





'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

a__							:							str			=32
def     lowercase__       (       __lowercase						:  Accelerator    ,  __lowercase						:  int = 16					)			->				Optional[int]:



							"""simple docstring"""






							__UpperCamelCase	=  AutoTokenizer.from_pretrained('bert-base-cased'					)
							__UpperCamelCase	=  load_dataset('glue'    ,  'mrpc'					)

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

							def collate_fn(__lowercase						:  List[str]					):
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					__UpperCamelCase	=  16
														elif accelerator.mixed_precision != "no":
																					__UpperCamelCase	=  8
														else:
																					__UpperCamelCase	=  None

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

							# Instantiate dataloaders.
							__UpperCamelCase	=  DataLoader(
							    tokenized_datasets['train']    ,  shuffle=_lowerCamelCase    ,  collate_fn=_lowerCamelCase    ,  batch_size=_lowerCamelCase					)
							__UpperCamelCase	=  DataLoader(
							    tokenized_datasets['validation']    ,  shuffle=_lowerCamelCase    ,  collate_fn=_lowerCamelCase    ,  batch_size=_lowerCamelCase					)

							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__							:							List[str]			=mocked_dataloaders  # noqa: F811
def     lowercase__       (       __lowercase						:  Any    ,  __lowercase						:  List[Any]					)			->				Optional[int]:



							"""simple docstring"""






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

							__UpperCamelCase	=  evaluate.load('glue'    ,  'mrpc'					)

							# New Code #
							# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
							# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
							# half each time. From this, we can calculate the number of gradient accumulation steps needed
							# and modify the Accelerator object as a result
							@find_executable_batch_size(starting_batch_size=int(_lowerCamelCase					)					)
							def inner_training_loop(__lowercase						:  str					):
														# Since we need to modify the outside accelerator object, we need to bring it
														# to the local scope
														nonlocal accelerator

														# We can calculate the number of gradient accumulation steps based on the current
														# batch size vs the starting batch size
														__UpperCamelCase	=  observed_batch_size // batch_size

														# And then set it in the Accelerator directly:
														__UpperCamelCase	=  num_gradient_accumulation_steps

														# Next we need to free all of the stored model references in the Accelerator each time
														accelerator.free_memory()

														# And set the seed so our results are reproducable each reset
														set_seed(_lowerCamelCase					)

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

														# 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).
														__UpperCamelCase	=  model.to(accelerator.device					)

														# Instantiate optimizer
														__UpperCamelCase	=  AdamW(params=model.parameters()    ,  lr=_lowerCamelCase					)
														__UpperCamelCase	,						__UpperCamelCase	=  get_dataloaders(_lowerCamelCase    ,  _lowerCamelCase					)

														# Instantiate scheduler
														__UpperCamelCase	=  get_linear_schedule_with_warmup(
														    optimizer=_lowerCamelCase    ,  num_warmup_steps=100    ,  num_training_steps=(len(_lowerCamelCase					) * 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.
														__UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  accelerator.prepare(
														    _lowerCamelCase    ,  _lowerCamelCase    ,  _lowerCamelCase    ,  _lowerCamelCase    ,  _lowerCamelCase					)

														# Now we train the model
														for epoch in range(_lowerCamelCase					):
																					model.train()
																					for step, batch in enumerate(_lowerCamelCase					):
																												# And perform gradient accumulation
																												with accelerator.accumulate(_lowerCamelCase					):
																																			# We could avoid this line since we set the accelerator with `device_placement=True`.
																																			batch.to(accelerator.device					)
																																			__UpperCamelCase	=  model(**_lowerCamelCase					)
																																			__UpperCamelCase	=  outputs.loss
																																			accelerator.backward(_lowerCamelCase					)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

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

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

    # 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     lowercase__       (       )			->				str:



							"""simple docstring"""






							__UpperCamelCase	=  argparse.ArgumentParser(description='Simple example of training script.'					)
							parser.add_argument(
							    '--mixed_precision'    ,  type=_lowerCamelCase    ,  default=_lowerCamelCase    ,  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.'					)
							__UpperCamelCase	=  parser.parse_args()
							# New Code #
							# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
							__UpperCamelCase	=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
							training_function(_lowerCamelCase    ,  _lowerCamelCase					)


if __name__ == "__main__":
				main()







'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


a__							:							Optional[Any]			=datasets.utils.logging.get_logger(__name__)

@dataclass
class 					snake_case					(   datasets.BuilderConfig	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[datasets.Features]      		=None






class 					snake_case					(   datasets.ArrowBasedBuilder	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=ArrowConfig






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return datasets.DatasetInfo(features=self.config.features     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Dict     ):
        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
        __UpperCamelCase	=  dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__A							,     (str, list, tuple)     ):
               __UpperCamelCase	=  data_files
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={'files': files}     )]
        __UpperCamelCase	=  []
        for split_name, files in data_files.items():
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(__A     ):
                             with open(__A							,     'rb'     ) as f:
                                    __UpperCamelCase	=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__A     ).schema     )
                             break
               splits.append(datasets.SplitGenerator(name=__A							,     gen_kwargs={'files': files}     )     )
        return splits






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							pa.Table     ):
        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __UpperCamelCase	=  table_cast(__A							,     self.info.features.arrow_schema     )
        return pa_table






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]     ):
        for file_idx, file in enumerate(itertools.chain.from_iterable(__A     )     ):
               with open(__A							,     'rb'     ) as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__A     )     ):
                                    __UpperCamelCase	=  pa.Table.from_batches([record_batch]     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield f'''{file_idx}_{batch_idx}''', self._cast_table(__A     )
                      except ValueError as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(__A     )}: {e}'''     )
                             raise
