




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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_		:					Any          =  16
UpperCAmelCase_		:					int          =  32




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 16 ):
				__magic_name__						:						List[str]										=AutoTokenizer.from_pretrained("""bert-base-cased""" )
				__magic_name__						:						str										=load_dataset("""glue"""						,  """mrpc""" )

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

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

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

				# Instantiate dataloaders.
				__magic_name__						:						Tuple										=DataLoader(
				    tokenized_datasets["""train"""]						,  shuffle=lowerCamelCase						,  collate_fn=lowerCamelCase						,  batch_size=lowerCamelCase )
				__magic_name__						:						Dict										=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

							UpperCAmelCase_		:					Tuple          =  mocked_dataloaders  # noqa: F811




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,  lowerCamelCase ) == "1":
								__magic_name__						:						Tuple										=2
				# New Code #
				__magic_name__						:						List[Any]										=int(args.gradient_accumulation_steps )
				__magic_name__						:						int										=int(args.local_sgd_steps )
				# Initialize accelerator
				__magic_name__						:						List[str]										=Accelerator(
				    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  gradient_accumulation_steps=lowerCamelCase )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__						:						List[str]										=config["""lr"""]
				__magic_name__						:						Dict										=int(config["""num_epochs"""] )
				__magic_name__						:						Optional[int]										=int(config["""seed"""] )
				__magic_name__						:						Tuple										=int(config["""batch_size"""] )

				__magic_name__						:						Dict										=evaluate.load("""glue"""						,  """mrpc""" )

				set_seed(lowerCamelCase )
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=get_dataloaders(lowerCamelCase						,  lowerCamelCase )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__						:						str										=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).
				__magic_name__						:						List[str]										=model.to(accelerator.device )

				# Instantiate optimizer
				__magic_name__						:						Tuple										=AdamW(params=model.parameters()						,  lr=lowerCamelCase )

				# Instantiate scheduler
				__magic_name__						:						Dict										=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.
				__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						List[Any]										=accelerator.prepare(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

				# Now we train the model
				for epoch in range(lowerCamelCase ):
								model.train()
								with LocalSGD(
								    accelerator=lowerCamelCase						,  model=lowerCamelCase						,  local_sgd_steps=lowerCamelCase						,  enabled=local_sgd_steps is not None ) as local_sgd:
												for step, batch in enumerate(lowerCamelCase ):
																# 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(lowerCamelCase ):
																				__magic_name__						:						Tuple										=model(**lowerCamelCase )
																				__magic_name__						:						Optional[int]										=output.loss
																				accelerator.backward(lowerCamelCase )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								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():
																__magic_name__						:						int										=model(**lowerCamelCase )
												__magic_name__						:						int										=outputs.logits.argmax(dim=-1 )
												__magic_name__					,			__magic_name__						:						Optional[int]										=accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=lowerCamelCase						,  references=lowerCamelCase						,  )

								__magic_name__						:						Tuple										=metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"						,  lowerCamelCase )




def 		lowerCAmelCase_ (						):
				__magic_name__						:						List[str]										=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."""						,  )
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps"""						,  type=lowerCamelCase						,  default=1						,  help="""The number of minibatches to be ran before gradients are accumulated."""						,  )
				parser.add_argument(
				    """--local_sgd_steps"""						,  type=lowerCamelCase						,  default=8						,  help="""Number of local SGD steps or None to disable local SGD""" )
				parser.add_argument("""--cpu"""						,  action="""store_true"""						,  help="""If passed, will train on the CPU.""" )
				__magic_name__						:						Tuple										=parser.parse_args()
				__magic_name__						:						Any										={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase						,  lowerCamelCase )


if __name__ == "__main__":
							main()










UpperCAmelCase_		:					int          =  range(2, 20 + 1)
UpperCAmelCase_		:					Tuple          =  [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_		:					dict[int, dict[int, list[list[int]]]]          =  {}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=sum(a_i[j] for j in range(lowerCamelCase						,  len(lowerCamelCase ) ) )
				__magic_name__						:						Any										=sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )						,  lowerCamelCase ) ) )

				__magic_name__					,			__magic_name__						:						Tuple										=0, 0
				__magic_name__						:						Optional[Any]										=n - i

				__magic_name__						:						Union[str, Any]										=memo.get(lowerCamelCase )

				if sub_memo is not None:
								__magic_name__						:						int										=sub_memo.get(lowerCamelCase )

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

												if max_jump >= 0:
																__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[int]										=jumps[max_jump]
																# since the difference between jumps is cached, add c
																__magic_name__						:						Tuple										=diff + c
																for j in range(min(lowerCamelCase						,  len(lowerCamelCase ) ) ):
																				__magic_name__					,			__magic_name__						:						Tuple										=divmod(lowerCamelCase						,  10 )
																if new_c > 0:
																				add(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

								else:
												__magic_name__						:						str										=[]
				else:
								__magic_name__						:						List[str]										={c: []}
								__magic_name__						:						List[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
												__magic_name__					,			__magic_name__						:						Union[str, Any]										=next_term(lowerCamelCase						,  k - 1						,  i + dn						,  lowerCamelCase )
												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
								__magic_name__					,			__magic_name__						:						Optional[int]										=compute(lowerCamelCase						,  lowerCamelCase						,  i + dn						,  lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				__magic_name__						:						Tuple										=sub_memo[c]

				# keep jumps sorted by # of terms skipped
				__magic_name__						:						List[Any]										=0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				__magic_name__						:						Tuple										=i
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Tuple										=0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

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

												ds_c += a_i[j]

								if addend > 0:
												break

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				for j in range(lowerCamelCase						,  len(lowerCamelCase ) ):
								__magic_name__						:						Tuple										=digits[j] + addend
								if s >= 10:
												__magic_name__					,			__magic_name__						:						int										=divmod(lowerCamelCase						,  10 )
												__magic_name__						:						int										=addend // 10 + quotient
								else:
												__magic_name__						:						Dict										=s
												__magic_name__						:						Any										=addend // 10

								if addend == 0:
												break

				while addend > 0:
								__magic_name__					,			__magic_name__						:						Union[str, Any]										=divmod(lowerCamelCase						,  10 )
								digits.append(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase = 10**15 ):
				__magic_name__						:						List[str]										=[1]
				__magic_name__						:						str										=1
				__magic_name__						:						str										=0
				while True:
								__magic_name__					,			__magic_name__						:						List[str]										=next_term(lowerCamelCase						,  20						,  i + dn						,  lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				__magic_name__						:						int										=0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 10**j
				return a_n


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




