



'''simple docstring'''





import argparse

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
#
# 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase = 16		)		-> List[Any]:
      snake_case__  :    Any    						=      AutoTokenizer.from_pretrained("""bert-base-cased"""		)
      snake_case__  :    Optional[int]    						=      load_dataset("""glue"""     ,   """mrpc"""		)

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

      def collate_fn(_lowerCAmelCase		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case__  :    List[Any]    						=      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":
                  snake_case__  :    str    						=      16
            elif accelerator.mixed_precision != "no":
                  snake_case__  :    Union[str, Any]    						=      8
            else:
                  snake_case__  :    int    						=      None

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

      # Instantiate dataloaders.
      snake_case__  :    List[str]    						=      DataLoader(
          tokenized_datasets["""train"""]     ,   shuffle=_lowerCAmelCase     ,   collate_fn=_lowerCAmelCase     ,   batch_size=_lowerCAmelCase     ,   drop_last=_lowerCAmelCase		)
      snake_case__  :    Any    						=      DataLoader(
          tokenized_datasets["""validation"""]     ,   shuffle=_lowerCAmelCase     ,   collate_fn=_lowerCAmelCase     ,   batch_size=_lowerCAmelCase     ,   drop_last=(accelerator.mixed_precision == """fp8""")     ,   )

      return train_dataloader, eval_dataloader





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[str]:
      # Initialize accelerator
      snake_case__  :    Tuple    						=      Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision		)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__  :    Optional[Any]    						=      config["""lr"""]
      snake_case__  :    Tuple    						=      int(config["""num_epochs"""]		)
      snake_case__  :    Optional[int]    						=      int(config["""seed"""]		)
      snake_case__  :    Tuple    						=      int(config["""batch_size"""]		)

      snake_case__  :    int    						=      evaluate.load("""glue"""     ,   """mrpc"""		)

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

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

      # 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).
      snake_case__  :    List[str]    						=      model.to(accelerator.device		)
      # Instantiate optimizer
      snake_case__  :    Union[str, Any]    						=      AdamW(params=model.parameters()     ,   lr=_lowerCAmelCase		)

      # Instantiate scheduler
      snake_case__  :    str    						=      get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase     ,   num_warmup_steps=100     ,   num_training_steps=(len(_lowerCAmelCase		) * 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.

      snake_case__						,				snake_case__						,				snake_case__						,				snake_case__						,				snake_case__  :    Union[str, Any]    						=      accelerator.prepare(
          _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # Now we train the model
      for epoch in range(_lowerCAmelCase		):
            model.train()
            for step, batch in enumerate(_lowerCAmelCase		):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device		)
                  snake_case__  :    Union[str, Any]    						=      model(**_lowerCAmelCase		)
                  snake_case__  :    Union[str, Any]    						=      outputs.loss
                  snake_case__  :    Dict    						=      loss / gradient_accumulation_steps
                  accelerator.backward(_lowerCAmelCase		)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

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





def 						__snake_case(				)		-> Any:
      snake_case__  :    Dict    						=      argparse.ArgumentParser(description="""Simple example of training script."""		)
      parser.add_argument(
          """--mixed_precision"""     ,   type=_lowerCAmelCase     ,   default=_lowerCAmelCase     ,   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."""		)
      snake_case__  :    Dict    						=      parser.parse_args()
      snake_case__  :    List[Any]    						=      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase     ,   _lowerCAmelCase		)


if __name__ == "__main__":
       main()

import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



