

'''simple docstring'''

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


A_       						=							16
A_       						=							32





def    A_  ( snake_case      ,       snake_case = 16      ,       snake_case = "bert-base-cased" ):
  SCREAMING_SNAKE_CASE:str						      =					AutoTokenizer.from_pretrained(snake_case )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					load_dataset("glue"      ,       "mrpc" )

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

  # 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:Optional[Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

  def collate_fn(snake_case ):
    # 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(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE:Optional[int]						      =					DataLoader(
      tokenized_datasets["train"]      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					DataLoader(
      tokenized_datasets["validation"]      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case )

  return train_dataloader, eval_dataloader





def    A_  ( snake_case      ,       snake_case ):
  # Initialize accelerator
  SCREAMING_SNAKE_CASE:int						      =					Accelerator()

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

  set_seed(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					get_dataloaders(snake_case      ,       snake_case      ,       snake_case )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE:Any						      =					AutoModelForSequenceClassification.from_pretrained(snake_case      ,       return_dict=snake_case )

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE:int						      =					(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE:Tuple						      =					optimizer_cls(params=model.parameters()      ,       lr=snake_case )

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE:Tuple						      =					accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					1
  SCREAMING_SNAKE_CASE:List[Any]						      =					(len(snake_case ) * 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:Optional[Any]						      =					get_linear_schedule_with_warmup(
        optimizer=snake_case      ,       num_warmup_steps=0      ,       num_training_steps=snake_case      ,       )
  else:
    SCREAMING_SNAKE_CASE:Any						      =					DummyScheduler(snake_case      ,       total_num_steps=snake_case      ,       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:Dict						      =					accelerator.prepare(
      snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

  # We need to keep track of how many total steps we have iterated over
  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  # We also need to keep track of the stating epoch so files are named properly
  SCREAMING_SNAKE_CASE:str						      =					0

  # Now we train the model
  SCREAMING_SNAKE_CASE:Optional[int]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE:Any						      =					0
  SCREAMING_SNAKE_CASE:Any						      =					{}
  for epoch in range(snake_case      ,       snake_case ):
    model.train()
    for step, batch in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(**snake_case )
      SCREAMING_SNAKE_CASE:str						      =					outputs.loss
      SCREAMING_SNAKE_CASE:Optional[int]						      =					loss / gradient_accumulation_steps
      accelerator.backward(snake_case )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

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

    SCREAMING_SNAKE_CASE:List[Any]						      =					metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''      ,       snake_case )
    SCREAMING_SNAKE_CASE:int						      =					eval_metric["accuracy"]

    if best_performance < eval_metric["accuracy"]:
      SCREAMING_SNAKE_CASE:List[Any]						      =					eval_metric["accuracy"]

  if args.performance_lower_bound is not None:
    assert (
        args.performance_lower_bound <= best_performance
    ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir      ,       "all_results.json" )      ,       "w" ) as f:
      json.dump(snake_case      ,       snake_case )





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
  parser.add_argument(
      "--model_name_or_path"      ,       type=snake_case      ,       default="bert-base-cased"      ,       help="Path to pretrained model or model identifier from huggingface.co/models."      ,       required=snake_case      ,       )
  parser.add_argument(
      "--output_dir"      ,       type=snake_case      ,       default="."      ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,       )
  parser.add_argument(
      "--performance_lower_bound"      ,       type=snake_case      ,       default=snake_case      ,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."      ,       )
  parser.add_argument(
      "--num_epochs"      ,       type=snake_case      ,       default=3      ,       help="Number of train epochs."      ,       )
  SCREAMING_SNAKE_CASE:List[Any]						      =					parser.parse_args()
  SCREAMING_SNAKE_CASE:List[str]						      =					{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(snake_case      ,       snake_case )


if __name__ == "__main__":
    main()





'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    A_       						=							{
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    A_       						=							{
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






