






'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCamelCase__			=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 							_UpperCAmelCase    :
    __lowerCamelCase: str									=     field(
        default=snake_case					,       metadata={'help': 'Model type selected in the list: ' + ', '.join(snake_case      )}      )
    __lowerCamelCase: str									=     field(
        default=snake_case					,       metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}      )
    __lowerCamelCase: int									=     field(
        default=128					,       metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }					,       )
    __lowerCamelCase: int									=     field(
        default=128					,       metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}					,       )
    __lowerCamelCase: int									=     field(
        default=64					,       metadata={
            'help': (
                'The maximum number of tokens for the question. Questions longer than this will '
                'be truncated to this length.'
            )
        }					,       )
    __lowerCamelCase: int									=     field(
        default=30					,       metadata={
            'help': (
                'The maximum length of an answer that can be generated. This is needed because the start '
                'and end predictions are not conditioned on one another.'
            )
        }					,       )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}      )
    __lowerCamelCase: float									=     field(
        default=0.0					,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
    __lowerCamelCase: int									=     field(
        default=20					,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
    __lowerCamelCase: int									=     field(
        default=0					,       metadata={
            'help': (
                'language id of input for language-specific xlm models (see'
                ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
            )
        }					,       )
    __lowerCamelCase: int									=     field(default=1					,       metadata={'help': 'multiple threads for converting example to features'}      )


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: List[str]									=     'train'
    __lowerCamelCase: Any									=     'dev'


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: SquadDataTrainingArguments
    __lowerCamelCase: List[SquadFeatures]
    __lowerCamelCase: Split
    __lowerCamelCase: bool




    def __init__(      self	:							int	,  a	:							SquadDataTrainingArguments	,  a	:							PreTrainedTokenizer	,  a	:							Optional[int] = None	,  a	:							Union[str, Split] = Split.train	,  a	:							Optional[bool] = False	,  a	:							Optional[str] = None	,  a	:							Optional[str] = "pt"	,  ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  args
       lowercase_						:     List[str]							     =  is_language_sensitive
       lowercase_						:     Optional[Any]							     =  SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
       if isinstance(a	,  a      ):
          try:
             lowercase_						:     int							     =  Split[mode]
          except KeyError:
             raise KeyError("mode is not a valid split name"      )
       lowercase_						:     Any							     =  mode
       # Load data features from cache or dataset file
       lowercase_						:     Optional[Any]							     =  "v2" if args.version_2_with_negative else "v1"
       lowercase_						:     str							     =  os.path.join(
           cache_dir if cache_dir is not None else args.data_dir	,  f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""	,  )

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       lowercase_						:     Tuple							     =  cached_features_file + ".lock"
       with FileLock(a      ):
          if os.path.exists(a      ) and not args.overwrite_cache:
             lowercase_						:     str							     =  time.time()
             lowercase_						:     Optional[int]							     =  torch.load(a      )

             # Legacy cache files have only features, while new cache files
             # will have dataset and examples also.
             lowercase_						:     Optional[int]							     =  self.old_features["features"]
             lowercase_						:     int							     =  self.old_features.get("dataset"	,  a      )
             lowercase_						:     List[str]							     =  self.old_features.get("examples"	,  a      )
             logger.info(
                 f"""Loading features from cached file {cached_features_file} [took %.3f s]"""	,  time.time() - start      )

             if self.dataset is None or self.examples is None:
                logger.warning(
                    f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
                    " future run"      )
          else:
             if mode == Split.dev:
                lowercase_						:     int							     =  self.processor.get_dev_examples(args.data_dir      )
             else:
                lowercase_						:     Dict							     =  self.processor.get_train_examples(args.data_dir      )

             lowercase_     ,		lowercase_						:     Optional[int]							     =  squad_convert_examples_to_features(
                 examples=self.examples	,  tokenizer=a	,  max_seq_length=args.max_seq_length	,  doc_stride=args.doc_stride	,  max_query_length=args.max_query_length	,  is_training=mode == Split.train	,  threads=args.threads	,  return_dataset=a	,  )

             lowercase_						:     Dict							     =  time.time()
             torch.save(
                 {"features": self.features, "dataset": self.dataset, "examples": self.examples}	,  a	,  )
             # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
             logger.info(
                 f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""      )




    def __len__(      self	:							Dict      ):






