






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:      List[str]  				=				1_6
A:      Optional[int]  				=				3_2






def 	_UpperCAmelCase    (  a		:	Accelerator						,			a		:	int = 1_6						,			a		:	str = "bert-base-cased"							)			->			Any:





						"""simple docstring"""
						lowercase_      :   Union[str, Any]        =			AutoTokenizer.from_pretrained(a_							)
						lowercase_      :   Optional[int]        =			load_dataset('glue'						,			'mrpc'							)

						def tokenize_function(a		:	List[Any]							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_      :   Tuple        =			tokenizer(examples['sentence1']						,			examples['sentence2']						,			truncation=a_						,			max_length=a_							)
												return outputs

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

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

						def collate_fn(a		:	Dict							):
												# 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(a_						,			padding='max_length'						,			max_length=1_2_8						,			return_tensors='pt'							)
												return tokenizer.pad(a_						,			padding='longest'						,			return_tensors='pt'							)

						# Instantiate dataloaders.
						lowercase_      :   int        =			DataLoader(
						    tokenized_datasets['train']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_							)
						lowercase_      :   int        =			DataLoader(
						    tokenized_datasets['validation']						,			shuffle=a_						,			collate_fn=a_						,			batch_size=a_							)

						return train_dataloader, eval_dataloader






def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Union[str, Any]							)			->			List[Any]:





						"""simple docstring"""
						# Initialize accelerator
						lowercase_      :   str        =			Accelerator()

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

						set_seed(a_							)
						lowercase_      :   List[Any]        =			get_dataloaders(a_						,			a_						,			a_							)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase_      :   List[str]        =			AutoModelForSequenceClassification.from_pretrained(a_						,			return_dict=a_							)

						# Instantiate optimizer
						lowercase_      :   Tuple        =			(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						lowercase_      :   Any        =			optimizer_cls(params=model.parameters()						,			lr=a_							)

						if accelerator.state.deepspeed_plugin is not None:
												lowercase_      :   int        =			accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												lowercase_      :   Dict        =			1
						lowercase_      :   List[Any]        =			(len(a_							) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												lowercase_      :   Optional[int]        =			get_linear_schedule_with_warmup(
												    optimizer=a_						,			num_warmup_steps=0						,			num_training_steps=a_						,			)
						else:
												lowercase_      :   Optional[int]        =			DummyScheduler(a_						,			total_num_steps=a_						,			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.
						lowercase_      :   Tuple        =			accelerator.prepare(
						    a_						,			a_						,			a_						,			a_						,			a_							)

						# We need to keep track of how many total steps we have iterated over
						lowercase_      :   Tuple        =			0
						# We also need to keep track of the stating epoch so files are named properly
						lowercase_      :   Tuple        =			0

						# Now we train the model
						lowercase_      :   Tuple        =			evaluate.load('glue'						,			'mrpc'							)
						lowercase_      :   Optional[Any]        =			0
						lowercase_      :   Tuple        =			{}
						for epoch in range(a_						,			a_							):
												model.train()
												for step, batch in enumerate(a_							):
																		lowercase_      :   Any        =			model(**a_							)
																		lowercase_      :   int        =			outputs.loss
																		lowercase_      :   List[Any]        =			loss / gradient_accumulation_steps
																		accelerator.backward(a_							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

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

												lowercase_      :   Tuple        =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"						,			a_							)
												lowercase_      :   int        =			eval_metric['''accuracy''']

												if best_performance < eval_metric["accuracy"]:
																		lowercase_      :   Dict        =			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(a_						,			a_							)






def 	_UpperCAmelCase    (  )			->			Union[str, Any]:





						"""simple docstring"""
						lowercase_      :   str        =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
						parser.add_argument(
						    '--model_name_or_path'						,			type=a_						,			default='bert-base-cased'						,			help='Path to pretrained model or model identifier from huggingface.co/models.'						,			required=a_						,			)
						parser.add_argument(
						    '--output_dir'						,			type=a_						,			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=a_						,			default=a_						,			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=a_						,			default=3						,			help='Number of train epochs.'						,			)
						lowercase_      :   Optional[Any]        =			parser.parse_args()
						lowercase_      :   Optional[int]        =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
						training_function(a_						,			a_							)


if __name__ == "__main__":
						main()








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

						def get_matched_characters(a		:	str						,			a		:	str							) -> str:
												lowercase_      :   Union[str, Any]        =			[]
												lowercase_      :   Tuple        =			min(len(_stra							)						,			len(_stra							)							) // 2
												for i, l in enumerate(_stra							):
																		lowercase_      :   Optional[int]        =			int(max(0						,			i - limit							)							)
																		lowercase_      :   Union[str, Any]        =			int(min(i + limit + 1						,			len(_stra							)							)							)
																		if l in _stra[left:right]:
																								matched.append(a							)
																								lowercase_      :   Union[str, Any]        =			f"{_stra[0:_stra.index(a							)]} {_stra[_stra.index(a							) + 1:]}"

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

						if not match_count:
												lowercase_      :   List[str]        =			0.0
						else:
												lowercase_      :   Any        =			(
												    1
												    / 3
												    * (
												        match_count / len(a							)
												        + match_count / len(a							)
												        + (match_count - transpositions) / match_count
												    )
												)

						# common prefix up to 4 characters
						lowercase_      :   Optional[Any]        =			0
						for ca, ca in zip(stra[:4]						,			stra[:4]							):
												if ca == ca:
																		prefix_len += 1
												else:
																		break

						return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler("hello", "world"))
