]			=   DataLoader(
       tokenized_datasets['validation'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   _a				:	Optional[Any]			=   DataLoader(
       tokenized_datasets['test'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   return train_dataloader, eval_dataloader, test_dataloader





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Union[str, Any] ):






   """simple docstring"""

   _a				:	Dict			=   []
   # Download the dataset
   _a				:	Tuple			=   load_dataset('glue' ,	'mrpc' )
   # Create our splits
   _a				:	Union[str, Any]			=   StratifiedKFold(n_splits=int(args.num_folds ) )
   # Initialize accelerator
   _a				:	Any			=   Accelerator(cpu=args.cpu ,	mixed_precision=args.mixed_precision )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _a				:	Optional[Any]			=   config['lr']
   _a				:	Optional[int]			=   int(config['num_epochs'] )
   _a				:	Dict			=   int(config['seed'] )
   _a				:	Dict			=   int(config['batch_size'] )

   _a				:	Optional[int]			=   evaluate.load('glue' ,	'mrpc' )

   # If the batch size is too big we use gradient accumulation
   _a				:	List[Any]			=   1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _a				:	Any			=   batch_size // MAX_GPU_BATCH_SIZE
      _a				:	List[str]			=   MAX_GPU_BATCH_SIZE

   set_seed(__a )

   # New Code #
   # Create our folds:
   _a				:	int			=   kfold.split(np.zeros(datasets['train'].num_rows ) ,	datasets['train']['label'] )
   _a				:	Any			=   []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(__a ):
      _a,  _a,  _a				:	Optional[Any]			=   get_fold_dataloaders(
          __a ,	__a ,	__a ,	__a ,	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _a				:	Dict			=   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).
      _a				:	List[Any]			=   model.to(accelerator.device )

      # Instantiate optimizer
      _a				:	List[str]			=   AdamW(params=model.parameters() ,	lr=__a )

      # Instantiate scheduler
      _a				:	List[Any]			=   get_linear_schedule_with_warmup(
          optimizer=__a ,	num_warmup_steps=1_0_0 ,	num_training_steps=(len(__a ) * num_epochs) // gradient_accumulation_steps ,	)

      # 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				:	Union[str, Any]			=   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 )
            _a				:	Dict			=   model(**__a )
            _a				:	int			=   outputs.loss
            _a				:	Any			=   loss / gradient_accumulation_steps
            accelerator.backward(__a )
            if step % gradient_accumulation_steps == 0:
               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():
               _a				:	Union[str, Any]			=   model(**__a )
            _a				:	Tuple			=   outputs.logits.argmax(dim=-1 )
            _a,  _a				:	Any			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=__a ,	references=__a ,	)

         _a				:	List[Any]			=   metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f"""epoch {epoch}:""" ,	__a )

      # New Code #
      # We also run predictions on the test set at the very end
      _a				:	Any			=   []
      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():
            _a				:	Tuple			=   model(**__a )
         _a				:	Dict			=   outputs.logits
         _a,  _a				:	Optional[int]			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
         fold_predictions.append(predictions.cpu() )
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(__a ,	dim=0 ) )
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   _a				:	Dict			=   torch.cat(__a ,	dim=0 )
   _a				:	Any			=   torch.stack(__a ,	dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
   _a				:	str			=   metric.compute(predictions=__a ,	references=__a )
   accelerator.print('Average test metrics from all folds:' ,	__a )





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Any			=   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.' ,	)
   parser.add_argument('--cpu' ,	action='store_true' ,	help='If passed, will train on the CPU.' )
   # New Code #
   parser.add_argument('--num_folds' ,	type=__a ,	default=3 ,	help='The number of splits to perform across the dataset' )
   _a				:	Any			=   parser.parse_args()
   _a				:	int			=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
   training_function(__a ,	__a )


if __name__ == "__main__":
  main()



