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


lowercase__       =16
lowercase__       =32




def    __UpperCamelCase     (				lowerCAmelCase__							:				Accelerator  ,   lowerCAmelCase__							:				int = 1_6  ):
 __a :  Any						=  AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 __a :  Any						=  load_dataset('''glue'''  ,   '''mrpc'''  )

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

 def collate_fn(lowerCAmelCase__							:				Optional[int]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __a :  List[str]						=  1_2_8 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 :  Optional[Any]						=  1_6
  elif accelerator.mixed_precision != "no":
   __a :  List[Any]						=  8
  else:
   __a :  Tuple						=  None

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

 # Instantiate dataloaders.
 __a :  int						=  DataLoader(
     tokenized_datasets['''train''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )
 __a :  Union[str, Any]						=  DataLoader(
     tokenized_datasets['''validation''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )

 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

      lowercase__       =mocked_dataloaders  # noqa: F811




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

 __a :  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=lowerCAmelCase__  )
 def inner_training_loop(lowerCAmelCase__							:				Dict  ):
  # 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(lowerCAmelCase__  )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __a :  Tuple						=  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).
  __a :  Any						=  model.to(accelerator.device  )

  # Instantiate optimizer
  __a :  int						=  AdamW(params=model.parameters()  ,   lr=lowerCAmelCase__  )
  __a				,       __a :  List[str]						=  get_dataloaders(lowerCAmelCase__  ,   lowerCAmelCase__  )

  # Instantiate scheduler
  __a :  Any						=  get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__  ,   num_warmup_steps=1_0_0  ,   num_training_steps=(len(lowerCAmelCase__  ) * 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 :  Optional[int]						=  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  )
    __a :  Tuple						=  model(**lowerCAmelCase__  )
    __a :  Union[str, Any]						=  outputs.loss
    accelerator.backward(lowerCAmelCase__  )
    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():
     __a :  str						=  model(**lowerCAmelCase__  )
    __a :  str						=  outputs.logits.argmax(dim=-1  )
    __a				,       __a :  Optional[int]						=  accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
    metric.add_batch(
        predictions=lowerCAmelCase__  ,   references=lowerCAmelCase__  ,   )

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

    # 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     (				):
 __a :  Optional[int]						=  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.'''  )
 __a :  List[Any]						=  parser.parse_args()
 __a :  Tuple						=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
 training_function(lowerCAmelCase__  ,   lowerCAmelCase__  )


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


lowercase__       =logging.get_logger(__name__)

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

lowercase__       ={
    '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'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    '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 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

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

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[Any]						=  [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       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
