


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


_a   :   Union[str, Any]												=   16
_a   :   Optional[Any]												=   32





def   _a		(lowercase__			:   Accelerator							,   lowercase__			:   int = 1_6						)			->			Union[str, Any]:






     """simple docstring"""
     __snake_case           =						AutoTokenizer.from_pretrained('bert-base-cased'						)
     __snake_case           =						load_dataset('glue'							,   'mrpc'						)

     def tokenize_function(lowercase__			:   Optional[Any]						):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case           =						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():
          __snake_case           =						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
     __snake_case           =						tokenized_datasets.rename_column('label'							,   'labels'						)

     def collate_fn(lowercase__			:   Optional[int]						):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __snake_case           =						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":
               __snake_case           =						1_6
          elif accelerator.mixed_precision != "no":
               __snake_case           =						8
          else:
               __snake_case           =						None

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

     # Instantiate dataloaders.
     __snake_case           =						DataLoader(
         tokenized_datasets['train']							,   shuffle=lowercase__							,   collate_fn=lowercase__							,   batch_size=lowercase__						)
     __snake_case           =						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   :   Optional[int]												=   mocked_dataloaders  # noqa: F811





def   _a		(lowercase__			:   str							,   lowercase__			:   Dict						)			->			Optional[Any]:






     """simple docstring"""
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'							,   lowercase__						) == "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(lowercase__						)
     __snake_case      ,       __snake_case           =						get_dataloaders(lowercase__							,   lowercase__						)
     # 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=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).
     __snake_case           =						model.to(accelerator.device						)

     # Instantiate optimizer
     __snake_case           =						AdamW(params=model.parameters()							,   lr=lowercase__						)

     # Instantiate scheduler
     __snake_case           =						get_linear_schedule_with_warmup(
         optimizer=lowercase__							,   num_warmup_steps=1_0_0							,   num_training_steps=(len(lowercase__						) * 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(
         lowercase__							,   lowercase__							,   lowercase__							,   lowercase__							,   lowercase__						)

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

          model.eval()
          __snake_case           =						0
          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():
                    __snake_case           =						model(**lowercase__						)
               __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(lowercase__						) - 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=lowercase__							,   references=lowercase__							,   )

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





def   _a		()			->			Union[str, Any]:






     """simple docstring"""
     __snake_case           =						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.'							,   )
     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': 4_2, 'batch_size': 1_6}
     training_function(lowercase__							,   lowercase__						)


if __name__ == "__main__":
       main()






"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							=     {
    'nielsr/canine-s': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__SCREAMING_SNAKE_CASE							=     1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__SCREAMING_SNAKE_CASE							=     0

__SCREAMING_SNAKE_CASE							=     0XE_0_0_0
__SCREAMING_SNAKE_CASE							=     0XE_0_0_1
__SCREAMING_SNAKE_CASE							=     0XE_0_0_2
__SCREAMING_SNAKE_CASE							=     0XE_0_0_3
__SCREAMING_SNAKE_CASE							=     0XE_0_0_4

# Maps special codepoints to human-readable names.
__SCREAMING_SNAKE_CASE							=     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__SCREAMING_SNAKE_CASE							=     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(							self :List[str]      ,  _lowerCamelCase :Optional[Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :List[str]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Any=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Optional[int]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Tuple=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Union[str, Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Union[str, Any]=2_048      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else bos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else eos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else sep_token
      UpperCamelCase_     :					Tuple   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else cls_token
      UpperCamelCase_     :					Optional[int]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase_     :					Dict   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  add_prefix_space=_lowerCamelCase      ,  model_max_length=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      # Creates a mapping for looking up the IDs of special symbols.
      UpperCamelCase_     :					Dict[str, int]   	={}
      for codepoint, name in SPECIAL_CODEPOINTS.items():
       UpperCamelCase_     :					List[str]   	=codepoint

      # Creates a mapping for looking up the string forms of special symbol IDs.
      UpperCamelCase_     :					Dict[int, str]   	={
          codepoint: name for name, codepoint in self._special_codepoints.items()
      }

      UpperCamelCase_     :					str   	=UNICODE_VOCAB_SIZE
      UpperCamelCase_     :					Any   	=len(self._special_codepoints   )



     @property
     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self._unicode_vocab_size



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return list(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      try:
       return ord(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid token: \'{token}\''''   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      try:
       if index in SPECIAL_CODEPOINTS:
        return SPECIAL_CODEPOINTS[index]
       return chr(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid id: {index}'''   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return "".join(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=[self.sep_token_id]
      UpperCamelCase_     :					Tuple   	=[self.cls_token_id]

      UpperCamelCase_     :					List[str]   	=cls + token_ids_a + sep
      if token_ids_a is not None:
       result += token_ids_a + sep
      return result



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



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

      UpperCamelCase_     :					int   	=[1] + ([0] * len(_lowerCamelCase   )) + [1]
      if token_ids_a is not None:
       result += ([0] * len(_lowerCamelCase   )) + [1]
      return result



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







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=[self.sep_token_id]
      UpperCamelCase_     :					Optional[int]   	=[self.cls_token_id]

      UpperCamelCase_     :					Dict   	=len(cls + token_ids_a + sep   ) * [0]
      if token_ids_a is not None:
       result += len(token_ids_a + sep   ) * [1]
      return result




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      return ()
