




"""simple docstring"""


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
#
########################################################################


A_      			=       16
A_      			=       32


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Optional[int]	,     snake_case__							:      int	,     snake_case__							:      Tuple	,     snake_case__							:      Optional[int] = 16					):
  """simple docstring"""

  _snake_case							:	Any			=						AutoTokenizer.from_pretrained("""bert-base-cased"""					)
  _snake_case							:	List[str]			=						DatasetDict(
      {
          """train""": dataset["""train"""].select(__UpperCamelCase					),
          """validation""": dataset["""train"""].select(__UpperCamelCase					),
          """test""": dataset["""validation"""],
      }					)

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

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

    return tokenizer.pad(
        __UpperCamelCase	,     padding="""longest"""	,     max_length=__UpperCamelCase	,     pad_to_multiple_of=__UpperCamelCase	,     return_tensors="""pt"""	,     )

  # Instantiate dataloaders.
  _snake_case							:	Tuple			=						DataLoader(
      tokenized_datasets["""train"""]	,     shuffle=__UpperCamelCase	,     collate_fn=__UpperCamelCase	,     batch_size=__UpperCamelCase					)
  _snake_case							:	Dict			=						DataLoader(
      tokenized_datasets["""validation"""]	,     shuffle=__UpperCamelCase	,     collate_fn=__UpperCamelCase	,     batch_size=__UpperCamelCase					)

  _snake_case							:	Any			=						DataLoader(
      tokenized_datasets["""test"""]	,     shuffle=__UpperCamelCase	,     collate_fn=__UpperCamelCase	,     batch_size=__UpperCamelCase					)

  return train_dataloader, eval_dataloader, test_dataloader


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						[]
  # Download the dataset
  _snake_case							:	Dict			=						load_dataset("""glue"""	,     """mrpc"""					)
  # Create our splits
  _snake_case							:	int			=						StratifiedKFold(n_splits=int(args.num_folds					)					)
  # Initialize accelerator
  _snake_case							:	Tuple			=						Accelerator(cpu=args.cpu	,     mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _snake_case							:	Any			=						config["""lr"""]
  _snake_case							:	Dict			=						int(config["""num_epochs"""]					)
  _snake_case							:	Union[str, Any]			=						int(config["""seed"""]					)
  _snake_case							:	Dict			=						int(config["""batch_size"""]					)

  _snake_case							:	int			=						evaluate.load("""glue"""	,     """mrpc"""					)

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

  set_seed(__UpperCamelCase					)

  # New Code #
  # Create our folds:
  _snake_case							:	Optional[Any]			=						kfold.split(np.zeros(datasets["""train"""].num_rows					)	,     datasets["""train"""]["""label"""]					)
  _snake_case							:	Any			=						[]
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(__UpperCamelCase					):
    _snake_case	,      _snake_case	,      _snake_case							:	Optional[Any]			=						get_fold_dataloaders(
        __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _snake_case							:	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).
    _snake_case							:	Dict			=						model.to(accelerator.device					)

    # Instantiate optimizer
    _snake_case							:	Dict			=						AdamW(params=model.parameters()	,     lr=__UpperCamelCase					)

    # Instantiate scheduler
    _snake_case							:	Dict			=						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.
    _snake_case	,      _snake_case	,      _snake_case	,      _snake_case	,      _snake_case							:	Optional[Any]			=						accelerator.prepare(
        __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase					)

    # Now we train the model
    for epoch in range(__UpperCamelCase					):
      model.train()
      for step, batch in enumerate(__UpperCamelCase					):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device					)
        _snake_case							:	Dict			=						model(**__UpperCamelCase					)
        _snake_case							:	Union[str, Any]			=						outputs.loss
        _snake_case							:	List[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`.
        batch.to(accelerator.device					)
        with torch.no_grad():
          _snake_case							:	Optional[Any]			=						model(**__UpperCamelCase					)
        _snake_case							:	int			=						outputs.logits.argmax(dim=-1					)
        _snake_case	,      _snake_case							:	Optional[int]			=						accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
        metric.add_batch(
            predictions=__UpperCamelCase	,     references=__UpperCamelCase	,     )

