



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# 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
#
########################################################################


a_			=					16
a_			=					32





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: int = 16							):
			__lowerCamelCase													=    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
			__lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc'''							)

			def tokenize_function(_UpperCamelCase			: int							):
						# 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(_UpperCamelCase			: Optional[int]							):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__lowerCamelCase													=    1_28 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													=    16
						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

		a_			=					mocked_dataloaders  # noqa: F811





def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: Tuple							):
			# For testing only
			if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_UpperCamelCase							) == "1":
						__lowerCamelCase													=    2
			# New Code #
			__lowerCamelCase													=    int(args.gradient_accumulation_steps							)
			# Initialize accelerator
			__lowerCamelCase													=    Accelerator(
			    cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=_UpperCamelCase							)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''							)
			# 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'''							)

			set_seed(_UpperCamelCase							)
			__lowerCamelCase							,__lowerCamelCase													=    get_dataloaders(_UpperCamelCase ,_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							)

			# Instantiate scheduler
			__lowerCamelCase													=    get_linear_schedule_with_warmup(
			    optimizer=_UpperCamelCase ,num_warmup_steps=1_00 ,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							)
									# New code #
									# We use the new `accumulate` context manager to perform gradient accumulation
									# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
									with accelerator.accumulate(_UpperCamelCase							):
												__lowerCamelCase													=    model(**_UpperCamelCase							)
												__lowerCamelCase													=    output.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							)





def 		a__			(		):
			__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.''' ,)
			# New Code #
			parser.add_argument(
			    '''--gradient_accumulation_steps''' ,type=_UpperCamelCase ,default=1 ,help='''The number of minibatches to be ran before gradients are accumulated.''' ,)
			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''': 42, '''batch_size''': 16}
			training_function(_UpperCamelCase ,_UpperCamelCase							)


if __name__ == "__main__":
		main()











from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_			=					namedtuple("""covid_data""", """cases deaths recovered""")





def 		a__			(		_UpperCamelCase			: str = "https://www.worldometers.info/coronavirus/"							):
   __lowerCamelCase													=    '''//div[@class = "maincounter-number"]/span/text()'''
   return covid_data(*html.fromstring(requests.get(_UpperCamelCase							).content							).xpath(_UpperCamelCase							)							)


a_			=					"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""





print(fmt.format(*covid_stats()))






