
"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


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

a		:       int     						=       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a		:       Tuple     						=       {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

a		:       List[str]     						=       {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}







class     lowercase(_lowercase       ):
     __snake_case:	List[str]						=     VOCAB_FILES_NAMES
     __snake_case:	Optional[Any]						=     PRETRAINED_VOCAB_FILES_MAP
     __snake_case:	List[str]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case:	List[Any]						=     ['input_ids', 'attention_mask']
     __snake_case:	str						=     RobertaTokenizer



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE="replace"     ,				__SCREAMING_SNAKE_CASE="<s>"     ,				__SCREAMING_SNAKE_CASE="</s>"     ,				__SCREAMING_SNAKE_CASE="</s>"     ,				__SCREAMING_SNAKE_CASE="<s>"     ,				__SCREAMING_SNAKE_CASE="<unk>"     ,				__SCREAMING_SNAKE_CASE="<pad>"     ,				__SCREAMING_SNAKE_CASE="<mask>"     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=True     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Tuple:







         """simple docstring"""



         super().__init__(
             __SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				tokenizer_file=__SCREAMING_SNAKE_CASE     ,				errors=__SCREAMING_SNAKE_CASE     ,				bos_token=__SCREAMING_SNAKE_CASE     ,				eos_token=__SCREAMING_SNAKE_CASE     ,				sep_token=__SCREAMING_SNAKE_CASE     ,				cls_token=__SCREAMING_SNAKE_CASE     ,				unk_token=__SCREAMING_SNAKE_CASE     ,				pad_token=__SCREAMING_SNAKE_CASE     ,				mask_token=__SCREAMING_SNAKE_CASE     ,				add_prefix_space=__SCREAMING_SNAKE_CASE     ,				trim_offsets=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)

         a__		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
         if pre_tok_state.get('add_prefix_space'     ,				__SCREAMING_SNAKE_CASE   ) != add_prefix_space:
             a__		=			getattr(__SCREAMING_SNAKE_CASE     ,				pre_tok_state.pop('type'   )   )
             a__		=			add_prefix_space
             a__		=			pre_tok_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			add_prefix_space

         a__		=			'post_processor'
         a__		=			getattr(self.backend_tokenizer     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         if tokenizer_component_instance:
             a__		=			json.loads(tokenizer_component_instance.__getstate__()   )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 a__		=			tuple(state['sep']   )
             if "cls" in state:
                 a__		=			tuple(state['cls']   )

             a__		=			False

             if state.get('add_prefix_space'     ,				__SCREAMING_SNAKE_CASE   ) != add_prefix_space:
                 a__		=			add_prefix_space
                 a__		=			True

             if state.get('trim_offsets'     ,				__SCREAMING_SNAKE_CASE   ) != trim_offsets:
                 a__		=			trim_offsets
                 a__		=			True

             if changes_to_apply:
                 a__		=			getattr(__SCREAMING_SNAKE_CASE     ,				state.pop('type'   )   )
                 a__		=			component_class(**__SCREAMING_SNAKE_CASE   )
                 setattr(self.backend_tokenizer     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     @property
     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'   )
             return None
         return str(self._mask_token   )



     @mask_token.setter
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     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 value
         a__		=			value



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     BatchEncoding:







         """simple docstring"""



         a__		=			kwargs.get('is_split_into_words'     ,				__SCREAMING_SNAKE_CASE   )
         assert self.add_prefix_space or not is_split_into_words, (
             f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     BatchEncoding:







         """simple docstring"""



         a__		=			kwargs.get('is_split_into_words'     ,				__SCREAMING_SNAKE_CASE   )

         assert self.add_prefix_space or not is_split_into_words, (
             f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     Tuple[str]:







         """simple docstring"""



         a__		=			self._tokenizer.model.save(__SCREAMING_SNAKE_CASE     ,				name=__SCREAMING_SNAKE_CASE   )
         return tuple(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     str:







         """simple docstring"""



         a__		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     List[int]:







         """simple docstring"""



         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 + sep + token_ids_a + sep   ) * [0]




"""simple docstring"""




import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


a		:       List[str]     						=       16
a		:       str     						=       32



def     __magic_name__		(					UpperCamelCase     :		Accelerator							,		UpperCamelCase     :		int = 16     )     ->						Dict:
    a__		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
    a__		=			load_dataset('glue'							,		'mrpc'     )

    def tokenize_function(UpperCamelCase     :		Optional[int]     ):
        # 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[str]     ):
        # 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     )
    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		:       Union[str, Any]     						=       mocked_dataloaders  # noqa: F811



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							,		UpperCamelCase     ) == "1":
        a__		=			2
    # New Code #
    a__		=			int(args.gradient_accumulation_steps     )
    a__		=			int(args.local_sgd_steps     )
    # Initialize accelerator
    a__		=			Accelerator(
        cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		gradient_accumulation_steps=UpperCamelCase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
    # 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'     )

    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)							,		)

    # 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()
        with LocalSGD(
            accelerator=UpperCamelCase							,		model=UpperCamelCase							,		local_sgd_steps=UpperCamelCase							,		enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(UpperCamelCase     ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device     )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(UpperCamelCase     ):
                    a__		=			model(**UpperCamelCase     )
                    a__		=			output.loss
                    accelerator.backward(UpperCamelCase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        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     __magic_name__		(					)     ->						Any:
    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.'							,		)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'							,		type=UpperCamelCase							,		default=1							,		help='The number of minibatches to be ran before gradients are accumulated.'							,		)
    parser.add_argument(
        '--local_sgd_steps'							,		type=UpperCamelCase							,		default=8							,		help='Number of local SGD steps or None to disable local SGD'     )
    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()


