





'''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
#
########################################################################


__magic_name__   :						Optional[Any]    =			16
__magic_name__   :						int    =			32




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 16       ):







  '''simple docstring'''



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

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

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

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

  # Instantiate dataloaders.
  _snake_case     		=				DataLoader(
      tokenized_datasets["train"]					,	shuffle=SCREAMING_SNAKE_CASE__					,	collate_fn=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				DataLoader(
      tokenized_datasets["validation"]					,	shuffle=SCREAMING_SNAKE_CASE__					,	collate_fn=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  _snake_case     		=				evaluate.load("glue"					,	"mrpc"       )

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

  set_seed(SCREAMING_SNAKE_CASE__       )
  _snake_case		,		_snake_case     		=				get_dataloaders(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _snake_case     		=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,	return_dict=SCREAMING_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).
  _snake_case     		=				model.to(accelerator.device       )

  # Instantiate optimizer
  _snake_case     		=				AdamW(params=model.parameters()					,	lr=SCREAMING_SNAKE_CASE__       )

  # Instantiate scheduler
  _snake_case     		=				get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE__					,	num_warmup_steps=1_00					,	num_training_steps=(len(SCREAMING_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.
  _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case     		=				accelerator.prepare(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

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

    model.eval()
    _snake_case     		=				0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device       )
      with torch.no_grad():
        _snake_case     		=				model(**SCREAMING_SNAKE_CASE__       )
      _snake_case     		=				outputs.logits.argmax(dim=-1       )
      _snake_case		,		_snake_case     		=				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(SCREAMING_SNAKE_CASE__       ) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          _snake_case     		=				predictions[: len(eval_dataloader.dataset       ) - samples_seen]
          _snake_case     		=				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=SCREAMING_SNAKE_CASE__					,	references=SCREAMING_SNAKE_CASE__					,	)

    _snake_case     		=				metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''					,	SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser(description="Simple example of training script."       )
  parser.add_argument(
      "--mixed_precision"					,	type=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_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."       )
  _snake_case     		=				parser.parse_args()
  _snake_case     		=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE__					,	SCREAMING_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


__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)

__magic_name__   :						str    =			"""▁"""

__magic_name__   :						int    =			{"""vocab_file""": """sentencepiece.bpe.model"""}

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

__magic_name__   :						Union[str, Any]    =			{
    """facebook/xglm-564M""": 2_048,
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






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

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				{} if sp_model_kwargs is None else sp_model_kwargs

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

       _snake_case     		=				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=lowerCamelCase						,   eos_token=lowerCamelCase						,   unk_token=lowerCamelCase						,   sep_token=lowerCamelCase						,   cls_token=lowerCamelCase						,   pad_token=lowerCamelCase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowerCamelCase						,   )

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(lowerCamelCase	)	)
       _snake_case     		=				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
       _snake_case     		=				1

       # Mimic fairseq token-to-id alignment for the first 4 token
       _snake_case     		=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

       _snake_case     		=				len(self.sp_model	)
       _snake_case     		=				{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
       self.fairseq_tokens_to_ids.update(lowerCamelCase	)

       _snake_case     		=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(					self	):
       _snake_case     		=				self.__dict__.copy()
       _snake_case     		=				None
       _snake_case     		=				self.sp_model.serialized_model_proto()
       return state

     def __setstate__(					self						,   lowerCamelCase	):
       _snake_case     		=				d

       # for backward compatibility
       if not hasattr(self						,   "sp_model_kwargs"	):
         _snake_case     		=				{}

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[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 	UpperCamelCase(					self	):
       return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words

     def 	UpperCamelCase(					self	):
       _snake_case     		=				{self.convert_ids_to_tokens(lowerCamelCase	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.sp_model.encode(lowerCamelCase						,   out_type=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       _snake_case     		=				self.sp_model.PieceToId(lowerCamelCase	)

       # 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 	UpperCamelCase(					self						,   lowerCamelCase	):
       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 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"".join(lowerCamelCase	).replace(lowerCamelCase						,   " "	).strip()
       return out_string






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if not os.path.isdir(lowerCamelCase	):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
         return
       _snake_case     		=				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	) and os.path.isfile(self.vocab_file	):
         copyfile(self.vocab_file						,   lowerCamelCase	)
       elif not os.path.isfile(self.vocab_file	):
         with open(lowerCamelCase						,   "wb"	) as fi:
           _snake_case     		=				self.sp_model.serialized_model_proto()
           fi.write(lowerCamelCase	)

       return (out_vocab_file,)


