





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a__:      Union[str, Any]									=		16
a__:      Union[str, Any]									=		32


def 	UpperCamelCase__(       UpperCamelCase__			:       Accelerator ,     UpperCamelCase__			:       int = 16 ,     UpperCamelCase__			:       str = "bert-base-cased"  )->Tuple:
       A__											=						AutoTokenizer.from_pretrained(UpperCamelCase__  )
       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
       A__											=						datasets.map(
           UpperCamelCase__ ,     batched=UpperCamelCase__ ,     remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,     load_from_cache_file=UpperCamelCase__  )

       # 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__			:       Dict  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(UpperCamelCase__ ,     padding='''max_length''' ,     max_length=1_28 ,     return_tensors='''pt'''  )
              return tokenizer.pad(UpperCamelCase__ ,     padding='''longest''' ,     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


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       int  )->Dict:
       # Initialize accelerator
       A__											=						Accelerator()

       # 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__											=						args.model_name_or_path

       set_seed(UpperCamelCase__  )
       A__					,	A__											=						get_dataloaders(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A__											=						AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,     return_dict=UpperCamelCase__  )

       # Instantiate optimizer
       A__											=						(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A__											=						optimizer_cls(params=model.parameters() ,     lr=UpperCamelCase__  )

       if accelerator.state.deepspeed_plugin is not None:
              A__											=						accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              A__											=						1
       A__											=						(len(UpperCamelCase__  ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A__											=						get_linear_schedule_with_warmup(
                  optimizer=UpperCamelCase__ ,     num_warmup_steps=0 ,     num_training_steps=UpperCamelCase__ ,     )
       else:
              A__											=						DummyScheduler(UpperCamelCase__ ,     total_num_steps=UpperCamelCase__ ,     warmup_num_steps=0  )

       # 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__  )

       # We need to keep track of how many total steps we have iterated over
       A__											=						0
       # We also need to keep track of the stating epoch so files are named properly
       A__											=						0

       # Now we train the model
       A__											=						evaluate.load('''glue''' ,     '''mrpc'''  )
       A__											=						0
       A__											=						{}
       for epoch in range(UpperCamelCase__ ,     UpperCamelCase__  ):
              model.train()
              for step, batch in enumerate(UpperCamelCase__  ):
                     A__											=						model(**UpperCamelCase__  )
                     A__											=						outputs.loss
                     A__											=						loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase__  )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              A__											=						0
              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  )
                     # It is slightly faster to call this once, than multiple times
                     A__					,	A__											=						accelerator.gather(
                         (predictions, batch['''labels'''])  )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(UpperCamelCase__  ) - 1:
                                   A__											=						predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                                   A__											=						references[: len(eval_dataloader.dataset  ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     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__  )
              A__											=						eval_metric['''accuracy''']

              if best_performance < eval_metric["accuracy"]:
                     A__											=						eval_metric['''accuracy''']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir ,     '''all_results.json'''  ) ,     '''w'''  ) as f:
                     json.dump(UpperCamelCase__ ,     UpperCamelCase__  )


def 	UpperCamelCase__(       )->Optional[int]:
       A__											=						argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
       parser.add_argument(
           '''--model_name_or_path''' ,     type=UpperCamelCase__ ,     default='''bert-base-cased''' ,     help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,     required=UpperCamelCase__ ,     )
       parser.add_argument(
           '''--output_dir''' ,     type=UpperCamelCase__ ,     default='''.''' ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,     )
       parser.add_argument(
           '''--performance_lower_bound''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' ,     )
       parser.add_argument(
           '''--num_epochs''' ,     type=UpperCamelCase__ ,     default=3 ,     help='''Number of train epochs.''' ,     )
       A__											=						parser.parse_args()
       A__											=						{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(UpperCamelCase__ ,     UpperCamelCase__  )


if __name__ == "__main__":
       main()












import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




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							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else bos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else eos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else unk_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else pad_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else cls_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        A__											=						{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCamelCase,eos_token=__lowerCamelCase,unk_token=__lowerCamelCase,pad_token=__lowerCamelCase,sep_token=__lowerCamelCase,mask_token=__lowerCamelCase,cls_token=__lowerCamelCase,sp_model_kwargs=self.sp_model_kwargs,**__lowerCamelCase,)

        A__											=						vocab_file

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(__lowerCamelCase )



 @property
 def 				UpperCamelCase			(							self ):
        return self.sp_model.get_piece_size()



 def 				UpperCamelCase			(							self ):
        A__											=						{self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab



 def __getstate__(							self ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        A__											=						d

        # for backward compatibility
        if not hasattr(self,'''sp_model_kwargs''' ):
               A__											=						{}

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.encode(__lowerCamelCase,out_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[]
        A__											=						''''''
        A__											=						False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(__lowerCamelCase ) + token
                      A__											=						True
                      A__											=						[]
               else:
                      current_sub_tokens.append(__lowerCamelCase )
                      A__											=						False
        out_string += self.sp_model.decode(__lowerCamelCase )
        return out_string.strip()



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        A__											=						kwargs.pop('''use_source_tokenizer''',__lowerCamelCase )

        A__											=						self.convert_ids_to_tokens(__lowerCamelCase,skip_special_tokens=__lowerCamelCase )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__											=						[]
        A__											=						[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )
                             A__											=						[]
                      sub_texts.append(__lowerCamelCase )
               else:
                      current_sub_text.append(__lowerCamelCase )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               A__											=						re.sub(r''' (\[(MASK|SEP)\])''',r'''\1''',''' '''.join(__lowerCamelCase ) )
        else:
               A__											=						''''''.join(__lowerCamelCase )

        A__											=						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               A__											=						self.clean_up_tokenization(__lowerCamelCase )
               return clean_text
        else:
               return text



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
               return
        A__											=						os.path.join(
            __lowerCamelCase,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file,__lowerCamelCase )
        elif not os.path.isfile(self.vocab_file ):
               with open(__lowerCamelCase,'''wb''' ) as fi:
                      A__											=						self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase )

        return (out_vocab_file,)



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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False ):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCamelCase,token_ids_a=__lowerCamelCase,already_has_special_tokens=__lowerCamelCase )

        if token_ids_a is None:
               return [1] + ([0] * len(__lowerCamelCase )) + [1]
        return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]




 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]