      _snake_case							:	int			=						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"	,     __UpperCamelCase					)

    # New Code #
    # We also run predictions on the test set at the very end
    _snake_case							:	Dict			=						[]
    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():
        _snake_case							:	Dict			=						model(**__UpperCamelCase					)
      _snake_case							:	Optional[int]			=						outputs.logits
      _snake_case	,      _snake_case							:	Tuple			=						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(__UpperCamelCase	,     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:
  _snake_case							:	Tuple			=						torch.cat(__UpperCamelCase	,     dim=0					)
  _snake_case							:	str			=						torch.stack(__UpperCamelCase	,     dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
  _snake_case							:	int			=						metric.compute(predictions=__UpperCamelCase	,     references=__UpperCamelCase					)
  accelerator.print("""Average test metrics from all folds:"""	,     __UpperCamelCase					)


def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	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."""					)
  # New Code #
  parser.add_argument("""--num_folds"""	,     type=__UpperCamelCase	,     default=3	,     help="""The number of splits to perform across the dataset"""					)
  _snake_case							:	Tuple			=						parser.parse_args()
  _snake_case							:	Optional[int]			=						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(__UpperCamelCase	,     __UpperCamelCase					)


if __name__ == "__main__":
     main()








"""simple docstring"""


import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[Any],   a_:	str = "cpu",   a_:	str = "openai/clip-vit-large-patch14"	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						device
   _snake_case							:	str			=						CLIPTokenizerFast.from_pretrained(a_	)
   _snake_case							:	Union[str, Any]			=						[0.48_145_466, 0.4_578_275, 0.40_821_073]
   _snake_case							:	Optional[int]			=						[0.26_862_954, 0.26_130_258, 0.27_577_711]
   _snake_case							:	str			=						torchvision.transforms.Normalize(self.image_mean,   self.image_std	)
   _snake_case							:	Optional[int]			=						torchvision.transforms.Resize(224	)
   _snake_case							:	str			=						torchvision.transforms.CenterCrop(224	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.resize(a_	)
   _snake_case							:	List[Any]			=						self.center_crop(a_	)
   _snake_case							:	Optional[Any]			=						self.normalize(a_	)
   return images


 def __call__(       self:	Any,   a_:	Optional[int]=None,   a_:	str=None,   **a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.tokenizer(text=a_,   **a_	)
   _snake_case							:	Any			=						self.preprocess_img(a_	)
   _snake_case							:	Union[str, Any]			=						{key: value.to(self.device	) for (key, value) in encoding.items()}
   return encoding







class       lowercase(  nn.Module				):







 '''simple docstring'''





 def __init__(       self:	List[Any],   a_:	List[Any]=10,   a_:	Optional[Any]=0.01,   a_:	List[str]=None,   a_:	str=None,   a_:	Any=None,   a_:	Tuple=None,   a_:	List[str]=None,   a_:	List[str]=None,   a_:	str=False,   a_:	List[str]=True,   a_:	Any="image",   a_:	Optional[Any]=True,   a_:	Dict=False,   a_:	List[str]=False,   a_:	Optional[int]=False,   ):




   '''simple docstring'''



   super().__init__()
   _snake_case							:	int			=						None
   _snake_case							:	List[str]			=						device if device else get_device()
   if vqgan:
     _snake_case							:	Any			=						vqgan
   else:
     _snake_case							:	Optional[Any]			=						load_vqgan(self.device,   conf_path=a_,   ckpt_path=a_	)
   self.vqgan.eval()
   if clip:
     _snake_case							:	Tuple			=						clip
   else:
     _snake_case							:	Optional[Any]			=						CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""	)
   self.clip.to(self.device	)
   _snake_case							:	List[str]			=						ProcessorGradientFlow(device=self.device	)

