

"""simple docstring"""






import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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										=		16
__lowerCamelCase										=		32





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase = 16						) ->      Union[str, Any]:
 __magic_name__     =      AutoTokenizer.from_pretrained('''bert-base-cased'''						)
 __magic_name__     =      load_dataset('''glue'''      ,   '''mrpc'''						)

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

 def collate_fn(__UpperCamelCase						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __magic_name__     =      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":
   __magic_name__     =      16
  elif accelerator.mixed_precision != "no":
   __magic_name__     =      8
  else:
   __magic_name__     =      None

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

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

 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

 __lowerCamelCase										=		mocked_dataloaders  # noqa: F811





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      ,   __UpperCamelCase						) == "1":
  __magic_name__     =      2
 # Initialize accelerator
 __magic_name__     =      Accelerator(cpu=args.cpu      ,   mixed_precision=args.mixed_precision						)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __magic_name__     =      config['''lr''']
 __magic_name__     =      int(config['''num_epochs''']						)
 __magic_name__     =      int(config['''seed''']						)
 __magic_name__     =      int(config['''batch_size''']						)

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

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=__UpperCamelCase						)
 def inner_training_loop(__UpperCamelCase						):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(__UpperCamelCase						)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __magic_name__     =      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''      ,   return_dict=__UpperCamelCase						)

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

  # Instantiate optimizer
  __magic_name__     =      AdamW(params=model.parameters()      ,   lr=__UpperCamelCase						)
  __magic_name__   ,						__magic_name__     =      get_dataloaders(__UpperCamelCase      ,   __UpperCamelCase						)

  # Instantiate scheduler
  __magic_name__     =      get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase      ,   num_warmup_steps=100      ,   num_training_steps=(len(__UpperCamelCase						) * 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.
  __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      accelerator.prepare(
      __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)

  # Now we train the model
  for epoch in range(__UpperCamelCase						):
   model.train()
   for step, batch in enumerate(__UpperCamelCase						):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device						)
    __magic_name__     =      model(**__UpperCamelCase						)
    __magic_name__     =      outputs.loss
    accelerator.backward(__UpperCamelCase						)
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

   __magic_name__     =      metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''      ,   __UpperCamelCase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()





def 	lowercase      (   ) ->      Union[str, Any]:
 __magic_name__     =      argparse.ArgumentParser(description='''Simple example of training script.'''						)
 parser.add_argument(
     '''--mixed_precision'''      ,   type=__UpperCamelCase      ,   default=__UpperCamelCase      ,   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.'''						)
 __magic_name__     =      parser.parse_args()
 __magic_name__     =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 main()




"""simple docstring"""






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase										=		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       ReformerTokenizer
     _lowerCamelCase     =       ReformerTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       False
     _lowerCamelCase     =       True







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()

      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )
      tokenizer.save_pretrained(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''<s>'''
      __magic_name__     =      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       ):
      __magic_name__     =      list(self.get_tokenizer().get_vocab().keys()       )

      self.assertEqual(vocab_keys[0] ,						'''<unk>'''       )
      self.assertEqual(vocab_keys[1] ,						'''<s>'''       )
      self.assertEqual(vocab_keys[-1] ,						'''j'''       )
      self.assertEqual(len(UpperCamelCase_       ) ,						1000       )







     def 	lowerCAmelCase__    (      self       ):
      self.assertEqual(self.get_tokenizer().vocab_size ,						1000       )







     def 	lowerCAmelCase__    (      self       ):
      if not self.test_rust_tokenizer:
       return

      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_rust_tokenizer()

      __magic_name__     =      '''I was born in 92000, and this is falsé.'''

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

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

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=15       ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
        __magic_name__     =      self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ ,						**UpperCamelCase_       )

        # Simple input
        __magic_name__     =      '''This is a simple input'''
        __magic_name__     =      ['''This is a simple input 1''', '''This is a simple input 2''']
        __magic_name__     =      ('''This is a simple input''', '''This is a pair''')
        __magic_name__     =      [
            ('''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(UpperCamelCase_ ,						tokenizer_r.encode ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

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

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

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

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

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







     def 	lowerCAmelCase__    (      self       ):
      pass







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )

      __magic_name__     =      tokenizer.tokenize('''This is a test'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[285, 46, 10, 170, 382] ,						)

      __magic_name__     =      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''',
              '''.''',
          ] ,						)
      __magic_name__     =      tokenizer.convert_tokens_to_ids(UpperCamelCase_       )
      self.assertListEqual(
          UpperCamelCase_ ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,						)

      __magic_name__     =      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>''',
              '''.''',
          ] ,						)







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''Hello World!'''
      __magic_name__     =      [126, 32, 262, 152, 38, 72, 287]

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







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      (
          '''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'''
      )
      __magic_name__     =      [
          108,
          265,
          24,
          111,
          4,
          258,
          156,
          35,
          28,
          275,
          3,
          259,
          297,
          260,
          84,
          4,
          35,
          110,
          44,
          8,
          259,
          91,
          268,
          21,
          11,
          209,
          274,
          109,
          266,
          277,
          117,
          86,
          93,
          315,
          258,
          278,
          258,
          277,
          258,
          0,
          258,
          288,
          258,
          319,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          287,
          258,
          315,
          258,
          289,
          258,
          278,
          99,
          269,
          266,
          262,
          8,
          259,
          241,
          4,
          217,
          230,
          268,
          266,
          55,
          168,
          106,
          75,
          193,
          266,
          223,
          27,
          49,
          26,
          282,
          25,
          264,
          299,
          19,
          26,
          0,
          258,
          277,
          117,
          86,
          93,
          176,
          183,
          270,
          11,
          262,
          42,
          61,
          265,
      ]

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







     @require_torch
     @slow
     def 	lowerCAmelCase__    (      self       ):
      import torch

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      __magic_name__     =      list(self.big_tokenizer.get_vocab().keys()       )[:10]
      __magic_name__     =      ''' '''.join(UpperCamelCase_       )
      __magic_name__     =      self.big_tokenizer.encode_plus(UpperCamelCase_ ,						return_tensors='''pt'''       )
      __magic_name__     =      self.big_tokenizer.batch_encode_plus([sequence, sequence] ,						return_tensors='''pt'''       )

      __magic_name__     =      ReformerConfig()
      # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
      __magic_name__     =      encoded_sequence['''input_ids'''].shape
      __magic_name__     =      ReformerModel(UpperCamelCase_       )

      # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
       model(**UpperCamelCase_       )
       model(**UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      # fmt: off
      __magic_name__     =      {'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''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]]}  # noqa: E501
      # fmt: on

      # This tokenizer does not know some characters like ")".
      # That is the reason why we use very simple texts here.
      # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
      __magic_name__     =      [
          '''This is a very simple sentence.''',
          '''The quick brown fox jumps over the lazy dog.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=UpperCamelCase_ ,						model_name='''google/reformer-crime-and-punishment''' ,						revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' ,						padding=UpperCamelCase_ ,						sequences=UpperCamelCase_ ,						)

