




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


lowerCamelCase				:		Tuple     =   16
lowerCamelCase				:		Dict     =   32

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Accelerator	,							lowercase	:       DatasetDict	,							lowercase	:       List[int]	,							lowercase	:       List[int]	,							lowercase	:       int = 16     ):


      '''simple docstring'''



      lowerCamelCase_		     =    AutoTokenizer.from_pretrained('bert-base-cased'     )
      lowerCamelCase_		     =    DatasetDict(
          {
              'train': dataset['train'].select(lowercase     ),
              'validation': dataset['train'].select(lowercase     ),
              'test': dataset['validation'],
          }     )

      def tokenize_function(lowercase	:       Dict     ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase_		     =    tokenizer(examples['sentence1']	,							examples['sentence2']	,							truncation=lowercase	,							max_length=lowercase     )
            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(
                lowercase	,							batched=lowercase	,							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(lowercase	:       List[Any]     ):
            # 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(
                lowercase	,							padding='longest'	,							max_length=lowercase	,							pad_to_multiple_of=lowercase	,							return_tensors='pt'	,							)

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

      lowerCamelCase_		     =    DataLoader(
          tokenized_datasets['test']	,							shuffle=lowercase	,							collate_fn=lowercase	,							batch_size=lowercase     )

      return train_dataloader, eval_dataloader, test_dataloader

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    []
      # Download the dataset
      lowerCamelCase_		     =    load_dataset('glue'	,							'mrpc'     )
      # Create our splits
      lowerCamelCase_		     =    StratifiedKFold(n_splits=int(args.num_folds     )     )
      # 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'     )

      # If the batch size is too big we use gradient accumulation
      lowerCamelCase_		     =    1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCamelCase_		     =    batch_size // MAX_GPU_BATCH_SIZE
            lowerCamelCase_		     =    MAX_GPU_BATCH_SIZE

      set_seed(lowercase     )

      # New Code #
      # Create our folds:
      lowerCamelCase_		     =    kfold.split(np.zeros(datasets['train'].num_rows     )	,							datasets['train']['label']     )
      lowerCamelCase_		     =    []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(lowercase     ):
            lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    get_fold_dataloaders(
                lowercase	,							lowercase	,							lowercase	,							lowercase	,							)
            # 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=lowercase     )

            # 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=lowercase     )

            # Instantiate scheduler
            lowerCamelCase_		     =    get_linear_schedule_with_warmup(
                optimizer=lowercase	,							num_warmup_steps=1_00	,							num_training_steps=(len(lowercase     ) * 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_		     =    accelerator.prepare(
                lowercase	,							lowercase	,							lowercase	,							lowercase	,							lowercase     )

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

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

                  lowerCamelCase_		     =    metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"""epoch {epoch}:"""	,							lowercase     )

            # New Code #
            # We also run predictions on the test set at the very end
            lowerCamelCase_		     =    []
            for step, batch in enumerate(lowercase     ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device     )
                  with torch.no_grad():
                        lowerCamelCase_		     =    model(**lowercase     )
                  lowerCamelCase_		     =    outputs.logits
                  lowerCamelCase_     ,				lowerCamelCase_		     =    accelerator.gather_for_metrics((predictions, batch['labels'])     )
                  fold_predictions.append(predictions.cpu()     )
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()     )
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(lowercase	,							dim=0     )     )
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      lowerCamelCase_		     =    torch.cat(lowercase	,							dim=0     )
      lowerCamelCase_		     =    torch.stack(lowercase	,							dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
      lowerCamelCase_		     =    metric.compute(predictions=lowercase	,							references=lowercase     )
      accelerator.print('Average test metrics from all folds:'	,							lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      lowerCamelCase_		     =    argparse.ArgumentParser(description='Simple example of training script.'     )
      parser.add_argument(
          '--mixed_precision'	,							type=lowercase	,							default=lowercase	,							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.'     )
      # New Code #
      parser.add_argument('--num_folds'	,							type=lowercase	,							default=3	,							help='The number of splits to perform across the dataset'     )
      lowerCamelCase_		     =    parser.parse_args()
      lowerCamelCase_		     =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(lowercase	,							lowercase     )


if __name__ == "__main__":
 main()









from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
 lowerCamelCase				:		int     =   input("Enter image url: ").strip()
 print(F"""Downloading image from {url} ...""")
 lowerCamelCase				:		Tuple     =   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				:		int     =   soup.find("meta", {"property": "og:image"})["content"]
 lowerCamelCase				:		Dict     =   requests.get(image_url).content
 lowerCamelCase				:		Optional[int]     =   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}.""")