       '''simple docstring'''






       return len(self.features      )





    def __getitem__(      self	:							List[Any]	,  a	:							str      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  self.features[i]

       lowercase_						:     str							     =  torch.tensor(feature.input_ids	,  dtype=torch.long      )
       lowercase_						:     str							     =  torch.tensor(feature.attention_mask	,  dtype=torch.long      )
       lowercase_						:     Any							     =  torch.tensor(feature.token_type_ids	,  dtype=torch.long      )
       lowercase_						:     Any							     =  torch.tensor(feature.cls_index	,  dtype=torch.long      )
       lowercase_						:     Tuple							     =  torch.tensor(feature.p_mask	,  dtype=torch.float      )
       lowercase_						:     Any							     =  torch.tensor(feature.is_impossible	,  dtype=torch.float      )

       lowercase_						:     Union[str, Any]							     =  {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "token_type_ids": token_type_ids,
       }

       if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
          del inputs["token_type_ids"]

       if self.args.model_type in ["xlnet", "xlm"]:
          inputs.update({"cls_index": cls_index, "p_mask": p_mask}      )
          if self.args.version_2_with_negative:
             inputs.update({"is_impossible": is_impossible}      )
          if self.is_language_sensitive:
             inputs.update({"langs": (torch.ones(input_ids.shape	,  dtype=torch.intaa      ) * self.args.lang_id)}      )

       if self.mode == Split.train:
          lowercase_						:     List[str]							     =  torch.tensor(feature.start_position	,  dtype=torch.long      )
          lowercase_						:     Any							     =  torch.tensor(feature.end_position	,  dtype=torch.long      )
          inputs.update({"start_positions": start_positions, "end_positions": end_positions}      )

       return inputs












'''simple docstring'''
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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__			=      16
UpperCamelCase__			=      32




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16				):


   """simple docstring"""




   lowercase_						:     int							     =  AutoTokenizer.from_pretrained("bert-base-cased"				)
   lowercase_						:     Optional[Any]							     =  load_dataset("glue"    ,		"mrpc"				)

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

   def collate_fn(_UpperCamelCase				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase_						:     List[str]							     =  128 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_						:     List[str]							     =  16
      elif accelerator.mixed_precision != "no":
         lowercase_						:     Tuple							     =  8
      else:
         lowercase_						:     Dict							     =  None

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

   # Instantiate dataloaders.
   lowercase_						:     Optional[int]							     =  DataLoader(
       tokenized_datasets["train"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)
   lowercase_						:     str							     =  DataLoader(
       tokenized_datasets["validation"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)

   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__			=      mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




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

   lowercase_						:     List[Any]							     =  evaluate.load("glue"    ,		"mrpc"				)

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=_UpperCamelCase				)
   def inner_training_loop(_UpperCamelCase				):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(_UpperCamelCase				)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase_						:     List[str]							     =  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).
      lowercase_						:     List[Any]							     =  model.to(accelerator.device				)

      # Instantiate optimizer
      lowercase_						:     Tuple							     =  AdamW(params=model.parameters()    ,		lr=_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     Any							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase				)

      # Instantiate scheduler
      lowercase_						:     Union[str, Any]							     =  get_linear_schedule_with_warmup(
          optimizer=_UpperCamelCase    ,		num_warmup_steps=100    ,		num_training_steps=(len(_UpperCamelCase				) * num_epochs)    ,		)

      # 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_						:     Optional[int]							     =  accelerator.prepare(
          _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

      # Now we train the model
      for epoch in range(_UpperCamelCase				):
         model.train()
         for step, batch in enumerate(_UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            lowercase_						:     Optional[Any]							     =  model(**_UpperCamelCase				)
            lowercase_						:     List[Any]							     =  outputs.loss
            accelerator.backward(_UpperCamelCase				)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         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():
               lowercase_						:     Optional[int]							     =  model(**_UpperCamelCase				)
            lowercase_						:     Union[str, Any]							     =  outputs.logits.argmax(dim=-1				)
            lowercase_     ,		lowercase_						:     str							     =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=_UpperCamelCase    ,		references=_UpperCamelCase    ,		)

         lowercase_						:     int							     =  metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""    ,		_UpperCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  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."				)
   lowercase_						:     int							     =  parser.parse_args()
   lowercase_						:     Optional[Any]							     =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()




