





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE_		  =			1_6
SCREAMING_SNAKE_CASE_		  =			3_2



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      if args.with_tracking:
            SCREAMING_SNAKE_CASE						=		Accelerator(
                cpu=args.cpu					,		mixed_precision=args.mixed_precision					,		log_with="""all"""					,		project_dir=args.project_dir      )
      else:
            SCREAMING_SNAKE_CASE						=		Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision      )

      if hasattr(args.checkpointing_steps					,		"""isdigit"""      ):
            if args.checkpointing_steps == "epoch":
                  SCREAMING_SNAKE_CASE						=		args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  SCREAMING_SNAKE_CASE						=		int(args.checkpointing_steps      )
            else:
                  raise ValueError(
                      F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
      else:
            SCREAMING_SNAKE_CASE						=		None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE						=		config['''lr''']
      SCREAMING_SNAKE_CASE						=		int(config["""num_epochs"""]      )
      SCREAMING_SNAKE_CASE						=		int(config["""seed"""]      )
      SCREAMING_SNAKE_CASE						=		int(config["""batch_size"""]      )

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            SCREAMING_SNAKE_CASE						=		os.path.split(_UpperCAmelCase      )[-1].split("""."""      )[0]
            accelerator.init_trackers(_UpperCAmelCase					,		_UpperCAmelCase      )

      SCREAMING_SNAKE_CASE						=		AutoTokenizer.from_pretrained("""bert-base-cased"""      )
      SCREAMING_SNAKE_CASE						=		load_dataset("""glue"""					,		"""mrpc"""      )
      SCREAMING_SNAKE_CASE						=		evaluate.load("""glue"""					,		"""mrpc"""      )

      def tokenize_function(_SCREAMING_SNAKE_CASE      ):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE						=		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():
            SCREAMING_SNAKE_CASE						=		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
      SCREAMING_SNAKE_CASE						=		tokenized_datasets.rename_column("""label"""					,		"""labels"""      )

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

      def collate_fn(_SCREAMING_SNAKE_CASE      ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE						=		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						=		16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE						=		8
            else:
                  SCREAMING_SNAKE_CASE						=		None

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

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

      set_seed(_UpperCAmelCase      )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE						=		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).
      SCREAMING_SNAKE_CASE						=		model.to(accelerator.device      )

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

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE						=		get_linear_schedule_with_warmup(
          optimizer=_UpperCAmelCase					,		num_warmup_steps=1_00					,		num_training_steps=(len(_UpperCAmelCase      ) * 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						=		accelerator.prepare(
          _UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase      )

      # We need to keep track of how many total steps we have iterated over
      SCREAMING_SNAKE_CASE						=		0
      # We also need to keep track of the stating epoch so files are named properly
      SCREAMING_SNAKE_CASE						=		0

      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
            if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                  accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""      )
                  accelerator.load_state(args.resume_from_checkpoint      )
                  SCREAMING_SNAKE_CASE						=		os.path.basename(args.resume_from_checkpoint      )
            else:
                  # Get the most recent checkpoint
                  SCREAMING_SNAKE_CASE						=		[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime      )
                  SCREAMING_SNAKE_CASE						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            SCREAMING_SNAKE_CASE						=		os.path.splitext(_UpperCAmelCase      )[0]

            if "epoch" in training_difference:
                  SCREAMING_SNAKE_CASE						=		int(training_difference.replace("""epoch_"""					,		""""""      )      ) + 1
                  SCREAMING_SNAKE_CASE						=		None
            else:
                  SCREAMING_SNAKE_CASE						=		int(training_difference.replace("""step_"""					,		""""""      )      )
                  SCREAMING_SNAKE_CASE						=		resume_step // len(_UpperCAmelCase      )
                  resume_step -= starting_epoch * len(_UpperCAmelCase      )

    # Now we train the model
      for epoch in range(_UpperCAmelCase					,		_UpperCAmelCase      ):
            model.train()
            if args.with_tracking:
                  SCREAMING_SNAKE_CASE						=		0
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  SCREAMING_SNAKE_CASE						=		accelerator.skip_first_batches(_UpperCAmelCase					,		_UpperCAmelCase      )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  SCREAMING_SNAKE_CASE						=		train_dataloader
            for step, batch in enumerate(_UpperCAmelCase      ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device      )
                  SCREAMING_SNAKE_CASE						=		model(**_UpperCAmelCase      )
                  SCREAMING_SNAKE_CASE						=		outputs.loss
                  SCREAMING_SNAKE_CASE						=		loss / gradient_accumulation_steps
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(_UpperCAmelCase      )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

                  if isinstance(_UpperCAmelCase					,		_UpperCAmelCase      ):
                        SCREAMING_SNAKE_CASE						=		F"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    SCREAMING_SNAKE_CASE						=		os.path.join(args.output_dir					,		_UpperCAmelCase      )
                              accelerator.save_state(_UpperCAmelCase      )

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

            SCREAMING_SNAKE_CASE						=		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""					,		_UpperCAmelCase      )
            if args.with_tracking:
                  accelerator.log(
                      {
                          """accuracy""": eval_metric["""accuracy"""],
                          """f1""": eval_metric["""f1"""],
                          """train_loss""": total_loss.item() / len(_UpperCAmelCase      ),
                          """epoch""": epoch,
                      }					,		step=_UpperCAmelCase					,		)

            if checkpointing_steps == "epoch":
                  SCREAMING_SNAKE_CASE						=		F"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        SCREAMING_SNAKE_CASE						=		os.path.join(args.output_dir					,		_UpperCAmelCase      )
                  accelerator.save_state(_UpperCAmelCase      )

      if args.with_tracking:
            accelerator.end_training()



def   __lowercase  ( )					->  List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		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."""					,		)
      parser.add_argument("""--cpu"""					,		action="""store_true"""					,		help="""If passed, will train on the CPU."""      )
      parser.add_argument(
          """--checkpointing_steps"""					,		type=_UpperCAmelCase					,		default=_UpperCAmelCase					,		help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""					,		)
      parser.add_argument(
          """--resume_from_checkpoint"""					,		type=_UpperCAmelCase					,		default=_UpperCAmelCase					,		help="""If the training should continue from a checkpoint folder."""					,		)
      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(
          """--output_dir"""					,		type=_UpperCAmelCase					,		default="""."""					,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					,		)
      parser.add_argument(
          """--project_dir"""					,		type=_UpperCAmelCase					,		default="""logs"""					,		help="""Location on where to store experiment tracking logs` and relevent project information"""					,		)
      SCREAMING_SNAKE_CASE						=		parser.parse_args()
      SCREAMING_SNAKE_CASE						=		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(_UpperCAmelCase					,		_UpperCAmelCase      )


if __name__ == "__main__":
      main()








"""simple docstring"""

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


_lowerCamelCase	:      Optional[Any]   =							logging.get_logger(__name__)

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







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



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		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__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[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]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''							)

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

					if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
						copyfile(self.vocab_file   ,  _lowerCamelCase							)

					return (out_vocab_file,)





