

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


__A      		=     16
__A      		=     32



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase = 16			,	_UpperCamelCase = "bert-base-cased"					)							->     Tuple:



		"""simple docstring"""






		_snake_case						=							AutoTokenizer.from_pretrained(__UpperCAmelCase					)
		_snake_case						=							load_dataset('''glue'''			,	'''mrpc'''					)

		def tokenize_function(_UpperCamelCase					):
				# max_length=None => use the model max length (it's actually the default)
				_snake_case						=							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
		_snake_case						=							datasets.map(
		    __UpperCAmelCase			,	batched=__UpperCAmelCase			,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,	load_from_cache_file=__UpperCAmelCase					)

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

		def collate_fn(_UpperCamelCase					):
				# 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(__UpperCAmelCase			,	padding='''max_length'''			,	max_length=128			,	return_tensors='''pt'''					)
				return tokenizer.pad(__UpperCAmelCase			,	padding='''longest'''			,	return_tensors='''pt'''					)

		# Instantiate dataloaders.
		_snake_case						=							DataLoader(
		    tokenized_datasets['''train''']			,	shuffle=__UpperCAmelCase			,	collate_fn=__UpperCAmelCase			,	batch_size=__UpperCAmelCase					)
		_snake_case						=							DataLoader(
		    tokenized_datasets['''validation''']			,	shuffle=__UpperCAmelCase			,	collate_fn=__UpperCAmelCase			,	batch_size=__UpperCAmelCase					)

		return train_dataloader, eval_dataloader



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     int:



		"""simple docstring"""






		model.eval()
		_snake_case						=							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():
						_snake_case						=							model(**__UpperCAmelCase					)
				_snake_case						=							outputs.logits.argmax(dim=-1					)
				# It is slightly faster to call this once, than multiple times
				_snake_case						=							accelerator.gather(
				    (predictions, batch['''labels'''])					)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(__UpperCAmelCase					) - 1:
								_snake_case						=							predictions[: len(eval_dataloader.dataset					) - samples_seen]
								_snake_case						=							references[: len(eval_dataloader.dataset					) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=__UpperCAmelCase			,	references=__UpperCAmelCase			,	)

		_snake_case						=							metric.compute()
		return eval_metric["accuracy"]



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



		"""simple docstring"""






		_snake_case						=							Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_snake_case						=							config['''lr''']
		_snake_case						=							int(config['''num_epochs''']					)
		_snake_case						=							int(config['''seed''']					)
		_snake_case						=							int(config['''batch_size''']					)
		_snake_case						=							args.model_name_or_path

		set_seed(__UpperCAmelCase					)
		_snake_case						=							get_dataloaders(__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase					)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_snake_case						=							AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase			,	return_dict=__UpperCAmelCase					)

		# Instantiate optimizer
		_snake_case						=							(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_snake_case						=							optimizer_cls(params=model.parameters()			,	lr=__UpperCAmelCase					)

		if accelerator.state.deepspeed_plugin is not None:
				_snake_case						=							accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				_snake_case						=							1
		_snake_case						=							(len(__UpperCAmelCase					) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_snake_case						=							get_linear_schedule_with_warmup(
				    optimizer=__UpperCAmelCase			,	num_warmup_steps=0			,	num_training_steps=__UpperCAmelCase			,	)
		else:
				_snake_case						=							DummyScheduler(__UpperCAmelCase			,	total_num_steps=__UpperCAmelCase			,	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.
		_snake_case						=							accelerator.prepare(
		    __UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase					)

		# We need to keep track of how many total steps we have iterated over
		_snake_case						=							0
		# We also need to keep track of the stating epoch so files are named properly
		_snake_case						=							0
		_snake_case						=							evaluate.load('''glue'''			,	'''mrpc'''					)
		_snake_case						=							num_epochs

		if args.partial_train_epoch is not None:
				_snake_case						=							args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint					)
				_snake_case						=							args.resume_from_checkpoint.split('''epoch_'''					)[1]
				_snake_case						=							''''''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				_snake_case						=							int(__UpperCAmelCase					) + 1
				_snake_case						=							evaluation_loop(__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase					)
				accelerator.print('''resumed checkpoint performance:'''			,	__UpperCAmelCase					)
				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:
						_snake_case						=							json.load(__UpperCAmelCase					)
						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
		_snake_case						=							{}
		for epoch in range(__UpperCAmelCase			,	__UpperCAmelCase					):
				model.train()
				for step, batch in enumerate(__UpperCAmelCase					):
						_snake_case						=							model(**__UpperCAmelCase					)
						_snake_case						=							outputs.loss
						_snake_case						=							loss / gradient_accumulation_steps
						accelerator.backward(__UpperCAmelCase					)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				_snake_case						=							F"""epoch_{epoch}"""
				_snake_case						=							os.path.join(args.output_dir			,	__UpperCAmelCase					)
				accelerator.save_state(__UpperCAmelCase					)
				_snake_case						=							evaluation_loop(__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase			,	__UpperCAmelCase					)
				_snake_case						=							accuracy
				_snake_case						=							lr_scheduler.get_lr()[0]
				_snake_case						=							optimizer.param_groups[0]['''lr''']
				_snake_case						=							epoch
				_snake_case						=							overall_step
				accelerator.print(F"""epoch {epoch}:"""			,	__UpperCAmelCase					)

				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(__UpperCAmelCase			,	__UpperCAmelCase					)



def 				snake_case_()							->     Optional[Any]:



		"""simple docstring"""






		_snake_case						=							argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
		parser.add_argument(
		    '''--model_name_or_path'''			,	type=__UpperCAmelCase			,	default='''bert-base-cased'''			,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,	required=__UpperCAmelCase			,	)
		parser.add_argument(
		    '''--output_dir'''			,	type=__UpperCAmelCase			,	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=__UpperCAmelCase			,	default=__UpperCAmelCase			,	help='''If the training should continue from a checkpoint folder.'''			,	)
		parser.add_argument(
		    '''--partial_train_epoch'''			,	type=__UpperCAmelCase			,	default=__UpperCAmelCase			,	help='''If passed, the training will stop after this number of epochs.'''			,	)
		parser.add_argument(
		    '''--num_epochs'''			,	type=__UpperCAmelCase			,	default=2			,	help='''Number of train epochs.'''			,	)
		_snake_case						=							parser.parse_args()
		_snake_case						=							{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

		training_function(__UpperCAmelCase			,	__UpperCAmelCase					)


if __name__ == "__main__":
	main()




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

