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
# and perform 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)
#
# 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_			    =							1_6
UpperCAmelCase_			    =							3_2




def       lowerCAmelCase_     (		lowercase:							Accelerator  ,					lowercase:							int = 16		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= AutoTokenizer.from_pretrained('''bert-base-cased'''		)
 _UpperCamelCase:     List[Any]							= load_dataset('''glue'''  ,					'''mrpc'''		)

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

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

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

 # Instantiate dataloaders.
 _UpperCamelCase:     Optional[int]							= DataLoader(
     tokenized_datasets['''train''']  ,					shuffle=lowercase  ,					collate_fn=lowercase  ,					batch_size=lowercase		)
 _UpperCamelCase:     Optional[Any]							= DataLoader(
     tokenized_datasets['''validation''']  ,					shuffle=lowercase  ,					collate_fn=lowercase  ,					batch_size=lowercase		)

 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       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,					lowercase		) == "1":
  _UpperCamelCase:     int							= 2
 # New Code #
 _UpperCamelCase:     Tuple							= int(args.gradient_accumulation_steps		)
 # Initialize accelerator
 _UpperCamelCase:     Tuple							= Accelerator(
     cpu=args.cpu  ,					mixed_precision=args.mixed_precision  ,					gradient_accumulation_steps=lowercase		)
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:     Dict							= config['''lr''']
 _UpperCamelCase:     Tuple							= int(config['''num_epochs''']		)
 _UpperCamelCase:     int							= int(config['''seed''']		)
 _UpperCamelCase:     List[str]							= int(config['''batch_size''']		)

 _UpperCamelCase:     List[str]							= evaluate.load('''glue'''  ,					'''mrpc'''		)

 set_seed(lowercase		)
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= get_dataloaders(lowercase  ,					lowercase		)
 # 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=lowercase		)

 # 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:     Any							= model.to(accelerator.device		)

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

 # Instantiate scheduler
 _UpperCamelCase:     int							= get_linear_schedule_with_warmup(
     optimizer=lowercase  ,					num_warmup_steps=100  ,					num_training_steps=(len(lowercase		) * 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:     Tuple							= accelerator.prepare(
     lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase		)

 # Now we train the model
 for epoch in range(lowercase		):
  model.train()
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(lowercase		):
    _UpperCamelCase:     List[Any]							= model(**lowercase		)
    _UpperCamelCase:     List[Any]							= output.loss
    accelerator.backward(lowercase		)
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

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




def       lowerCAmelCase_     (		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument(
     '''--mixed_precision'''  ,					type=lowercase  ,					default=lowercase  ,					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.'''  ,					)
 # New Code #
 parser.add_argument(
     '''--gradient_accumulation_steps'''  ,					type=lowercase  ,					default=1  ,					help='''The number of minibatches to be ran before gradients are accumulated.'''  ,					)
 parser.add_argument('''--cpu'''  ,					action='''store_true'''  ,					help='''If passed, will train on the CPU.'''		)
 _UpperCamelCase:     Any							= parser.parse_args()
 _UpperCamelCase:     Any							= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(lowercase  ,					lowercase		)


if __name__ == "__main__":
  main()
def       lowerCAmelCase_     (		lowercase:							int = 600_851_475_143		)    ->				int:







 '''simple docstring'''

 try:
  _UpperCamelCase:     Optional[int]							= int(lowercase		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 _UpperCamelCase:     Dict							= 1
 _UpperCamelCase:     Tuple							= 2
 while i * i <= n:
  while n % i == 0:
   _UpperCamelCase:     List[Any]							= i
   n //= i
  i += 1
 if n > 1:
  _UpperCamelCase:     Any							= n
 return int(lowercase		)


if __name__ == "__main__":
  print(f"""{solution() = }""")