



"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


A:   int								=		1_6
A:   Tuple								=		3_2

def       _snake_case						(						UpperCamelCase						:		Accelerator			,		UpperCamelCase						:		int = 16						):
 UpperCAmelCase	:					Optional[int]	=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
 UpperCAmelCase	:					List[Any]	=	load_dataset("""glue"""			,		"""mrpc"""						)

 def tokenize_function(UpperCamelCase						:		Tuple						):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase	:					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	:					Any	=	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	:					Tuple	=	tokenized_datasets.rename_column("""label"""			,		"""labels"""						)

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

  return tokenizer.pad(
      UpperCamelCase			,		padding="""longest"""			,		max_length=UpperCamelCase			,		pad_to_multiple_of=UpperCamelCase			,		return_tensors="""pt"""			,		)

 # Instantiate dataloaders.
 UpperCAmelCase	:					List[str]	=	DataLoader(
     tokenized_datasets["""train"""]			,		shuffle=UpperCamelCase			,		collate_fn=UpperCamelCase			,		batch_size=UpperCamelCase						)
 UpperCAmelCase	:					int	=	DataLoader(
     tokenized_datasets["""validation"""]			,		shuffle=UpperCamelCase			,		collate_fn=UpperCamelCase			,		batch_size=UpperCamelCase						)

 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:   Dict								=		mocked_dataloaders  # noqa: F811

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Optional[int]						):
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,		UpperCamelCase						) == "1":
  UpperCAmelCase	:					List[str]	=	2
 # Initialize accelerator
 UpperCAmelCase	:					str	=	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	:					List[Any]	=	int(config["""seed"""]						)
 UpperCAmelCase	:					Optional[Any]	=	int(config["""batch_size"""]						)

 UpperCAmelCase	:					str	=	evaluate.load("""glue"""			,		"""mrpc"""						)

 # If the batch size is too big we use gradient accumulation
 UpperCAmelCase	:					Union[str, Any]	=	1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  UpperCAmelCase	:					Dict	=	batch_size // MAX_GPU_BATCH_SIZE
  UpperCAmelCase	:					Optional[int]	=	MAX_GPU_BATCH_SIZE

 set_seed(UpperCamelCase						)
 UpperCAmelCase	,		UpperCAmelCase	:					Any	=	get_dataloaders(UpperCamelCase			,		UpperCamelCase						)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase	:					Optional[Any]	=	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	:					Tuple	=	model.to(accelerator.device						)

 # Instantiate optimizer
 UpperCAmelCase	:					Dict	=	AdamW(params=model.parameters()			,		lr=UpperCamelCase						)

 # Instantiate scheduler
 UpperCAmelCase	:					int	=	get_linear_schedule_with_warmup(
     optimizer=UpperCamelCase			,		num_warmup_steps=100			,		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	:					int	=	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	:					int	=	model(**UpperCamelCase						)
   UpperCAmelCase	:					Union[str, Any]	=	outputs.loss
   UpperCAmelCase	:					List[Any]	=	loss / gradient_accumulation_steps
   accelerator.backward(UpperCamelCase						)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  UpperCAmelCase	:					Any	=	0
  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	:					Union[str, Any]	=	model(**UpperCamelCase						)
   UpperCAmelCase	:					Any	=	outputs.logits.argmax(dim=-1						)
   UpperCAmelCase	,		UpperCAmelCase	:					int	=	accelerator.gather((predictions, batch["""labels"""])						)
   # New Code #
   # First we check if it's a distributed system
   if accelerator.use_distributed:
    # Then see if we're on the last batch of our eval dataloader
    if step == len(UpperCamelCase						) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     UpperCAmelCase	:					Union[str, Any]	=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
     UpperCAmelCase	:					Union[str, Any]	=	references[: len(eval_dataloader.dataset						) - samples_seen]
    else:
     # Otherwise we add the number of samples seen
     samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
   metric.add_batch(
       predictions=UpperCamelCase			,		references=UpperCamelCase			,		)

  UpperCAmelCase	:					Optional[int]	=	metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"			,		UpperCamelCase						)

def       _snake_case						(						):
 UpperCAmelCase	:					str	=	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	:					Tuple	=	parser.parse_args()
 UpperCAmelCase	:					List[Any]	=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      main()





"""simple docstring"""
A:   int								=		range(2, 2_0 + 1)
A:   Any								=		[1_0**k for k in range(ks[-1] + 1)]
A:   dict[int, dict[int, list[list[int]]]]								=		{}

def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[str]	=	sum(a_i[j] for j in range(UpperCamelCase			,		len(UpperCamelCase						)						)						)
 UpperCAmelCase	:					str	=	sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase						)			,		UpperCamelCase						)						)						)

