from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")

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			: Any							   =		16
__lowerCamelCase			: List[Any]							   =		32



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int = 16 ,     __UpperCamelCase    :       str = "bert-base-cased"	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      load_dataset("""glue""" ,     """mrpc"""	)

  def tokenize_function(__UpperCamelCase    :       Optional[Any]	):
    # max_length=None => use the model max length (it's actually the default)
    SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      datasets.map(
      __UpperCamelCase ,     batched=__UpperCamelCase ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=__UpperCamelCase	)

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

  def collate_fn(__UpperCamelCase    :       List[Any]	):
    # 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(__UpperCamelCase ,     padding="""max_length""" ,     max_length=1_28 ,     return_tensors="""pt"""	)
    return tokenizer.pad(__UpperCamelCase ,     padding="""longest""" ,     return_tensors="""pt"""	)

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""train"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""validation"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)

  return train_dataloader, eval_dataloader



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__										=      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	)
    with torch.no_grad():
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      outputs.logits.argmax(dim=-1	)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.gather(
        (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(__UpperCamelCase	) - 1:
        SCREAMING_SNAKE_CASE__										=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
        SCREAMING_SNAKE_CASE__										=      references[: len(eval_dataloader.dataset	) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase ,     references=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      metric.compute()
  return eval_metric["accuracy"]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  SCREAMING_SNAKE_CASE__										=      config["""lr"""]
  SCREAMING_SNAKE_CASE__										=      int(config["""num_epochs"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""seed"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""batch_size"""]	)
  SCREAMING_SNAKE_CASE__										=      args.model_name_or_path

  set_seed(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_dataloaders(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE__										=      AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,     return_dict=__UpperCamelCase	)

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__										=      (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE__										=      optimizer_cls(params=model.parameters() ,     lr=__UpperCamelCase	)

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__										=      accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      (len(__UpperCamelCase	) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    SCREAMING_SNAKE_CASE__										=      get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase ,     num_warmup_steps=0 ,     num_training_steps=__UpperCamelCase ,     )
  else:
    SCREAMING_SNAKE_CASE__										=      DummyScheduler(__UpperCamelCase ,     total_num_steps=__UpperCamelCase ,     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.
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.prepare(
      __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # We need to keep track of how many total steps we have iterated over
  SCREAMING_SNAKE_CASE__										=      0
  # We also need to keep track of the stating epoch so files are named properly
  SCREAMING_SNAKE_CASE__										=      0
  SCREAMING_SNAKE_CASE__										=      evaluate.load("""glue""" ,     """mrpc"""	)
  SCREAMING_SNAKE_CASE__										=      num_epochs

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__										=      args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint	)
    SCREAMING_SNAKE_CASE__										=      args.resume_from_checkpoint.split("""epoch_"""	)[1]
    SCREAMING_SNAKE_CASE__										=      """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__										=      int(__UpperCamelCase	) + 1
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint performance:""" ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint's scheduler's lr:""" ,     lr_scheduler.get_lr()[0]	)
    accelerator.print("""resumed optimizers's lr:""" ,     optimizer.param_groups[0]["""lr"""]	)
    with open(os.path.join(args.output_dir ,     f"""state_{starting_epoch-1}.json"""	) ,     """r"""	) as f:
      SCREAMING_SNAKE_CASE__										=      json.load(__UpperCamelCase	)
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  SCREAMING_SNAKE_CASE__										=      {}
  for epoch in range(__UpperCamelCase ,     __UpperCamelCase	):
    model.train()
    for step, batch in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      outputs.loss
      SCREAMING_SNAKE_CASE__										=      loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__										=      f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__										=      os.path.join(args.output_dir ,     __UpperCamelCase	)
    accelerator.save_state(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      accuracy
    SCREAMING_SNAKE_CASE__										=      lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__										=      optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__										=      epoch
    SCREAMING_SNAKE_CASE__										=      overall_step
    accelerator.print(f"""epoch {epoch}:""" ,     __UpperCamelCase	)

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir ,     f"""state_{epoch}.json"""	) ,     """w"""	) as f:
        json.dump(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
  parser.add_argument(
      """--model_name_or_path""" ,     type=__UpperCamelCase ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=__UpperCamelCase ,     )
  parser.add_argument(
      """--output_dir""" ,     type=__UpperCamelCase ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
  parser.add_argument(
      """--resume_from_checkpoint""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If the training should continue from a checkpoint folder.""" ,     )
  parser.add_argument(
      """--partial_train_epoch""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If passed, the training will stop after this number of epochs.""" ,     )
  parser.add_argument(
      """--num_epochs""" ,     type=__UpperCamelCase ,     default=2 ,     help="""Number of train epochs.""" ,     )
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  SCREAMING_SNAKE_CASE__										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(__UpperCamelCase ,     __UpperCamelCase	)


if __name__ == "__main__":
   main()
