






"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


UpperCAmelCase_   : Any   			= 16
UpperCAmelCase_   : List[str]   			= 32



def 					_A			(__a    ,				__a = 16							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    SCREAMING_SNAKE_CASE_      :  str    						=		load_dataset('''glue'''    ,				'''mrpc'''							)

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

    def collate_fn(__a							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		8
        else:
            SCREAMING_SNAKE_CASE_      :  Any    						=		None

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		DataLoader(
        tokenized_datasets['''train''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		DataLoader(
        tokenized_datasets['''validation''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)

    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

  UpperCAmelCase_   : Union[str, Any]   			= mocked_dataloaders  # noqa: F811



def 					_A			(__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,				__a							) == "1":
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		2
    # Initialize accelerator
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_      :  str    						=		config['''lr''']
    SCREAMING_SNAKE_CASE_      :  Any    						=		int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_      :  str    						=		int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		int(config['''batch_size''']							)

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

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

    set_seed(__a							)
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_dataloaders(__a    ,				__a							)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,				return_dict=__a							)

    # 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).
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model.to(accelerator.device							)

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		AdamW(params=model.parameters()    ,				lr=__a							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  Dict    						=		get_linear_schedule_with_warmup(
        optimizer=__a    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(__a							) * 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.
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		accelerator.prepare(
        __a    ,				__a    ,				__a    ,				__a    ,				__a							)

    # Now we train the model
    for epoch in range(__a							):
        model.train()
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  str    						=		outputs.loss
            SCREAMING_SNAKE_CASE_      :  int    						=		loss / gradient_accumulation_steps
            accelerator.backward(__a							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        SCREAMING_SNAKE_CASE_      :  Any    						=		0
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            with torch.no_grad():
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.gather((predictions, batch['''labels'''])							)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(__a							) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    SCREAMING_SNAKE_CASE_      :  Any    						=		predictions[: len(eval_dataloader.dataset							) - samples_seen]
                    SCREAMING_SNAKE_CASE_      :  int    						=		references[: len(eval_dataloader.dataset							) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=__a    ,				references=__a    ,				)

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'    ,				__a							)



def 					_A			() ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''    ,				type=__a    ,				default=__a    ,				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.'''							)
    SCREAMING_SNAKE_CASE_      :  int    						=		parser.parse_args()
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__a    ,				__a							)


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


UpperCAmelCase_   : Dict   			= logging.get_logger(__name__)

UpperCAmelCase_   : Tuple   			= """▁"""

UpperCAmelCase_   : Optional[Any]   			= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_   : str   			= {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_   : str   			= {
    """facebook/xglm-564M""": 2048,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      def __init__(						self     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Tuple="<s>"						,	lowercase_     :  Any="</s>"						,	lowercase_     :  Optional[int]="</s>"						,	lowercase_     :  List[Any]="<s>"						,	lowercase_     :  Union[str, Any]="<unk>"						,	lowercase_     :  Union[str, Any]="<pad>"						,	lowercase_     :  Optional[Dict[str, Any]] = None						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		7
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[F'<madeupword{i}>' for i in range(self.num_madeup_words)]

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''additional_special_tokens'''						,	[])
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1

          # Mimic fairseq token-to-id alignment for the first 4 token
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.sp_model)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
          self.fairseq_tokens_to_ids.update(lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.__dict__.copy()
          SCREAMING_SNAKE_CASE_      :  str    						=		None
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.sp_model.serialized_model_proto()
          return state
      def __setstate__(						self     :  Tuple						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		d

          # for backward compatibility
          if not hasattr(self						,	'''sp_model_kwargs'''):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None						,	lowercase_     :  bool = False):




          '''simple docstring'''


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

          if token_ids_a is None:
              return [1] + ([0] * len(lowercase_))
          return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          return self.sp_model.encode(lowercase_						,	out_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.sp_model.PieceToId(lowercase_)

          # 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 _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]):




          '''simple docstring'''


          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 _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(lowercase_).replace(lowercase_						,	''' ''').strip()
          return out_string



      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


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

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

          return (out_vocab_file,)


