



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE__      :			str							=		16
SCREAMING_SNAKE_CASE__      :			Tuple							=		32

def      __lowercase					( snake_case,  snake_case = 1_6,  snake_case = "bert-base-cased" ):







				"""simple docstring"""






				__magic_name__		:int        =      AutoTokenizer.from_pretrained(snake_case )
				__magic_name__		:Any        =      load_dataset('''glue''',  '''mrpc''' )

				def tokenize_function(snake_case ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__		:int        =      tokenizer(examples['''sentence1'''],  examples['''sentence2'''],  truncation=snake_case,  max_length=snake_case )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				__magic_name__		:Dict        =      datasets.map(
				    snake_case,  batched=snake_case,  remove_columns=['''idx''', '''sentence1''', '''sentence2'''],  load_from_cache_file=snake_case )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__magic_name__		:Tuple        =      tokenized_datasets.rename_column('''label''',  '''labels''' )

				def collate_fn(snake_case ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(snake_case,  padding='''max_length''',  max_length=1_2_8,  return_tensors='''pt''' )
								return tokenizer.pad(snake_case,  padding='''longest''',  return_tensors='''pt''' )

				# Instantiate dataloaders.
				__magic_name__		:str        =      DataLoader(
				    tokenized_datasets['''train'''],  shuffle=snake_case,  collate_fn=snake_case,  batch_size=snake_case )
				__magic_name__		:Optional[int]        =      DataLoader(
				    tokenized_datasets['''validation'''],  shuffle=snake_case,  collate_fn=snake_case,  batch_size=snake_case )

				return train_dataloader, eval_dataloader

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				model.eval()
				__magic_name__		:Tuple        =      0
				for step, batch in enumerate(snake_case ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device )
								with torch.no_grad():
												__magic_name__		:List[Any]        =      model(**snake_case )
								__magic_name__		:Any        =      outputs.logits.argmax(dim=-1 )
								# It is slightly faster to call this once, than multiple times
								__magic_name__		:List[str]        =      accelerator.gather(
								    (predictions, batch['''labels''']) )  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(snake_case ) - 1:
																__magic_name__		:Dict        =      predictions[: len(eval_dataloader.dataset ) - samples_seen]
																__magic_name__		:Tuple        =      references[: len(eval_dataloader.dataset ) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=snake_case,  references=snake_case,  )

				__magic_name__		:List[str]        =      metric.compute()
				return eval_metric["accuracy"]

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__		:Dict        =      config["""lr"""]
				__magic_name__		:List[Any]        =      int(config['''num_epochs'''] )
				__magic_name__		:Dict        =      int(config['''seed'''] )
				__magic_name__		:Any        =      int(config['''batch_size'''] )
				__magic_name__		:List[str]        =      args.model_name_or_path

				set_seed(snake_case )
				__magic_name__		:str        =      get_dataloaders(snake_case,  snake_case,  snake_case )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__		:Any        =      AutoModelForSequenceClassification.from_pretrained(snake_case,  return_dict=snake_case )

				# Instantiate optimizer
				__magic_name__		:Union[str, Any]        =      (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__magic_name__		:Tuple        =      optimizer_cls(params=model.parameters(),  lr=snake_case )

				if accelerator.state.deepspeed_plugin is not None:
								__magic_name__		:Dict        =      accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								__magic_name__		:int        =      1
				__magic_name__		:Any        =      (len(snake_case ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								__magic_name__		:Tuple        =      get_linear_schedule_with_warmup(
								    optimizer=snake_case,  num_warmup_steps=0,  num_training_steps=snake_case,  )
				else:
								__magic_name__		:Dict        =      DummyScheduler(snake_case,  total_num_steps=snake_case,  warmup_num_steps=0 )

				# 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__		:List[str]        =      accelerator.prepare(
				    snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				# We need to keep track of how many total steps we have iterated over
				__magic_name__		:Tuple        =      0
				# We also need to keep track of the stating epoch so files are named properly
				__magic_name__		:Union[str, Any]        =      0
				__magic_name__		:Tuple        =      evaluate.load('''glue''',  '''mrpc''' )
				__magic_name__		:List[str]        =      num_epochs

				if args.partial_train_epoch is not None:
								__magic_name__		:Dict        =      args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint )
								__magic_name__		:Union[str, Any]        =      args.resume_from_checkpoint.split('''epoch_''' )[1]
								__magic_name__		:Union[str, Any]        =      """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								__magic_name__		:Union[str, Any]        =      int(snake_case ) + 1
								__magic_name__		:Tuple        =      evaluation_loop(snake_case,  snake_case,  snake_case,  snake_case )
								accelerator.print('''resumed checkpoint performance:''',  snake_case )
								accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''',  lr_scheduler.get_lr()[0] )
								accelerator.print('''resumed optimizers\'s lr:''',  optimizer.param_groups[0]['''lr'''] )
								with open(os.path.join(args.output_dir,  f'''state_{starting_epoch-1}.json''' ),  '''r''' ) as f:
												__magic_name__		:str        =      json.load(snake_case )
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				__magic_name__		:Optional[Any]        =      {}
				for epoch in range(snake_case,  snake_case ):
								model.train()
								for step, batch in enumerate(snake_case ):
												__magic_name__		:Tuple        =      model(**snake_case )
												__magic_name__		:Optional[Any]        =      outputs.loss
												__magic_name__		:Tuple        =      loss / gradient_accumulation_steps
												accelerator.backward(snake_case )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								__magic_name__		:Union[str, Any]        =      f'''epoch_{epoch}'''
								__magic_name__		:Union[str, Any]        =      os.path.join(args.output_dir,  snake_case )
								accelerator.save_state(snake_case )
								__magic_name__		:List[Any]        =      evaluation_loop(snake_case,  snake_case,  snake_case,  snake_case )
								__magic_name__		:Union[str, Any]        =      accuracy
								__magic_name__		:Tuple        =      lr_scheduler.get_lr()[0]
								__magic_name__		:Union[str, Any]        =      optimizer.param_groups[0]["""lr"""]
								__magic_name__		:Union[str, Any]        =      epoch
								__magic_name__		:Tuple        =      overall_step
								accelerator.print(f'''epoch {epoch}:''',  snake_case )

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir,  f'''state_{epoch}.json''' ),  '''w''' ) as f:
																json.dump(snake_case,  snake_case )

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
				parser.add_argument(
				    '''--model_name_or_path''',  type=snake_case,  default='''bert-base-cased''',  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  required=snake_case,  )
				parser.add_argument(
				    '''--output_dir''',  type=snake_case,  default='''.''',  help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',  )
				parser.add_argument(
				    '''--resume_from_checkpoint''',  type=snake_case,  default=snake_case,  help='''If the training should continue from a checkpoint folder.''',  )
				parser.add_argument(
				    '''--partial_train_epoch''',  type=snake_case,  default=snake_case,  help='''If passed, the training will stop after this number of epochs.''',  )
				parser.add_argument(
				    '''--num_epochs''',  type=snake_case,  default=2,  help='''Number of train epochs.''',  )
				__magic_name__		:Any        =      parser.parse_args()
				__magic_name__		:Tuple        =      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

				training_function(snake_case,  snake_case )


if __name__ == "__main__":
					main()














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






