





'''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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__							:							int			=16
a__							:							Any			=32
def     lowercase__       (       __lowercase						:  List[str]    ,  __lowercase						:  int					)			->				List[str]:



							"""simple docstring"""






							if args.with_tracking:
														__UpperCamelCase	=  Accelerator(
														    cpu=args.cpu    ,  mixed_precision=args.mixed_precision    ,  log_with='all'    ,  project_dir=args.project_dir					)
							else:
														__UpperCamelCase	=  Accelerator(cpu=args.cpu    ,  mixed_precision=args.mixed_precision					)

							if hasattr(args.checkpointing_steps    ,  'isdigit'					):
														if args.checkpointing_steps == "epoch":
																					__UpperCamelCase	=  args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					__UpperCamelCase	=  int(args.checkpointing_steps					)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
							else:
														__UpperCamelCase	=  None
							# 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']					)

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														__UpperCamelCase	=  os.path.split(SCREAMING_SNAKE_CASE__					)[-1].split('.'					)[0]
														accelerator.init_trackers(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)

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

							def tokenize_function(__lowercase						:  List[str]					):
														# max_length=None => use the model max length (it's actually the default)
														__UpperCamelCase	=  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():
														__UpperCamelCase	=  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
							__UpperCamelCase	=  tokenized_datasets.rename_column('label'    ,  'labels'					)

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

							def collate_fn(__lowercase						:  Any					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__UpperCamelCase	=  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":
																					__UpperCamelCase	=  16
														elif accelerator.mixed_precision != "no":
																					__UpperCamelCase	=  8
														else:
																					__UpperCamelCase	=  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.
							__UpperCamelCase	=  DataLoader(
							    tokenized_datasets['train']    ,  shuffle=SCREAMING_SNAKE_CASE__    ,  collate_fn=SCREAMING_SNAKE_CASE__    ,  batch_size=SCREAMING_SNAKE_CASE__					)
							__UpperCamelCase	=  DataLoader(
							    tokenized_datasets['validation']    ,  shuffle=SCREAMING_SNAKE_CASE__    ,  collate_fn=SCREAMING_SNAKE_CASE__    ,  batch_size=SCREAMING_SNAKE_CASE__					)

							set_seed(SCREAMING_SNAKE_CASE__					)

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

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

							# Instantiate scheduler
							__UpperCamelCase	=  get_linear_schedule_with_warmup(
							    optimizer=SCREAMING_SNAKE_CASE__    ,  num_warmup_steps=100    ,  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.
							__UpperCamelCase	=  accelerator.prepare(
							    SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)

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

							# Potentially load in the weights and states from a previous save
							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					)
																					__UpperCamelCase	=  os.path.basename(args.resume_from_checkpoint					)
														else:
																					# Get the most recent checkpoint
																					__UpperCamelCase	=  [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
																					dirs.sort(key=os.path.getctime					)
																					__UpperCamelCase	=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														__UpperCamelCase	=  os.path.splitext(SCREAMING_SNAKE_CASE__					)[0]

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

    # Now we train the model
							for epoch in range(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					):
														model.train()
														if args.with_tracking:
																					__UpperCamelCase	=  0
														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
																					__UpperCamelCase	=  accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					__UpperCamelCase	=  train_dataloader
														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					)
																					__UpperCamelCase	=  model(**SCREAMING_SNAKE_CASE__					)
																					__UpperCamelCase	=  outputs.loss
																					__UpperCamelCase	=  loss / gradient_accumulation_steps
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(SCREAMING_SNAKE_CASE__					)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

																					if isinstance(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					):
																												__UpperCamelCase	=  F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										__UpperCamelCase	=  os.path.join(args.output_dir    ,  SCREAMING_SNAKE_CASE__					)
																																			accelerator.save_state(SCREAMING_SNAKE_CASE__					)

														model.eval()
														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():
																												__UpperCamelCase	=  model(**SCREAMING_SNAKE_CASE__					)
																					__UpperCamelCase	=  outputs.logits.argmax(dim=-1					)
																					__UpperCamelCase	=  accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=SCREAMING_SNAKE_CASE__    ,  references=SCREAMING_SNAKE_CASE__    ,  )

														__UpperCamelCase	=  metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''    ,  SCREAMING_SNAKE_CASE__					)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        'accuracy': eval_metric['accuracy'],
																					        'f1': eval_metric['f1'],
																					        'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE__					),
																					        'epoch': epoch,
																					    }    ,  step=SCREAMING_SNAKE_CASE__    ,  )

														if checkpointing_steps == "epoch":
																					__UpperCamelCase	=  F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												__UpperCamelCase	=  os.path.join(args.output_dir    ,  SCREAMING_SNAKE_CASE__					)
																					accelerator.save_state(SCREAMING_SNAKE_CASE__					)

							if args.with_tracking:
														accelerator.end_training()
def     lowercase__       (       )			->				List[Any]:



							"""simple docstring"""






							__UpperCamelCase	=  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.'					)
							parser.add_argument(
							    '--checkpointing_steps'    ,  type=SCREAMING_SNAKE_CASE__    ,  default=SCREAMING_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(
							    '--resume_from_checkpoint'    ,  type=SCREAMING_SNAKE_CASE__    ,  default=SCREAMING_SNAKE_CASE__    ,  help='If the training should continue from a checkpoint folder.'    ,  )
							parser.add_argument(
							    '--with_tracking'    ,  action='store_true'    ,  help='Whether to load in all available experiment trackers from the environment and use them for logging.'    ,  )
							parser.add_argument(
							    '--output_dir'    ,  type=SCREAMING_SNAKE_CASE__    ,  default='.'    ,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'    ,  )
							parser.add_argument(
							    '--project_dir'    ,  type=SCREAMING_SNAKE_CASE__    ,  default='logs'    ,  help='Location on where to store experiment tracking logs` and relevent project information'    ,  )
							__UpperCamelCase	=  parser.parse_args()
							__UpperCamelCase	=  {'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 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
