






'''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


__lowerCAmelCase       		=      16
__lowerCAmelCase       		=      32





def 		_UpperCAmelCase   (							__A							:   Accelerator						,		__A							:   int = 16						,		__A							:   str = "bert-base-cased"      ):
				a_		:  Union[str, Any]											=      AutoTokenizer.from_pretrained(a_      )
				a_		:  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)
								a_		:  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
				a_		:  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
				a_		:  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_28						,		return_tensors='''pt'''      )
								return tokenizer.pad(a_						,		padding='''longest'''						,		return_tensors='''pt'''      )

				# Instantiate dataloaders.
				a_		:  int											=      DataLoader(
				    tokenized_datasets['''train''']						,		shuffle=a_						,		collate_fn=a_						,		batch_size=a_      )
				a_		:  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]      ):
				# Initialize accelerator
				a_		:  str											=      Accelerator()

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

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

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

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

				if accelerator.state.deepspeed_plugin is not None:
								a_		:  int											=      accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								a_		:  Dict											=      1
				a_		:  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
				):
								a_		:  Optional[int]											=      get_linear_schedule_with_warmup(
								    optimizer=a_						,		num_warmup_steps=0						,		num_training_steps=a_						,		)
				else:
								a_		:  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.
				a_		:  Tuple											=      accelerator.prepare(
				    a_						,		a_						,		a_						,		a_						,		a_      )

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

				# Now we train the model
				a_		:  Tuple											=      evaluate.load('''glue'''						,		'''mrpc'''      )
				a_		:  Optional[Any]											=      0
				a_		:  Tuple											=      {}
				for epoch in range(a_						,		a_      ):
								model.train()
								for step, batch in enumerate(a_      ):
												a_		:  Any											=      model(**a_      )
												a_		:  int											=      outputs.loss
												a_		:  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()
								a_		:  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():
																a_		:  int											=      model(**a_      )
												a_		:  List[Any]											=      outputs.logits.argmax(dim=-1      )
												# It is slightly faster to call this once, than multiple times
												a_		:  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:
																				a_		:  Any											=      predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																				a_		:  Any											=      references[: len(eval_dataloader.dataset      ) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=a_						,		references=a_						,		)

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

								if best_performance < eval_metric["accuracy"]:
												a_		:  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   (							):
				a_		:  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.'''						,		)
				a_		:  Optional[Any]											=      parser.parse_args()
				a_		:  Optional[int]											=      {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(a_						,		a_      )


if __name__ == "__main__":
		main()












'''simple docstring'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