   _snake_case							:	Union[str, Any]			=						iterations
   _snake_case							:	Dict			=						lr
   _snake_case							:	Optional[int]			=						log
   _snake_case							:	List[str]			=						make_grid
   _snake_case							:	Union[str, Any]			=						return_val
   _snake_case							:	List[str]			=						quantize
   _snake_case							:	List[str]			=						self.vqgan.decoder.z_shape


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str=None,   a_:	Dict=None,   a_:	Dict=5,   a_:	Dict=True	):




   '''simple docstring'''



   _snake_case							:	Dict			=						[]
   if output_path is None:
     _snake_case							:	Tuple			=						"""./animation.gif"""
   if input_path is None:
     _snake_case							:	Any			=						self.save_path
   _snake_case							:	Optional[int]			=						sorted(glob(input_path + """/*"""	)	)
   if not len(a_	):
     raise ValueError(
         """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
         """ function?)"""	)
   if len(a_	) == 1:
     print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""	)
   _snake_case							:	List[Any]			=						total_duration / len(a_	)
   _snake_case							:	Optional[Any]			=						[frame_duration] * len(a_	)
   if extend_frames:
     _snake_case							:	Optional[int]			=						1.5
     _snake_case							:	int			=						3
   for file_name in paths:
     if file_name.endswith(""".png"""	):
       images.append(imageio.imread(a_	)	)
   imageio.mimsave(a_,   a_,   duration=a_	)
   print(f"gif saved to {output_path}"	)


 def 				UpperCamelCase_     (       self:	str,   a_:	Tuple=None,   a_:	Optional[Any]=None	):




   '''simple docstring'''



   if not (path or img):
     raise ValueError("""Input either path or tensor"""	)
   if img is not None:
     raise NotImplementedError
   _snake_case							:	int			=						preprocess(Image.open(a_	),   target_image_size=256	).to(self.device	)
   _snake_case							:	int			=						preprocess_vqgan(a_	)
   _snake_case	,      *_snake_case							:	List[Any]			=						self.vqgan.encode(a_	)
   return z


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.latent.detach().requires_grad_()
   _snake_case							:	Tuple			=						base_latent + transform_vector
   if self.quantize:
     _snake_case	,      *_snake_case							:	Any			=						self.vqgan.quantize(a_	)
   else:
     _snake_case							:	List[Any]			=						trans_latent
   return self.vqgan.decode(a_	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Any,   a_:	Union[str, Any],   a_:	Dict=None	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.clip_preprocessor(text=a_,   images=a_,   return_tensors="""pt""",   padding=a_	)
   _snake_case							:	Any			=						self.clip(**a_	)
   _snake_case							:	str			=						clip_outputs.logits_per_image
   if weights is not None:
     _snake_case							:	Any			=						similarity_logits * weights
   return similarity_logits.sum()


 def 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	List[str],   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self._get_clip_similarity(pos_prompts["""prompts"""],   a_,   weights=(1 / pos_prompts["""weights"""])	)
   if neg_prompts:
     _snake_case							:	List[str]			=						self._get_clip_similarity(neg_prompts["""prompts"""],   a_,   weights=neg_prompts["""weights"""]	)
   else:
     _snake_case							:	Tuple			=						torch.tensor([1],   device=self.device	)
   _snake_case							:	int			=						-torch.log(a_	) + torch.log(a_	)
   return loss


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Tuple,   a_:	Union[str, Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						torch.randn_like(self.latent,   requires_grad=a_,   device=self.device	)
   _snake_case							:	Dict			=						torch.optim.Adam([vector],   lr=self.lr	)

   for i in range(self.iterations	):
     optim.zero_grad()
     _snake_case							:	str			=						self._add_vector(a_	)
     _snake_case							:	List[Any]			=						loop_post_process(a_	)
     _snake_case							:	List[Any]			=						self._get_CLIP_loss(a_,   a_,   a_	)
     print("""CLIP loss""",   a_	)
     if self.log:
       wandb.log({"""CLIP Loss""": clip_loss}	)
     clip_loss.backward(retain_graph=a_	)
     optim.step()
     if self.return_val == "image":
       yield custom_to_pil(transformed_img[0]	)
     else:
       yield vector


 def 				UpperCamelCase_     (       self:	int,   a_:	Any,   a_:	Union[str, Any],   a_:	Optional[int]	):




