



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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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_			=					16
a_			=					32





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: int = 16							):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
   __lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc'''							)

   def tokenize_function(_UpperCamelCase			: int							):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCamelCase													=    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():
      __lowerCamelCase													=    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
   __lowerCamelCase													=    tokenized_datasets.rename_column('''label''' ,'''labels'''							)

   def collate_fn(_UpperCamelCase			: Optional[int]							):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __lowerCamelCase													=    1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         __lowerCamelCase													=    16
      elif accelerator.mixed_precision != "no":
         __lowerCamelCase													=    8
      else:
         __lowerCamelCase													=    None

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

   # Instantiate dataloaders.
   __lowerCamelCase													=    DataLoader(
       tokenized_datasets['''train'''] ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=_UpperCamelCase							)
   __lowerCamelCase													=    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

  a_			=					mocked_dataloaders  # noqa: F811





def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: Tuple							):
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase							) == "1":
      __lowerCamelCase													=    2
   # New Code #
   __lowerCamelCase													=    int(args.gradient_accumulation_steps							)
   # Initialize accelerator
   __lowerCamelCase													=    Accelerator(
       cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=_UpperCamelCase							)
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''							)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCamelCase													=    config['''lr''']
   __lowerCamelCase													=    int(config['''num_epochs''']							)
   __lowerCamelCase													=    int(config['''seed''']							)
   __lowerCamelCase													=    int(config['''batch_size''']							)

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

   set_seed(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    get_dataloaders(_UpperCamelCase ,_UpperCamelCase							)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCamelCase													=    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).
   __lowerCamelCase													=    model.to(accelerator.device							)

   # Instantiate optimizer
   __lowerCamelCase													=    AdamW(params=model.parameters() ,lr=_UpperCamelCase							)

   # Instantiate scheduler
   __lowerCamelCase													=    get_linear_schedule_with_warmup(
       optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,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.
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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							)
         # 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(_UpperCamelCase							):
            __lowerCamelCase													=    model(**_UpperCamelCase							)
            __lowerCamelCase													=    output.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():
            __lowerCamelCase													=    model(**_UpperCamelCase							)
         __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
         __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
         metric.add_batch(
             predictions=_UpperCamelCase ,references=_UpperCamelCase ,)

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





def 		a__			(		):
   __lowerCamelCase													=    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.''' ,)
   # New Code #
   parser.add_argument(
       '''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''The number of minibatches to be ran before gradients are accumulated.''' ,)
   parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.'''							)
   __lowerCamelCase													=    parser.parse_args()
   __lowerCamelCase													=    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_UpperCamelCase ,_UpperCamelCase							)


if __name__ == "__main__":
  main()











import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_			=					get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  XLMProphetNetTokenizer
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()

    # We have a SentencePiece fixture for testing
    __lowerCamelCase													=    XLMProphetNetTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)
    tokenizer.save_pretrained(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''[PAD]'''
    __lowerCamelCase													=    0

    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				):



    '''simple docstring'''
    __lowerCamelCase													=    list(self.get_tokenizer().get_vocab().keys()				)

    self.assertEqual(vocab_keys[0]	,				'''[PAD]'''				)
    self.assertEqual(vocab_keys[1]	,				'''[CLS]'''				)
    self.assertEqual(vocab_keys[-1]	,				'''j'''				)
    self.assertEqual(len(__UpperCAmelCase				)	,				1012				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.assertEqual(self.get_tokenizer().vocab_size	,				1012				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    XLMProphetNetTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)

    __lowerCamelCase													=    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]]	,				)

    __lowerCamelCase													=    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''',
            '''.''',
        ]	,				)
    __lowerCamelCase													=    tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            value + tokenizer.fairseq_offset
            for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
        ]	,				)

    __lowerCamelCase													=    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				):



    '''simple docstring'''
    return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''Hello World!'''
    __lowerCamelCase													=    [35389, 6672, 49, 2]
    self.assertListEqual(__UpperCAmelCase	,				self.big_tokenizer.encode(__UpperCAmelCase				)				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # fmt: off
    __lowerCamelCase													=    {'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 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], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 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]], '''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, 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, 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='''microsoft/xprophetnet-large-wiki100-cased'''	,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''	,				)






