

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


snake_case_        =			16
snake_case_        =			32

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int] = 16      ):



     '''simple docstring'''





     lowercase__   :       int				=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     lowercase__   :       Tuple				=	load_dataset('glue'     ,					'mrpc'      )

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

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

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

     # Instantiate dataloaders.
     lowercase__   :       int				=	DataLoader(
         tokenized_datasets['train']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       str				=	DataLoader(
         tokenized_datasets['validation']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )

     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

    snake_case_        =			mocked_dataloaders  # noqa: F811

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,					SCREAMING_SNAKE_CASE_      ) == "1":
          lowercase__   :       Dict				=	2
     # Initialize accelerator
     lowercase__   :       Optional[int]				=	Accelerator(cpu=args.cpu     ,					mixed_precision=args.mixed_precision      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase__   :       Dict				=	config['lr']
     lowercase__   :       Optional[Any]				=	int(config['num_epochs']      )
     lowercase__   :       List[Any]				=	int(config['seed']      )
     lowercase__   :       Optional[int]				=	int(config['batch_size']      )

     lowercase__   :       str				=	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=SCREAMING_SNAKE_CASE_      )
     def inner_training_loop(SCREAMING_SNAKE_CASE_		:   str      ):
          # 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(SCREAMING_SNAKE_CASE_      )

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          lowercase__   :       Optional[Any]				=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,					return_dict=SCREAMING_SNAKE_CASE_      )

          # 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).
          lowercase__   :       List[str]				=	model.to(accelerator.device      )

          # Instantiate optimizer
          lowercase__   :       Tuple				=	AdamW(params=model.parameters()     ,					lr=SCREAMING_SNAKE_CASE_      )
          lowercase__				,			lowercase__   :       Optional[Any]				=	get_dataloaders(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

          # Instantiate scheduler
          lowercase__   :       Union[str, Any]				=	get_linear_schedule_with_warmup(
              optimizer=SCREAMING_SNAKE_CASE_     ,					num_warmup_steps=100     ,					num_training_steps=(len(SCREAMING_SNAKE_CASE_      ) * 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.
          lowercase__				,			lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Any				=	accelerator.prepare(
              SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

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

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

               lowercase__   :       int				=	metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f"""epoch {epoch}:"""     ,					SCREAMING_SNAKE_CASE_      )

          # 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      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       int				=	argparse.ArgumentParser(description='Simple example of training script.'      )
     parser.add_argument(
         '--mixed_precision'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					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.'      )
     lowercase__   :       Union[str, Any]				=	parser.parse_args()
     lowercase__   :       List[Any]				=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
    main()




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case_        =			logging.get_logger(__name__)

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

snake_case_        =			{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





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

         lowercase__   :       Optional[Any]				=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'						,       a) != do_lower_case
             or pre_tok_state.get('strip_accents'						,       a) != strip_accents
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