   '''simple docstring'''



   wandb.init(reinit=a_,   project="""face-editor"""	)
   wandb.config.update({"""Positive Prompts""": positive_prompts}	)
   wandb.config.update({"""Negative Prompts""": negative_prompts}	)
   wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}	)
   if image_path:
     _snake_case							:	Any			=						Image.open(a_	)
     _snake_case							:	str			=						image.resize((256, 256)	)
     wandb.log("""Original Image""",   wandb.Image(a_	)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any]	):




   '''simple docstring'''



   if not prompts:
     return []
   _snake_case							:	List[str]			=						[]
   _snake_case							:	Tuple			=						[]
   if isinstance(a_,   a_	):
     _snake_case							:	Union[str, Any]			=						[prompt.strip() for prompt in prompts.split("""|"""	)]
   for prompt in prompts:
     if isinstance(a_,   (tuple, list)	):
       _snake_case							:	List[Any]			=						prompt[0]
       _snake_case							:	Optional[Any]			=						float(prompt[1]	)
     elif ":" in prompt:
       _snake_case	,      _snake_case							:	List[Any]			=						prompt.split(""":"""	)
       _snake_case							:	str			=						float(a_	)
     else:
       _snake_case							:	int			=						prompt
       _snake_case							:	Union[str, Any]			=						1.0
     processed_prompts.append(a_	)
     weights.append(a_	)
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(a_,   device=self.device	),
   }


 def 				UpperCamelCase_     (       self:	Dict,   a_:	List[Any],   a_:	List[Any]=None,   a_:	Optional[Any]=None,   a_:	Optional[Any]=True,   a_:	Dict=False,   a_:	Optional[Any]=True,   a_:	Optional[Any]=True,   a_:	Any=None,   ):




   '''simple docstring'''



   if image_path:
     _snake_case							:	Union[str, Any]			=						self._get_latent(a_	)
   else:
     _snake_case							:	Any			=						torch.randn(self.latent_dim,   device=self.device	)
   if self.log:
     self._init_logging(a_,   a_,   a_	)

   assert pos_prompts, "You must provide at least one positive prompt."
   _snake_case							:	str			=						self.process_prompts(a_	)
   _snake_case							:	Dict			=						self.process_prompts(a_	)
   if save_final and save_path is None:
     _snake_case							:	Any			=						os.path.join("""./outputs/""",   """_""".join(pos_prompts["""prompts"""]	)	)
   if not os.path.exists(a_	):
     os.makedirs(a_	)
   else:
     _snake_case							:	List[Any]			=						save_path + """_""" + get_timestamp()
     os.makedirs(a_	)
   _snake_case							:	Optional[Any]			=						save_path

   _snake_case							:	List[Any]			=						self.vqgan.decode(self.latent	)[0]
   if show_intermediate:
     print("""Original Image"""	)
     show_pil(custom_to_pil(a_	)	)

   _snake_case							:	List[Any]			=						loop_post_process(a_	)
   for iter, transformed_img in enumerate(self._optimize_CLIP(a_,   a_,   a_	)	):
     if show_intermediate:
       show_pil(a_	)
     if save_intermediate:
       transformed_img.save(os.path.join(self.save_path,   f"iter_{iter:03d}.png"	)	)
     if self.log:
       wandb.log({"""Image""": wandb.Image(a_	)}	)
   if show_final:
     show_pil(a_	)
   if save_final:
     transformed_img.save(os.path.join(self.save_path,   f"iter_{iter:03d}_final.png"	)	)


