

'''simple docstring'''

import argparse

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)
#
# 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
#
########################################################################


lowercase__         =      16
lowercase__         =      32




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_ = 16     ):
 UpperCAmelCase	:     List[str]						 =			AutoTokenizer.from_pretrained('bert-base-cased'     )
 UpperCAmelCase	:     Optional[int]						 =			load_dataset('glue'  ,	'mrpc'     )

 def tokenize_function(UpperCAmelCase_     ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase	:     List[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():
  UpperCAmelCase	:     int						 =			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
 UpperCAmelCase	:     Dict						 =			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.
  UpperCAmelCase	:     List[Any]						 =			1_28 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	:     List[Any]						 =			16
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase	:     Tuple						 =			8
  else:
   UpperCAmelCase	:     List[str]						 =			None

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

 # Instantiate dataloaders.
 UpperCAmelCase	:     Optional[int]						 =			DataLoader(
     tokenized_datasets['train']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_  ,	drop_last=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			DataLoader(
     tokenized_datasets['validation']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_  ,	drop_last=(accelerator.mixed_precision == 'fp8')  ,	)

 return train_dataloader, eval_dataloader




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # Initialize accelerator
 UpperCAmelCase	:     int						 =			Accelerator(cpu=args.cpu  ,	mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCAmelCase	:     Optional[int]						 =			config['lr']
 UpperCAmelCase	:     List[Any]						 =			int(config['num_epochs']     )
 UpperCAmelCase	:     Optional[Any]						 =			int(config['seed']     )
 UpperCAmelCase	:     Dict						 =			int(config['batch_size']     )

 UpperCAmelCase	:     Optional[int]						 =			evaluate.load('glue'  ,	'mrpc'     )

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

 set_seed(UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase	:     Optional[int]						 =			get_dataloaders(UpperCAmelCase_  ,	UpperCAmelCase_     )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase	:     Tuple						 =			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).
 UpperCAmelCase	:     Dict						 =			model.to(accelerator.device     )
 # Instantiate optimizer
 UpperCAmelCase	:     int						 =			AdamW(params=model.parameters()  ,	lr=UpperCAmelCase_     )

 # Instantiate scheduler
 UpperCAmelCase	:     str						 =			get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase_  ,	num_warmup_steps=1_00  ,	num_training_steps=(len(UpperCAmelCase_     ) * 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 ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Optional[Any]						 =			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     )
   UpperCAmelCase	:     Any						 =			model(**UpperCAmelCase_     )
   UpperCAmelCase	:     List[str]						 =			outputs.loss
   UpperCAmelCase	:     int						 =			loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase_     )
   if step % gradient_accumulation_steps == 0:
    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():
    UpperCAmelCase	:     List[str]						 =			model(**UpperCAmelCase_     )
   UpperCAmelCase	:     Optional[int]						 =			outputs.logits.argmax(dim=-1     )
   UpperCAmelCase ,			UpperCAmelCase	:     int						 =			accelerator.gather_for_metrics((predictions, batch['labels'])     )
   metric.add_batch(
       predictions=UpperCAmelCase_  ,	references=UpperCAmelCase_  ,	)

  UpperCAmelCase	:     str						 =			metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Dict						 =			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.'     )
 UpperCAmelCase	:     Union[str, Any]						 =			parser.parse_args()
 UpperCAmelCase	:     Any						 =			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(UpperCAmelCase_  ,	UpperCAmelCase_     )


if __name__ == "__main__":
 main()







'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


lowercase__         =      re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__         =      re.compile(r"([a-z\d])([A-Z])")

lowercase__         =      re.compile(r"(?<!_)_(?!_)")
lowercase__         =      re.compile(r"(_{2,})")

lowercase__         =      r"^\w+(\.\w+)*$"

lowercase__         =      r"<>:/\|?*"




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			_uppercase_uppercase_re.sub(R'\1_\2'  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			_lowercase_uppercase_re.sub(R'\1_\2'  ,	UpperCAmelCase_     )
 return name.lower()




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			_single_underscore_re.split(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			[_multiple_underscores_re.split(UpperCAmelCase_     ) for n in name]
 return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCAmelCase_     ) if n != ''     )




def 				UpperCamelCase(					UpperCAmelCase_     ):
 if os.path.basename(UpperCAmelCase_     ) != name:
  raise ValueError(F"""Should be a dataset name, not a path: {name}"""     )
 return camelcase_to_snakecase(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if os.path.basename(UpperCAmelCase_     ) != name:
  raise ValueError(F"""Should be a dataset name, not a path: {name}"""     )
 if not re.match(_split_re  ,	UpperCAmelCase_     ):
  raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""     )
 return F"""{filename_prefix_for_name(UpperCAmelCase_     )}-{split}"""




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None     ):
 UpperCAmelCase	:     Dict						 =			filename_prefix_for_split(UpperCAmelCase_  ,	UpperCAmelCase_     )
 if filetype_suffix:
  prefix += F""".{filetype_suffix}"""
 UpperCAmelCase	:     Optional[int]						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return F"""{filepath}*"""




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None     ):
 UpperCAmelCase	:     Optional[int]						 =			filename_prefix_for_split(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 if shard_lengths:
  UpperCAmelCase	:     List[str]						 =			len(UpperCAmelCase_     )
  UpperCAmelCase	:     List[str]						 =			[F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(UpperCAmelCase_     )]
  if filetype_suffix:
   UpperCAmelCase	:     Dict						 =			[filename + F""".{filetype_suffix}""" for filename in filenames]
  return filenames
 else:
  UpperCAmelCase	:     Optional[Any]						 =			prefix
  if filetype_suffix:
   filename += F""".{filetype_suffix}"""
  return [filename]




