





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
#
########################################################################


a__:      Tuple									=		16
a__:      str									=		32


def 	UpperCamelCase__(       UpperCamelCase__			:       Accelerator ,     UpperCamelCase__			:       int = 16  )->Optional[int]:
       A__											=						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
       A__											=						load_dataset('''glue''' ,     '''mrpc'''  )

       def tokenize_function(UpperCamelCase__			:       Union[str, Any]  ):
              # max_length=None => use the model max length (it's actually the default)
              A__											=						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():
              A__											=						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
       A__											=						tokenized_datasets.rename_column('''label''' ,     '''labels'''  )

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

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

       # Instantiate dataloaders.
       A__											=						DataLoader(
           tokenized_datasets['''train'''] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__  )
       A__											=						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__:      List[Any]									=		mocked_dataloaders  # noqa: F811


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

       A__											=						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__			:       Tuple  ):
              # 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)
              A__											=						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).
              A__											=						model.to(accelerator.device  )

              # Instantiate optimizer
              A__											=						AdamW(params=model.parameters() ,     lr=UpperCamelCase__  )
              A__					,	A__											=						get_dataloaders(UpperCamelCase__ ,     UpperCamelCase__  )

              # Instantiate scheduler
              A__											=						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.
              A__					,	A__					,	A__					,	A__					,	A__											=						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  )
                            A__											=						model(**UpperCamelCase__  )
                            A__											=						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():
                                   A__											=						model(**UpperCamelCase__  )
                            A__											=						outputs.logits.argmax(dim=-1  )
                            A__					,	A__											=						accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
                            metric.add_batch(
                                predictions=UpperCamelCase__ ,     references=UpperCamelCase__ ,     )

                     A__											=						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 	UpperCamelCase__(       )->Optional[int]:
       A__											=						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.'''  )
       A__											=						parser.parse_args()
       A__											=						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(UpperCamelCase__ ,     UpperCamelCase__  )


if __name__ == "__main__":
       main()












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


a__:      Dict									=		logging.get_logger(__name__)

a__:      str									=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__:      Any									=		{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

a__:      Any									=		{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


a__:      Optional[Any]									=		{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	DistilBertTokenizer



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
            or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
        ):
               A__											=						getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
               A__											=						do_lower_case
               A__											=						strip_accents
               A__											=						tokenize_chinese_chars
               A__											=						normalizer_class(**__lowerCamelCase )

        A__											=						do_lower_case



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        A__											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
        return tuple(__lowerCamelCase )





