


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


lowerCAmelCase__    :Optional[Any]       =				1_6
lowerCAmelCase__    :Any       =				3_2

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Optional[int] = 1_6			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     AutoTokenizer.from_pretrained('bert-base-cased'			)
							_UpperCAmelCase											=     load_dataset('glue'     ,    'mrpc'			)

							def tokenize_function(a__:						List[str]			):
														# max_length=None => use the model max length (it's actually the default)
														_UpperCAmelCase											=     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():
														_UpperCAmelCase											=     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
							_UpperCAmelCase											=     tokenized_datasets.rename_column('label'     ,    'labels'			)

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

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

							# Instantiate dataloaders.
							_UpperCAmelCase											=     DataLoader(
							    tokenized_datasets['train']     ,    shuffle=lowerCAmelCase__     ,    collate_fn=lowerCAmelCase__     ,    batch_size=lowerCAmelCase__			)
							_UpperCAmelCase											=     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

		lowerCAmelCase__    :Optional[Any]       =				mocked_dataloaders  # noqa: F811

def 			lowerCAmelCase__					(     a__:						List[Any]     ,    a__:						int			)    ->						Any:


							'''simple docstring'''





							if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,    lowerCAmelCase__			) == "1":
														_UpperCAmelCase											=     2
							# New Code #
							_UpperCAmelCase											=     int(args.gradient_accumulation_steps			)
							_UpperCAmelCase											=     int(args.local_sgd_steps			)
							# Initialize accelerator
							_UpperCAmelCase											=     Accelerator(
							    cpu=args.cpu     ,    mixed_precision=args.mixed_precision     ,    gradient_accumulation_steps=lowerCAmelCase__			)
							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
							_UpperCAmelCase											=     config['lr']
							_UpperCAmelCase											=     int(config['num_epochs']			)
							_UpperCAmelCase											=     int(config['seed']			)
							_UpperCAmelCase											=     int(config['batch_size']			)

							_UpperCAmelCase											=     evaluate.load('glue'     ,    'mrpc'			)

							set_seed(lowerCAmelCase__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     get_dataloaders(lowerCAmelCase__     ,    lowerCAmelCase__			)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_UpperCAmelCase											=     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).
							_UpperCAmelCase											=     model.to(accelerator.device			)

							# Instantiate optimizer
							_UpperCAmelCase											=     AdamW(params=model.parameters()     ,    lr=lowerCAmelCase__			)

							# Instantiate scheduler
							_UpperCAmelCase											=     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.
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     accelerator.prepare(
							    lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__			)

							# Now we train the model
							for epoch in range(lowerCAmelCase__			):
														model.train()
														with LocalSGD(
														    accelerator=lowerCAmelCase__     ,    model=lowerCAmelCase__     ,    local_sgd_steps=lowerCAmelCase__     ,    enabled=local_sgd_steps is not None			) as local_sgd:
																					for step, batch in enumerate(lowerCAmelCase__			):
																												# 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(lowerCAmelCase__			):
																																			_UpperCAmelCase											=     model(**lowerCAmelCase__			)
																																			_UpperCAmelCase											=     output.loss
																																			accelerator.backward(lowerCAmelCase__			)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

														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():
																												_UpperCAmelCase											=     model(**lowerCAmelCase__			)
																					_UpperCAmelCase											=     outputs.logits.argmax(dim=-1			)
																					_UpperCAmelCase						,			_UpperCAmelCase											=     accelerator.gather_for_metrics((predictions, batch['labels'])			)
																					metric.add_batch(
																					    predictions=lowerCAmelCase__     ,    references=lowerCAmelCase__     ,    )

														_UpperCAmelCase											=     metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''     ,    lowerCAmelCase__			)

def 			lowerCAmelCase__					(     )    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     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.'     ,    )
							# New Code #
							parser.add_argument(
							    '--gradient_accumulation_steps'     ,    type=lowerCAmelCase__     ,    default=1     ,    help='The number of minibatches to be ran before gradients are accumulated.'     ,    )
							parser.add_argument(
							    '--local_sgd_steps'     ,    type=lowerCAmelCase__     ,    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.'			)
							_UpperCAmelCase											=     parser.parse_args()
							_UpperCAmelCase											=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							training_function(lowerCAmelCase__     ,    lowerCAmelCase__			)


if __name__ == "__main__":
		main()




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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'▁'

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'prophetnet.tokenizer'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  a   (lowerCAmelCase__   ):
   __a					=  collections.OrderedDict()
   with open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""   ) as reader:
      __a					=  reader.readlines()
   for index, token in enumerate(lowerCAmelCase__   ):
      __a					=  token.rstrip("""\n"""   )
      __a					=  index
   return vocab


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[UNK]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A = None  ,       **__A  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

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

        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__A    )    )
        __a					=  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        __a					=  {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10    ):
           __a					=  f'''[unused{i}]'''
           __a					=  5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        __a					=  12
        __a					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(__A    )


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


     def __setstate__(	self  ,       __A    ):
        __a					=  d
        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        # 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 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]

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


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    ) + self.fairseq_offset


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


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        __a					=  self.sp_model.PieceToId(__A    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 	snake_case_  (	self  ,       __A    ):
        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__A    ).replace(__A  ,       """ """    ).strip()
        return out_string


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

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

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

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



