






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

import sentencepiece as spm

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


__a   :							List[str]						= "▁"

__a   :							int						= {"vocab_file": "spiece.model"}

__a   :							Any						= {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__a   :							Any						= {
    "google/pegasus-xsum": 512,
}


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


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES

     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"]




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict="<pad>"			,						UpperCamelCase_							:      Dict="</s>"			,						UpperCamelCase_							:      Optional[Any]="<unk>"			,						UpperCamelCase_							:      List[Any]="<mask_2>"			,						UpperCamelCase_							:      List[Any]="<mask_1>"			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Dict=103			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						offset
         if additional_special_tokens is not None:
             if not isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 raise TypeError(
                     F"additional_special_tokens should be of type {type(UpperCamelCase_					)}, but is"
                     F" {type(UpperCamelCase_					)}"					)

             __A       =						(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(UpperCamelCase_					)			,						self.offset - 1					)
             ]

             if len(set(UpperCamelCase_					)					) != len(UpperCamelCase_					):
                 raise ValueError(
                     """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                     F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."					)
             __A       =						additional_special_tokens_extended
         else:
             __A       =						[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2			,						self.offset					)]

         __A       =						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token_sent=UpperCamelCase_			,						offset=UpperCamelCase_			,						additional_special_tokens=UpperCamelCase_			,						sp_model_kwargs=self.sp_model_kwargs			,						**UpperCamelCase_			,						)
         __A       =						mask_token_sent
         __A       =						vocab_file
         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(UpperCamelCase_					)

         # add special tokens to encoder dict
         __A       =						{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }					)

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,						self.offset - 1					)}					)

         __A       =						{v: k for k, v in self.encoder.items()}




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return len(self.sp_model					) + self.offset




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



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




     def __getstate__(					self							:      Tuple					):


         """simple docstring"""



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




     def __setstate__(					self							:      Tuple			,						UpperCamelCase_							:      Dict					):


         """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							:      Tuple			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         return self.sp_model.encode(UpperCamelCase_			,						out_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         __A       =						self.sp_model.piece_to_id(UpperCamelCase_					)
         return sp_id + self.offset




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             __A       =						self.sp_model.IdToPiece(index - self.offset					)
         return token




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						[]
         __A       =						""""""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(UpperCamelCase_					) + token
                 __A       =						[]
             else:
                 current_sub_tokens.append(UpperCamelCase_					)
         out_string += self.sp_model.decode(UpperCamelCase_					)
         return out_string.strip()




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[Any]=False					):


         """simple docstring"""



         return 1




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List			,						UpperCamelCase_							:      Optional[List] = None			,						UpperCamelCase_							:      bool = False					):


         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase_					)
         elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase_					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



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

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

         return (out_vocab_file,)












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   :							int						= 16
__a   :							str						= 32



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Accelerator      ,					__lowercase :      int = 1_6     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
    __A       =						load_dataset("""glue"""      ,					"""mrpc"""     )

    def tokenize_function(__lowercase :      Tuple     ):
        # max_length=None => use the model max length (it's actually the default)
        __A       =						tokenizer(examples["""sentence1"""]      ,					examples["""sentence2"""]      ,					truncation=__lowercase      ,					max_length=__lowercase     )
        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(
            __lowercase      ,					batched=__lowercase      ,					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(__lowercase :      List[Any]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __A       =						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       =						1_6
        elif accelerator.mixed_precision != "no":
            __A       =						8
        else:
            __A       =						None

        return tokenizer.pad(
            __lowercase      ,					padding="""longest"""      ,					max_length=__lowercase      ,					pad_to_multiple_of=__lowercase      ,					return_tensors="""pt"""      ,					)

    # Instantiate dataloaders.
    __A       =						DataLoader(
        tokenized_datasets["""train"""]      ,					shuffle=__lowercase      ,					collate_fn=__lowercase      ,					batch_size=__lowercase     )
    __A       =						DataLoader(
        tokenized_datasets["""validation"""]      ,					shuffle=__lowercase      ,					collate_fn=__lowercase      ,					batch_size=__lowercase     )

    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   :							int						= mocked_dataloaders  # noqa: F811



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      List[Any]     )    ->   Tuple:





    """simple docstring"""

    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,					__lowercase     ) == "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=__lowercase     )
    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(__lowercase     )
    __A     ,						__A       =						get_dataloaders(__lowercase      ,					__lowercase     )
    # 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=__lowercase     )

    # 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=__lowercase     )

    # Instantiate scheduler
    __A       =						get_linear_schedule_with_warmup(
        optimizer=__lowercase      ,					num_warmup_steps=1_0_0      ,					num_training_steps=(len(__lowercase     ) * 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(
        __lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )

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

        model.eval()
        for step, batch in enumerate(__lowercase     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            with torch.no_grad():
                __A       =						model(**__lowercase     )
            __A       =						outputs.logits.argmax(dim=-1     )
            __A     ,						__A       =						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
            metric.add_batch(
                predictions=__lowercase      ,					references=__lowercase      ,					)

        __A       =						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    __A       =						argparse.ArgumentParser(description="""Simple example of training script."""     )
    parser.add_argument(
        """--mixed_precision"""      ,					type=__lowercase      ,					default=__lowercase      ,					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=__lowercase      ,					default=1      ,					help="""The number of minibatches to be ran before gradients are accumulated."""      ,					)
    parser.add_argument(
        """--local_sgd_steps"""      ,					type=__lowercase      ,					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""": 4_2, """batch_size""": 1_6}
    training_function(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()




