



'''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
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
#
########################################################################


lowercase_      :				Tuple   					=    16
lowercase_      :				Dict   					=    32







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Accelerator ,  lowercase_    :      int = 16						):
						lowercase        =							AutoTokenizer.from_pretrained("""bert-base-cased"""						)
						lowercase        =							load_dataset("""glue""" ,  """mrpc"""						)

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

						def collate_fn(lowercase_    :      Tuple						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase        =							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":
																		lowercase        =							16
												elif accelerator.mixed_precision != "no":
																		lowercase        =							8
												else:
																		lowercase        =							None

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

						# Instantiate dataloaders.
						lowercase        =							DataLoader(
						    tokenized_datasets["""train"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)
						lowercase        =							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

						lowercase_      :				List[Any]   					=    mocked_dataloaders  # noqa: F811







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict						):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,  lowercase_						) == "1":
												lowercase        =							2
						# New Code #
						lowercase        =							int(args.gradient_accumulation_steps						)
						lowercase        =							int(args.local_sgd_steps						)
						# Initialize accelerator
						lowercase        =							Accelerator(
						    cpu=args.cpu ,  mixed_precision=args.mixed_precision ,  gradient_accumulation_steps=lowercase_						)
						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
						lowercase        =							config["""lr"""]
						lowercase        =							int(config["""num_epochs"""]						)
						lowercase        =							int(config["""seed"""]						)
						lowercase        =							int(config["""batch_size"""]						)

						lowercase        =							evaluate.load("""glue""" ,  """mrpc"""						)

						set_seed(lowercase_						)
						lowercase     ,   lowercase        =							get_dataloaders(lowercase_ ,  lowercase_						)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase        =							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).
						lowercase        =							model.to(accelerator.device						)

						# Instantiate optimizer
						lowercase        =							AdamW(params=model.parameters() ,  lr=lowercase_						)

						# Instantiate scheduler
						lowercase        =							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.
						lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							accelerator.prepare(
						    lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_						)

						# Now we train the model
						for epoch in range(lowercase_						):
												model.train()
												with LocalSGD(
												    accelerator=lowercase_ ,  model=lowercase_ ,  local_sgd_steps=lowercase_ ,  enabled=local_sgd_steps is not None						) as local_sgd:
																		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_						):
																														lowercase        =							model(**lowercase_						)
																														lowercase        =							output.loss
																														accelerator.backward(lowercase_						)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

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

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







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							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(
						    """--local_sgd_steps""" ,  type=lowercase_ ,  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."""						)
						lowercase        =							parser.parse_args()
						lowercase        =							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowercase_ ,  lowercase_						)


if __name__ == "__main__":
						main()







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


