

import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# 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
#
########################################################################


__snake_case							:	Tuple											=       16
__snake_case							:	Dict											=       32


def  _UpperCAmelCase							(		a__   ,     a__ = 1_6):

			'''simple docstring'''



			a_		:		Optional[Any]      				=      AutoTokenizer.from_pretrained("""bert-base-cased""")
			a_		:		str      				=      load_dataset("""glue"""   ,     """mrpc""")

			def tokenize_function(a__):
						# max_length=None => use the model max length (it's actually the default)
						a_		:		List[Any]      				=      tokenizer(examples["""sentence1"""]   ,     examples["""sentence2"""]   ,     truncation=__snake_case   ,     max_length=__snake_case)
						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():
						a_		:		Dict      				=      datasets.map(
						    __snake_case   ,     batched=__snake_case   ,     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
			a_		:		Union[str, Any]      				=      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.
						a_		:		Tuple      				=      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":
									a_		:		List[str]      				=      1_6
						elif accelerator.mixed_precision != "no":
									a_		:		Dict      				=      8
						else:
									a_		:		str      				=      None

						return tokenizer.pad(
						    __snake_case   ,     padding="""longest"""   ,     max_length=__snake_case   ,     pad_to_multiple_of=__snake_case   ,     return_tensors="""pt"""   ,     )

			# Instantiate dataloaders.
			a_		:		List[str]      				=      DataLoader(
			    tokenized_datasets["""train"""]   ,     shuffle=__snake_case   ,     collate_fn=__snake_case   ,     batch_size=__snake_case   ,     drop_last=__snake_case)
			a_		:		Optional[Any]      				=      DataLoader(
			    tokenized_datasets["""validation"""]   ,     shuffle=__snake_case   ,     collate_fn=__snake_case   ,     batch_size=__snake_case   ,     drop_last=(accelerator.mixed_precision == """fp8""")   ,     )

			return train_dataloader, eval_dataloader


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			a_		:		str      				=      Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a_		:		str      				=      config["""lr"""]
			a_		:		str      				=      int(config["""num_epochs"""])
			a_		:		Tuple      				=      int(config["""seed"""])
			a_		:		Any      				=      int(config["""batch_size"""])

			a_		:		Tuple      				=      evaluate.load("""glue"""   ,     """mrpc""")

			# If the batch size is too big we use gradient accumulation
			a_		:		Dict      				=      1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						a_		:		str      				=      batch_size // MAX_GPU_BATCH_SIZE
						a_		:		Optional[Any]      				=      MAX_GPU_BATCH_SIZE

			set_seed(__snake_case)
			a_				, a_		:		List[Any]      				=      get_dataloaders(__snake_case   ,     __snake_case)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a_		:		Any      				=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,     return_dict=__snake_case)

			# 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).
			a_		:		int      				=      model.to(accelerator.device)
			# Instantiate optimizer
			a_		:		Any      				=      AdamW(params=model.parameters()   ,     lr=__snake_case)

			# Instantiate scheduler
			a_		:		List[Any]      				=      get_linear_schedule_with_warmup(
			    optimizer=__snake_case   ,     num_warmup_steps=1_0_0   ,     num_training_steps=(len(__snake_case) * num_epochs) // gradient_accumulation_steps   ,     )

			# 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_				, a_				, a_				, a_				, a_		:		List[str]      				=      accelerator.prepare(
			    __snake_case   ,     __snake_case   ,     __snake_case   ,     __snake_case   ,     __snake_case)

			# Now we train the model
			for epoch in range(__snake_case):
						model.train()
						for step, batch in enumerate(__snake_case):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									a_		:		List[str]      				=      model(**__snake_case)
									a_		:		Union[str, Any]      				=      outputs.loss
									a_		:		List[Any]      				=      loss / gradient_accumulation_steps
									accelerator.backward(__snake_case)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(__snake_case):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									with torch.no_grad():
												a_		:		List[str]      				=      model(**__snake_case)
									a_		:		Optional[int]      				=      outputs.logits.argmax(dim=-1)
									a_				, a_		:		Tuple      				=      accelerator.gather_for_metrics((predictions, batch["""labels"""]))
									metric.add_batch(
									    predictions=__snake_case   ,     references=__snake_case   ,     )

						a_		:		str      				=      metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''   ,     __snake_case)


def  _UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Any      				=      argparse.ArgumentParser(description="""Simple example of training script.""")
			parser.add_argument(
			    """--mixed_precision"""   ,     type=__snake_case   ,     default=__snake_case   ,     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.""")
			a_		:		Union[str, Any]      				=      parser.parse_args()
			a_		:		Optional[Any]      				=      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
			training_function(__snake_case   ,     __snake_case)


if __name__ == "__main__":
	main()






import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

