"""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


lowercase__					       =	16
lowercase__					       =	32







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = 16		,     _SCREAMING_SNAKE_CASE = "bert-base-cased"   )	->Tuple:
  a__:     Optional[int]										=	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
  a__:     str										=	load_dataset('glue'		,     'mrpc'   )

  def tokenize_function(_SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    a__:     Optional[Any]										=	tokenizer(examples['sentence1']		,     examples['sentence2']		,     truncation=_SCREAMING_SNAKE_CASE		,     max_length=_SCREAMING_SNAKE_CASE   )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  a__:     Optional[Any]										=	datasets.map(
      _SCREAMING_SNAKE_CASE		,     batched=_SCREAMING_SNAKE_CASE		,     remove_columns=['idx', 'sentence1', 'sentence2']		,     load_from_cache_file=_SCREAMING_SNAKE_CASE   )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a__:     Optional[Any]										=	tokenized_datasets.rename_column('label'		,     'labels'   )

  def collate_fn(_SCREAMING_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(_SCREAMING_SNAKE_CASE		,     padding='max_length'		,     max_length=128		,     return_tensors='pt'   )
    return tokenizer.pad(_SCREAMING_SNAKE_CASE		,     padding='longest'		,     return_tensors='pt'   )

  # Instantiate dataloaders.
  a__:     Any										=	DataLoader(
      tokenized_datasets['train']		,     shuffle=_SCREAMING_SNAKE_CASE		,     collate_fn=_SCREAMING_SNAKE_CASE		,     batch_size=_SCREAMING_SNAKE_CASE   )
  a__:     Optional[int]										=	DataLoader(
      tokenized_datasets['validation']		,     shuffle=_SCREAMING_SNAKE_CASE		,     collate_fn=_SCREAMING_SNAKE_CASE		,     batch_size=_SCREAMING_SNAKE_CASE   )

  return train_dataloader, eval_dataloader







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  # Initialize accelerator
  a__:     List[str]										=	Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__:     int										=	config['lr']
  a__:     str										=	int(config['num_epochs']   )
  a__:     str										=	int(config['seed']   )
  a__:     Tuple										=	int(config['batch_size']   )
  a__:     List[str]										=	args.model_name_or_path

  set_seed(_SCREAMING_SNAKE_CASE   )
  a__							,    a__:     List[str]										=	get_dataloaders(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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

  # Instantiate optimizer
  a__:     Optional[int]										=	(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  a__:     Optional[int]										=	optimizer_cls(params=model.parameters()		,     lr=_SCREAMING_SNAKE_CASE   )

  if accelerator.state.deepspeed_plugin is not None:
    a__:     Dict										=	accelerator.state.deepspeed_plugin.deepspeed_config[
        'gradient_accumulation_steps'
    ]
  else:
    a__:     str										=	1
  a__:     Union[str, Any]										=	(len(_SCREAMING_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
  ):
    a__:     Tuple										=	get_linear_schedule_with_warmup(
        optimizer=_SCREAMING_SNAKE_CASE		,     num_warmup_steps=0		,     num_training_steps=_SCREAMING_SNAKE_CASE		,     )
  else:
    a__:     Tuple										=	DummyScheduler(_SCREAMING_SNAKE_CASE		,     total_num_steps=_SCREAMING_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.
  a__							,    a__							,    a__							,    a__							,    a__:     Union[str, Any]										=	accelerator.prepare(
      _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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

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

      overall_step += 1

    model.eval()
    a__:     List[str]										=	0
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device   )
      with torch.no_grad():
        a__:     int										=	model(**_SCREAMING_SNAKE_CASE   )
      a__:     Optional[int]										=	outputs.logits.argmax(dim=-1   )
      # It is slightly faster to call this once, than multiple times
      a__							,    a__:     List[str]										=	accelerator.gather(
          (predictions, batch['labels'])   )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(_SCREAMING_SNAKE_CASE   ) - 1:
          a__:     Any										=	predictions[: len(eval_dataloader.dataset   ) - samples_seen]
          a__:     Union[str, Any]										=	references[: len(eval_dataloader.dataset   ) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=_SCREAMING_SNAKE_CASE		,     references=_SCREAMING_SNAKE_CASE		,     )

    a__:     Union[str, Any]										=	metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'		,     _SCREAMING_SNAKE_CASE   )
    a__:     Union[str, Any]										=	eval_metric['accuracy']

    if best_performance < eval_metric["accuracy"]:
      a__:     Optional[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(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->int:
  a__:     str										=	argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
  parser.add_argument(
      '--model_name_or_path'		,     type=_SCREAMING_SNAKE_CASE		,     default='bert-base-cased'		,     help='Path to pretrained model or model identifier from huggingface.co/models.'		,     required=_SCREAMING_SNAKE_CASE		,     )
  parser.add_argument(
      '--output_dir'		,     type=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,     default=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,     default=3		,     help='Number of train epochs.'		,     )
  a__:     List[Any]										=	parser.parse_args()
  a__:     Any										=	{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
  training_function(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 main()



"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


