'''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 the checkpointing capability,
# 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						:    str     					=					16
_UpperCAmelCase						:    str     					=					32





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Accelerator      ,		__snake_case       : int = 1_6							):
     _A								=     AutoTokenizer.from_pretrained('bert-base-cased'							)
     _A								=     load_dataset('glue'      ,		'mrpc'							)

     def tokenize_function(__snake_case       : Union[str, Any]							):
          # max_length=None => use the model max length (it's actually the default)
          _A								=     tokenizer(examples['sentence1']      ,		examples['sentence2']      ,		truncation=__snake_case      ,		max_length=__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():
          _A								=     datasets.map(
              __snake_case      ,		batched=__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
     _A								=     tokenized_datasets.rename_column('label'      ,		'labels'							)

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

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

     # Instantiate dataloaders.
     _A								=     DataLoader(
         tokenized_datasets['train']      ,		shuffle=__snake_case      ,		collate_fn=__snake_case      ,		batch_size=__snake_case							)
     _A								=     DataLoader(
         tokenized_datasets['validation']      ,		shuffle=__snake_case      ,		collate_fn=__snake_case      ,		batch_size=__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

       _UpperCAmelCase						:    List[str]     					=					mocked_dataloaders  # noqa: F811





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

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps      ,		'isdigit'							):
          if args.checkpointing_steps == "epoch":
               _A								=     args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _A								=     int(args.checkpointing_steps							)
          else:
               raise ValueError(
                   F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'							)
     else:
          _A								=     None

     set_seed(__snake_case							)

     _A		,					_A								=     get_dataloaders(__snake_case      ,		__snake_case							)
     _A								=     evaluate.load('glue'      ,		'mrpc'							)

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

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _A								=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,		return_dict=__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).
     _A								=     model.to(accelerator.device							)

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

     # Instantiate scheduler
     _A								=     get_linear_schedule_with_warmup(
         optimizer=__snake_case      ,		num_warmup_steps=1_0_0      ,		num_training_steps=(len(__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.
     _A		,					_A		,					_A		,					_A		,					_A								=     accelerator.prepare(
         __snake_case      ,		__snake_case      ,		__snake_case      ,		__snake_case      ,		__snake_case							)

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

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     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							)
               _A								=     os.path.basename(args.resume_from_checkpoint							)
          else:
               # Get the most recent checkpoint
               _A								=     [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
               dirs.sort(key=os.path.getctime							)
               _A								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _A								=     os.path.splitext(__snake_case							)[0]

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

    # Now we train the model
     for epoch in range(__snake_case      ,		__snake_case							):
          model.train()
          # New Code #
          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
               _A								=     accelerator.skip_first_batches(__snake_case      ,		__snake_case							)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               _A								=     train_dataloader
          for step, batch in enumerate(__snake_case							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               _A								=     model(**__snake_case							)
               _A								=     outputs.loss
               _A								=     loss / gradient_accumulation_steps
               accelerator.backward(__snake_case							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(__snake_case      ,		__snake_case							):
                    _A								=     F'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _A								=     os.path.join(args.output_dir      ,		__snake_case							)
                         accelerator.save_state(__snake_case							)

          model.eval()
          for step, batch in enumerate(__snake_case							):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device							)
               with torch.no_grad():
                    _A								=     model(**__snake_case							)
               _A								=     outputs.logits.argmax(dim=-1							)
               _A		,					_A								=     accelerator.gather_for_metrics((predictions, batch['labels'])							)
               metric.add_batch(
                   predictions=__snake_case      ,		references=__snake_case      ,		)

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

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               _A								=     F'epoch_{epoch}'
               if args.output_dir is not None:
                    _A								=     os.path.join(args.output_dir      ,		__snake_case							)
               accelerator.save_state(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			):
     _A								=     argparse.ArgumentParser(description='Simple example of training script.'							)
     parser.add_argument(
         '--mixed_precision'      ,		type=__snake_case      ,		default=__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.'							)
     parser.add_argument(
         '--checkpointing_steps'      ,		type=__snake_case      ,		default=__snake_case      ,		help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'      ,		)
     parser.add_argument(
         '--output_dir'      ,		type=__snake_case      ,		default='.'      ,		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,		)
     parser.add_argument(
         '--resume_from_checkpoint'      ,		type=__snake_case      ,		default=__snake_case      ,		help='If the training should continue from a checkpoint folder.'      ,		)
     _A								=     parser.parse_args()
     _A								=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
     training_function(__snake_case      ,		__snake_case							)


if __name__ == "__main__":
       main()




'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        # Build iterable dataset
        if self.streaming:
             _A								=     self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
             _A								=     None
             _A								=     None
             _A								=     None
             _A								=     None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     batch.to_pandas().to_csv(
            path_or_buf=UpperCamelCase__,   header=header if (offset == 0) else False,   index=UpperCamelCase__,   **UpperCamelCase__    )
        return csv_str.encode(self.encoding    )

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,   len(self.dataset    ),   self.batch_size    ),   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     len(self.dataset    ), self.batch_size
             with multiprocessing.Pool(self.num_proc    ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv,   [(offset, header, index, to_csv_kwargs) for offset in range(0,   UpperCamelCase__,   UpperCamelCase__    )],   ),   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                       written += file_obj.write(UpperCamelCase__    )

        return written



