
'''simple docstring'''







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


lowercase    :							Tuple      =					16
lowercase    :							List[Any]      =					32







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A = 16			,				__A = "bert-base-cased"	)       ->	Any:
	_snake_case   						=		AutoTokenizer.from_pretrained(__UpperCamelCase	)
	_snake_case   						=		load_dataset('glue'			,				'mrpc'	)

	def tokenize_function(__A	):
		# 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(__A	):
		# 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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[str]:
	_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						, _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						, _snake_case						, _snake_case						, _snake_case						, _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

	# Now we train the model
	_snake_case   						=		evaluate.load('glue'			,				'mrpc'	)
	_snake_case   						=		0
	_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

		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						, _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()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'			,				__UpperCamelCase	)
		_snake_case   						=		eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			_snake_case   						=		eval_metric['accuracy']

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir			,				'all_results.json'	)			,				'w'	) as f:
			json.dump(__UpperCamelCase			,				__UpperCamelCase	)







def 				SCREAMING_SNAKE_CASE__	(   )       ->	List[Any]:
	_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(
	    '--performance_lower_bound'			,				type=__UpperCamelCase			,				default=__UpperCamelCase			,				help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'			,				)
	parser.add_argument(
	    '--num_epochs'			,				type=__UpperCamelCase			,				default=3			,				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()









"""simple docstring"""


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
       import ctypes
       import msvcrt  # noqa


       class 					snake_case		(							ctypes.Structure						):


          '''simple docstring'''

          A_						:	List[str]						 = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l''' )
    sys.stdout.flush()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h''' )
    sys.stdout.flush()





@contextmanager
def 					lowerCAmelCase				(    ):




  """simple docstring"""







  try:
    hide_cursor()
    yield
  finally:
    show_cursor()




