

'''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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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         =  16
_snake_case         =  32







def  _A		(     snake_case		,							snake_case = 16					)      ->     Union[str, Any]:
   _lowercase      :				Tuple      					=       AutoTokenizer.from_pretrained("bert-base-cased"					)
   _lowercase      :				str      					=       load_dataset("glue"		,							"mrpc"					)

   def tokenize_function(snake_case					):
      # max_length=None => use the model max length (it's actually the default)
      _lowercase      :				Optional[int]      					=       tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=snake_case		,							max_length=snake_case					)
      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():
      _lowercase      :				Optional[Any]      					=       datasets.map(
          snake_case		,							batched=snake_case		,							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
   _lowercase      :				Tuple      					=       tokenized_datasets.rename_column("label"		,							"labels"					)

   def collate_fn(snake_case					):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _lowercase      :				Any      					=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _lowercase      :				Union[str, Any]      					=       16
      elif accelerator.mixed_precision != "no":
         _lowercase      :				Optional[int]      					=       8
      else:
         _lowercase      :				List[Any]      					=       None

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

   # Instantiate dataloaders.
   _lowercase      :				List[str]      					=       DataLoader(
       tokenized_datasets["train"]		,							shuffle=snake_case		,							collate_fn=snake_case		,							batch_size=snake_case					)
   _lowercase      :				Tuple      					=       DataLoader(
       tokenized_datasets["validation"]		,							shuffle=snake_case		,							collate_fn=snake_case		,							batch_size=snake_case					)

   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

    _snake_case         =  mocked_dataloaders  # noqa: F811







def  _A		(     snake_case		,							snake_case					)      ->     Tuple:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							snake_case					) == "1":
      _lowercase      :				List[Any]      					=       2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      _lowercase      :				Any      					=       Accelerator(
          cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir					)
   else:
      _lowercase      :				int      					=       Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _lowercase      :				Optional[Any]      					=       config["lr"]
   _lowercase      :				List[str]      					=       int(config["num_epochs"]					)
   _lowercase      :				Union[str, Any]      					=       int(config["seed"]					)
   _lowercase      :				Optional[int]      					=       int(config["batch_size"]					)
   set_seed(snake_case					)

   _lowercase					,				_lowercase      :				Any      					=       get_dataloaders(snake_case		,							snake_case					)
   _lowercase      :				Optional[int]      					=       evaluate.load("glue"		,							"mrpc"					)

   # If the batch size is too big we use gradient accumulation
   _lowercase      :				Optional[Any]      					=       1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _lowercase      :				Dict      					=       batch_size // MAX_GPU_BATCH_SIZE
      _lowercase      :				Any      					=       MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _lowercase      :				int      					=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=snake_case					)

   # 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).
   _lowercase      :				Optional[Any]      					=       model.to(accelerator.device					)

   # Instantiate optimizer
   _lowercase      :				Optional[Any]      					=       AdamW(params=model.parameters()		,							lr=snake_case					)

   # Instantiate scheduler
   _lowercase      :				Optional[int]      					=       get_linear_schedule_with_warmup(
       optimizer=snake_case		,							num_warmup_steps=1_00		,							num_training_steps=(len(snake_case					) * 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.
   _lowercase					,				_lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				str      					=       accelerator.prepare(
       snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case					)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      _lowercase      :				str      					=       os.path.split(snake_case					)[-1].split("."					)[0]
      accelerator.init_trackers(snake_case		,							snake_case					)

   # Now we train the model
   for epoch in range(snake_case					):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         _lowercase      :				str      					=       0
      for step, batch in enumerate(snake_case					):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device					)
         _lowercase      :				Union[str, Any]      					=       model(**snake_case					)
         _lowercase      :				Optional[int]      					=       outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         _lowercase      :				Optional[Any]      					=       loss / gradient_accumulation_steps
         accelerator.backward(snake_case					)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

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

      _lowercase      :				List[str]      					=       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,							snake_case					)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": eval_metric["accuracy"],
                 "f1": eval_metric["f1"],
                 "train_loss": total_loss.item() / len(snake_case					),
                 "epoch": epoch,
             }		,							step=snake_case		,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()







def  _A		(     )      ->     List[Any]:
   _lowercase      :				Tuple      					=       argparse.ArgumentParser(description="Simple example of training script."					)
   parser.add_argument(
       "--mixed_precision"		,							type=snake_case		,							default=snake_case		,							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."					)
   parser.add_argument(
       "--with_tracking"		,							action="store_true"		,							help="Whether to load in all available experiment trackers from the environment and use them for logging."		,							)
   parser.add_argument(
       "--project_dir"		,							type=snake_case		,							default="logs"		,							help="Location on where to store experiment tracking logs` and relevent project information"		,							)
   _lowercase      :				Tuple      					=       parser.parse_args()
   _lowercase      :				List[str]      					=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(snake_case		,							snake_case					)


if __name__ == "__main__":
    main()









'''simple docstring'''
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


_snake_case         =  '▁'

_snake_case         =  {'vocab_file': 'spiece.model'}

_snake_case         =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

_snake_case         =  {
    'google/pegasus-xsum': 512,
}


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							VOCAB_FILES_NAMES

   _SCREAMING_SNAKE_CASE   :					Tuple     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['input_ids', 'attention_mask']


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase="<pad>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="<unk>"      ,    _UpperCamelCase="<mask_2>"      ,    _UpperCamelCase="<mask_1>"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=103      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       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  )}'''  )

         _lowercase      :				Dict      					=       (
             ([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}.'''  )
         _lowercase      :				List[str]      					=       additional_special_tokens_extended
      else:
         _lowercase      :				Dict      					=       [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2      ,    self.offset  )]

      _lowercase      :				int      					=       {} 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      ,    )
      _lowercase      :				Union[str, Any]      					=       mask_token_sent
      _lowercase      :				str      					=       vocab_file
      _lowercase      :				Dict      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(_UpperCamelCase  )

      # add special tokens to encoder dict
      _lowercase      :				Dict[int, str]      					=       {
          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  )}  )

      _lowercase      :				Dict[str, int]      					=       {v: k for k, v in self.encoder.items()}


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self.sp_model  ) + self.offset


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       {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  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.__dict__.copy()
      _lowercase      :				Union[str, Any]      					=       None
      return state


   def __setstate__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       d

      # for backward compatibility
      if not hasattr(self      ,    "sp_model_kwargs"  ):
         _lowercase      :				List[Any]      					=       {}

      _lowercase      :				Any      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """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:
         _lowercase      :				Any      					=       self.sp_model.IdToPiece(index - self.offset  )
      return token


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       []
      _lowercase      :				Optional[Any]      					=       ""
      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
            _lowercase      :				Tuple      					=       []
         else:
            current_sub_tokens.append(_UpperCamelCase  )
      out_string += self.sp_model.decode(_UpperCamelCase  )
      return out_string.strip()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      return 1


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       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      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 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      ,    _UpperCamelCase      ,    _UpperCamelCase=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      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      if not os.path.isdir(_UpperCamelCase  ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
         return
      _lowercase      :				List[Any]      					=       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:
            _lowercase      :				Optional[int]      					=       self.sp_model.serialized_model_proto()
            fi.write(_UpperCamelCase  )

      return (out_vocab_file,)






