



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


__snake_case						:List[Any]						=					16
__snake_case						:List[Any]						=					32





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase = 16    ):
    __a							=    AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    __a							=    load_dataset('''glue'''       ,			'''mrpc'''    )

    def tokenize_function(_UpperCAmelCase    ):
        # 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    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __a							=    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":
            __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       ,			drop_last=_UpperCAmelCase    )
    __a							=    DataLoader(
        tokenized_datasets['''validation''']       ,			shuffle=_UpperCAmelCase       ,			collate_fn=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase       ,			drop_last=(accelerator.mixed_precision == '''fp8''')       ,			)

    return train_dataloader, eval_dataloader





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    # 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'''    )

    # If the batch size is too big we use gradient accumulation
    __a							=    1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __a							=    batch_size // MAX_GPU_BATCH_SIZE
        __a							=    MAX_GPU_BATCH_SIZE

    set_seed(_UpperCAmelCase    )
    __a	,       __a							=    get_dataloaders(_UpperCAmelCase       ,			_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    )

    # Instantiate scheduler
    __a							=    get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase       ,			num_warmup_steps=100       ,			num_training_steps=(len(_UpperCAmelCase    ) * 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.

    __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
            __a							=    loss / gradient_accumulation_steps
            accelerator.backward(_UpperCAmelCase    )
            if step % gradient_accumulation_steps == 0:
                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    )





def 					__snake_case     (      ):
    __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 os
import unicodedata
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 SPIECE_UNDERLINE, logging


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[str]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Dict						=					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=False					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<s>"					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<sep>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<pad>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<cls>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<mask>"					,		__SCREAMING_SNAKE_CASE							:					Any=["<eop>", "<eod>"]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           __a							=    AddedToken(__SCREAMING_SNAKE_CASE					,		lstrip=__SCREAMING_SNAKE_CASE					,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else mask_token

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__SCREAMING_SNAKE_CASE					,		remove_space=__SCREAMING_SNAKE_CASE					,		keep_accents=__SCREAMING_SNAKE_CASE					,		bos_token=__SCREAMING_SNAKE_CASE					,		eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		sep_token=__SCREAMING_SNAKE_CASE					,		pad_token=__SCREAMING_SNAKE_CASE					,		cls_token=__SCREAMING_SNAKE_CASE					,		mask_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    3

           __a							=    do_lower_case
           __a							=    remove_space
           __a							=    keep_accents
           __a							=    vocab_file

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)

           try:
               import jieba
           except ModuleNotFoundError as error:
               raise error.__class__(
                   '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                   '''See https://pypi.org/project/jieba/ for installation.''')
           __a							=    jieba
           __a							=    str.maketrans(''' \n'''					,		'''\u2582\u2583''')



       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return len(self.sp_model)



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(		self							:					Any):


           '''simple docstring'''

           __a							=    self.__dict__.copy()
           __a							=    None
           return state



       def __setstate__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __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 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           if self.remove_space:
               __a							=    ''' '''.join(inputs.strip().split())
           else:
               __a							=    inputs
           __a							=    outputs.replace('''``'''					,		'''"''').replace('''\'\''''					,		'''"''')

           if not self.keep_accents:
               __a							=    unicodedata.normalize('''NFKD'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    ''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
           if self.do_lower_case:
               __a							=    outputs.lower()

           return outputs



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    self.preprocess_text(__SCREAMING_SNAKE_CASE)
           __a							=    self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)
           __a							=    []
           for piece in pieces:
               if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                   __a							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE					,		''''''))
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                       if len(cur_pieces[0]) == 1:
                           __a							=    cur_pieces[1:]
                       else:
                           __a							=    cur_pieces[0][1:]
                   cur_pieces.append(piece[-1])
                   new_pieces.extend(__SCREAMING_SNAKE_CASE)
               else:
                   new_pieces.append(__SCREAMING_SNAKE_CASE)

           return new_pieces



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE					,		''' ''').strip()
           return out_string



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [self.cls_token_id]
           if token_ids_a is None:
               return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__SCREAMING_SNAKE_CASE					,		token_ids_a=__SCREAMING_SNAKE_CASE					,		already_has_special_tokens=__SCREAMING_SNAKE_CASE)

           if token_ids_a is not None:
               return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]
           return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [2]

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



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

           return (out_vocab_file,)





       def 		_lowerCamelCase      (		self							:					Any					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    super()._decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    text.replace(''' '''					,		'''''').replace('''\u2582'''					,		''' ''').replace('''\u2583'''					,		'''\n''')
           return text





