



'''simple docstring'''

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase				: Tuple							    =				1_6
_UpperCAmelCase				: str							    =				3_2

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase = 1_6):
						__lowerCAmelCase							       =				AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase							       =				load_dataset('''glue''',				'''mrpc''')

						def tokenize_function(lowerCamelCase):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase							       =				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
						# starting with the main process first:
						with accelerator.main_process_first():
												__lowerCAmelCase							       =				datasets.map(
												    UpperCamelCase__,				batched=UpperCamelCase__,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''],				)

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

						def collate_fn(lowerCamelCase):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__lowerCAmelCase							       =				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase							       =				1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase							       =				8
												else:
																		__lowerCAmelCase							       =				None

												return tokenizer.pad(
												    UpperCamelCase__,				padding='''longest''',				max_length=UpperCamelCase__,				pad_to_multiple_of=UpperCamelCase__,				return_tensors='''pt''',				)

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

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_UpperCAmelCase				: Dict							    =				mocked_dataloaders  # noqa: F811

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',				UpperCamelCase__) == "1":
												__lowerCAmelCase							       =				2
						# Initialize accelerator
						__lowerCAmelCase							       =				Accelerator(cpu=args.cpu,				mixed_precision=args.mixed_precision)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowerCAmelCase							       =				config['''lr''']
						__lowerCAmelCase							       =				int(config['''num_epochs'''])
						__lowerCAmelCase							       =				int(config['''seed'''])
						__lowerCAmelCase							       =				int(config['''batch_size'''])

						__lowerCAmelCase							       =				evaluate.load('''glue''',				'''mrpc''')

						# New Code #
						# We now can define an inner training loop function. It should take a batch size as the only parameter,
						# and build the dataloaders in there.
						# It also gets our decorator
						@find_executable_batch_size(starting_batch_size=UpperCamelCase__)
						def inner_training_loop(lowerCamelCase):
												# And now just move everything below under this function
												# We need to bring in the Accelerator object from earlier
												nonlocal accelerator
												# And reset all of its attributes that could hold onto any memory:
												accelerator.free_memory()

												# Then we can declare the model, optimizer, and everything else:
												set_seed(UpperCamelCase__)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												__lowerCAmelCase							       =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',				return_dict=UpperCamelCase__)

												# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
												# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
												# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
												__lowerCAmelCase							       =				model.to(accelerator.device)

												# Instantiate optimizer
												__lowerCAmelCase							       =				AdamW(params=model.parameters(),				lr=UpperCamelCase__)
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_dataloaders(UpperCamelCase__,				UpperCamelCase__)

												# Instantiate scheduler
												__lowerCAmelCase							       =				get_linear_schedule_with_warmup(
												    optimizer=UpperCamelCase__,				num_warmup_steps=1_0_0,				num_training_steps=(len(UpperCamelCase__) * num_epochs),				)

												# 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.
												__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				accelerator.prepare(
												    UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__)

												# Now we train the model
												for epoch in range(UpperCamelCase__):
																		model.train()
																		for step, batch in enumerate(UpperCamelCase__):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device)
																								__lowerCAmelCase							       =				model(**UpperCamelCase__)
																								__lowerCAmelCase							       =				outputs.loss
																								accelerator.backward(UpperCamelCase__)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		model.eval()
																		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():
																														__lowerCAmelCase							       =				model(**UpperCamelCase__)
																								__lowerCAmelCase							       =				outputs.logits.argmax(dim=-1)
																								__lowerCAmelCase       ,					__lowerCAmelCase							       =				accelerator.gather_for_metrics((predictions, batch['''labels''']))
																								metric.add_batch(
																								    predictions=UpperCamelCase__,				references=UpperCamelCase__,				)

																		__lowerCAmelCase							       =				metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:""",				UpperCamelCase__)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()

def 							__magic_name__(						):
						__lowerCAmelCase							       =				argparse.ArgumentParser(description='''Simple example of training script.''')
						parser.add_argument(
						    '''--mixed_precision''',				type=UpperCamelCase__,				default=UpperCamelCase__,				choices=['''no''', '''fp16''', '''bf16''', '''fp8'''],				help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.''',				)
						parser.add_argument('''--cpu''',				action='''store_true''',				help='''If passed, will train on the CPU.''')
						__lowerCAmelCase							       =				parser.parse_args()
						__lowerCAmelCase							       =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
						training_function(UpperCamelCase__,				UpperCamelCase__)


if __name__ == "__main__":
	main()





'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				BeautifulSoup(requests.get(lowerCamelCase,				params=lowerCamelCase).content,				'''html.parser''')
						__lowerCAmelCase							       =				soup.find('''div''',				attrs={'''class''': '''gs_ri'''})
						__lowerCAmelCase							       =				div.find('''div''',				attrs={'''class''': '''gs_fl'''}).find_all('''a''')
						return anchors[2].get_text()


if __name__ == "__main__":
	_UpperCAmelCase				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