 UpperCAmelCase	,		UpperCAmelCase	:					str	=	0, 0
 UpperCAmelCase	:					Optional[Any]	=	n - i

 UpperCAmelCase	:					Optional[int]	=	memo.get(UpperCamelCase						)

 if sub_memo is not None:
  UpperCAmelCase	:					str	=	sub_memo.get(UpperCamelCase						)

  if jumps is not None and len(UpperCamelCase						) > 0:
   # find and make the largest jump without going over
   UpperCAmelCase	:					Tuple	=	-1
   for _k in range(len(UpperCamelCase						) - 1			,		-1			,		-1						):
    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
     UpperCAmelCase	:					int	=	_k
     break

   if max_jump >= 0:
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	jumps[max_jump]
    # since the difference between jumps is cached, add c
    UpperCAmelCase	:					List[str]	=	diff + c
    for j in range(min(UpperCamelCase			,		len(UpperCamelCase						)						)						):
     UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	divmod(UpperCamelCase			,		10						)
    if new_c > 0:
     add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

  else:
   UpperCAmelCase	:					int	=	[]
 else:
  UpperCAmelCase	:					List[str]	=	{c: []}
  UpperCAmelCase	:					str	=	sub_memo

 if dn >= max_dn or c + diff >= base[k]:
  return diff, dn

 if k > ks[0]:
  while True:
   # keep doing smaller jumps
   UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	next_term(UpperCamelCase			,		k - 1			,		i + dn			,		UpperCamelCase						)
   diff += _diff
   dn += terms_jumped

   if dn >= max_dn or c + diff >= base[k]:
    break
 else:
  # would be too small a jump, just compute sequential terms instead
  UpperCAmelCase	,		UpperCAmelCase	:					int	=	compute(UpperCamelCase			,		UpperCamelCase			,		i + dn			,		UpperCamelCase						)
  diff += _diff
  dn += terms_jumped

 UpperCAmelCase	:					Dict	=	sub_memo[c]

 # keep jumps sorted by # of terms skipped
 UpperCAmelCase	:					str	=	0
 while j < len(UpperCamelCase						):
  if jumps[j][1] > dn:
   break
  j += 1

 # cache the jump for this value digitsum(b) and c
 sub_memo[c].insert(UpperCamelCase			,		(diff, dn, k)						)
 return (diff, dn)

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Any						):
 if i >= n:
  return 0, i
 if k > len(UpperCamelCase						):
  a_i.extend([0 for _ in range(k - len(UpperCamelCase						)						)]						)

 # note: a_i -> b * 10^k + c
 # ds_b -> digitsum(b)
 # ds_c -> digitsum(c)
 UpperCAmelCase	:					List[str]	=	i
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	0, 0, 0
 for j in range(len(UpperCamelCase						)						):
  if j >= k:
   ds_b += a_i[j]
  else:
   ds_c += a_i[j]

 while i < n:
  i += 1
  UpperCAmelCase	:					Optional[int]	=	ds_c + ds_b
  diff += addend
  UpperCAmelCase	:					str	=	0
  for j in range(UpperCamelCase						):
   UpperCAmelCase	:					Any	=	a_i[j] + addend
   UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)

   ds_c += a_i[j]

  if addend > 0:
   break

 if addend > 0:
  add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 return diff, i - start_i

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[int]						):
 for j in range(UpperCamelCase			,		len(UpperCamelCase						)						):
  UpperCAmelCase	:					Optional[int]	=	digits[j] + addend
  if s >= 10:
   UpperCAmelCase	,		UpperCAmelCase	:					int	=	divmod(UpperCamelCase			,		10						)
   UpperCAmelCase	:					str	=	addend // 10 + quotient
  else:
   UpperCAmelCase	:					Any	=	s
   UpperCAmelCase	:					Union[str, Any]	=	addend // 10

  if addend == 0:
   break

 while addend > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)
  digits.append(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		int = 10**15						):
 UpperCAmelCase	:					Dict	=	[1]
 UpperCAmelCase	:					int	=	1
 UpperCAmelCase	:					Tuple	=	0
 while True:
  UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	next_term(UpperCamelCase			,		20			,		i + dn			,		UpperCamelCase						)
  dn += terms_jumped
  if dn == n - i:
   break

 UpperCAmelCase	:					Any	=	0
 for j in range(len(UpperCamelCase						)						):
  a_n += digits[j] * 10**j
 return a_n


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