


'''simple docstring'''



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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

A__						:   Union[str, Any]				=    16
A__						:   Dict				=    32



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Accelerator ,       UpperCAmelCase_       :							int = 16				)	->						str:
     __lowerCamelCase  :      Union[str, Any]														=						AutoTokenizer.from_pretrained('bert-base-cased'				)
     __lowerCamelCase  :      Any														=						load_dataset('glue' ,       'mrpc'				)

     def tokenize_function(UpperCAmelCase_       :							Optional[Any]				):
          # max_length=None => use the model max length (it's actually the default)
          __lowerCamelCase  :      Optional[Any]														=						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  :      Union[str, Any]														=						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  :      Tuple														=						tokenized_datasets.rename_column('label' ,       'labels'				)

     def collate_fn(UpperCAmelCase_       :							Union[str, Any]				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowerCamelCase  :      Dict														=						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  :      List[Any]														=						16
          elif accelerator.mixed_precision != "no":
               __lowerCamelCase  :      Optional[int]														=						8
          else:
               __lowerCamelCase  :      Any														=						None

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

     # Instantiate dataloaders.
     __lowerCamelCase  :      Optional[int]														=						DataLoader(
         tokenized_datasets['train'] ,       shuffle=UpperCAmelCase_ ,       collate_fn=UpperCAmelCase_ ,       batch_size=UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						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__						:   Union[str, Any]				=    mocked_dataloaders  # noqa: F811



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							List[Any]				)	->						Union[str, Any]:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS' ,       UpperCAmelCase_				) == "1":
          __lowerCamelCase  :      Tuple														=						2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __lowerCamelCase  :      str														=						Accelerator(
              cpu=args.cpu ,       mixed_precision=args.mixed_precision ,       log_with='all' ,       project_dir=args.project_dir				)
     else:
          __lowerCamelCase  :      int														=						Accelerator(cpu=args.cpu ,       mixed_precision=args.mixed_precision				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCamelCase  :      Optional[Any]														=						config['lr']
     __lowerCamelCase  :      int														=						int(config['num_epochs']				)
     __lowerCamelCase  :      Union[str, Any]														=						int(config['seed']				)
     __lowerCamelCase  :      List[str]														=						int(config['batch_size']				)
     set_seed(UpperCAmelCase_				)

     __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						get_dataloaders(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						evaluate.load('glue' ,       'mrpc'				)

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

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCamelCase  :      Optional[int]														=						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  :      Dict														=						model.to(accelerator.device				)

     # Instantiate optimizer
     __lowerCamelCase  :      int														=						AdamW(params=model.parameters() ,       lr=UpperCAmelCase_				)

     # Instantiate scheduler
     __lowerCamelCase  :      str														=						get_linear_schedule_with_warmup(
         optimizer=UpperCAmelCase_ ,       num_warmup_steps=1_00 ,       num_training_steps=(len(UpperCAmelCase_				) * 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.
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						accelerator.prepare(
         UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __lowerCamelCase  :      List[str]														=						os.path.split(UpperCAmelCase_				)[-1].split('.'				)[0]
          accelerator.init_trackers(UpperCAmelCase_ ,       UpperCAmelCase_				)

     # Now we train the model
     for epoch in range(UpperCAmelCase_				):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __lowerCamelCase  :      Dict														=						0
          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  :      Union[str, Any]														=						model(**UpperCAmelCase_				)
               __lowerCamelCase  :      Dict														=						outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __lowerCamelCase  :      Union[str, Any]														=						loss / gradient_accumulation_steps
               accelerator.backward(UpperCAmelCase_				)
               if step % gradient_accumulation_steps == 0:
                    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` (the default).
               batch.to(accelerator.device				)
               with torch.no_grad():
                    __lowerCamelCase  :      Any														=						model(**UpperCAmelCase_				)
               __lowerCamelCase  :      List[Any]														=						outputs.logits.argmax(dim=-1				)
               __lowerCamelCase							,			__lowerCamelCase  :      Any														=						accelerator.gather_for_metrics((predictions, batch['labels'])				)
               metric.add_batch(
                   predictions=UpperCAmelCase_ ,       references=UpperCAmelCase_ ,       )

          __lowerCamelCase  :      Optional[Any]														=						metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:' ,       UpperCAmelCase_				)

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(UpperCAmelCase_				),
                       'epoch': epoch,
                   } ,       step=UpperCAmelCase_ ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()



def      UpperCAmelCase__   (      )	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						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.'				)
     parser.add_argument(
         '--with_tracking' ,       action='store_true' ,       help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,       )
     parser.add_argument(
         '--project_dir' ,       type=UpperCAmelCase_ ,       default='logs' ,       help='Location on where to store experiment tracking logs` and relevent project information' ,       )
     __lowerCamelCase  :      Union[str, Any]														=						parser.parse_args()
     __lowerCamelCase  :      Optional[Any]														=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(UpperCAmelCase_ ,       UpperCAmelCase_				)


if __name__ == "__main__":
      main()










'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



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


A__						:   str				=    """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()))






