






'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A							:							Optional[int]  							=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default="cifar10" ,       metadata={"help": "Name of a dataset from the datasets package"}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={"help": "The column name of the images in the files."}      )
    lowerCAmelCase__      				=							field(default=__UpperCAmelCase ,       metadata={"help": "A folder containing the training data."}      )
    lowerCAmelCase__      				=							field(default=__UpperCAmelCase ,       metadata={"help": "A folder containing the validation data."}      )
    lowerCAmelCase__      				=							field(
        default=0.15 ,       metadata={"help": "Percent to split off of train for validation."}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        } ,       )

    def    a__				(					self     :     str     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     {}
     if self.train_dir is not None:
      lowerCamelCase__  =     self.train_dir
     if self.validation_dir is not None:
      lowerCamelCase__  =     self.validation_dir
     lowerCamelCase__  =     data_files if data_files else None
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={
            "help": (
                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}      )
    lowerCAmelCase__      				=							field(
        default="main" ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,       )
    lowerCAmelCase__      				=							field(default=__UpperCAmelCase ,       metadata={"help": "Name or path of preprocessor config."}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default=0.75 ,       metadata={"help": "The ratio of the number of masked tokens in the input sequence."}      )
    lowerCAmelCase__      				=							field(
        default=__UpperCAmelCase ,       metadata={"help": "Whether or not to train with normalized pixel values as target."}      )
@dataclass
class 				lowercase       (							__UpperCAmelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default=1e-3 ,       metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}      )






def    lowerCamelCase_     (    lowercase__)     ->   Dict:
 lowerCamelCase__  =     torch.stack([example["pixel_values"] for example in examples])
 return {"pixel_values": pixel_values}






def    lowerCamelCase_     (    )     ->   Optional[Any]:
 lowerCamelCase__  =     HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))
 if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
 else:
  lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("run_mae"				,     __lowerCAmelCase				,     __lowerCAmelCase)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,     datefmt="%m/%d/%Y %H:%M:%S"				,     handlers=[logging.StreamHandler(sys.stdout)]				,     )

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 lowerCamelCase__  =     training_args.get_process_log_level()
 logger.setLevel(__lowerCAmelCase)
 transformers.utils.logging.set_verbosity(__lowerCAmelCase)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
 logger.info(F'''Training/evaluation parameters {training_args}''')

 # Detecting last checkpoint.
 lowerCamelCase__  =     None
 if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCamelCase__  =     get_last_checkpoint(training_args.output_dir)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
   raise ValueError(
       F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       "Use --overwrite_output_dir to overcome.")
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       "the `--output_dir` or add `--overwrite_output_dir` to train from scratch.")

  # Initialize our dataset.
 lowerCamelCase__  =     load_dataset(
     data_args.dataset_name				,     data_args.dataset_config_name				,     data_files=data_args.data_files				,     cache_dir=model_args.cache_dir				,     use_auth_token=True if model_args.use_auth_token else None				,     )

 # If we don't have a validation split, split off a percentage of train as validation.
 lowerCamelCase__  =     None if "validation" in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split				,     __lowerCAmelCase) and data_args.train_val_split > 0.0:
  lowerCamelCase__  =     ds["train"].train_test_split(data_args.train_val_split)
  lowerCamelCase__  =     split["train"]
  lowerCamelCase__  =     split["test"]

 # Load pretrained model and image processor
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowerCamelCase__  =     {
     "cache_dir": model_args.cache_dir,
     "revision": model_args.model_revision,
     "use_auth_token": True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  lowerCamelCase__  =     ViTMAEConfig.from_pretrained(model_args.config_name				,     **__lowerCAmelCase)
 elif model_args.model_name_or_path:
  lowerCamelCase__  =     ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,     **__lowerCAmelCase)
 else:
  lowerCamelCase__  =     ViTMAEConfig()
  logger.warning("You are instantiating a new config instance from scratch.")
  if model_args.config_overrides is not None:
   logger.info(F'''Overriding config: {model_args.config_overrides}''')
   config.update_from_string(model_args.config_overrides)
   logger.info(F'''New config: {config}''')

  # adapt config
 config.update(
     {
         "mask_ratio": model_args.mask_ratio,
         "norm_pix_loss": model_args.norm_pix_loss,
     })

 # create image processor
 if model_args.image_processor_name:
  lowerCamelCase__  =     ViTImageProcessor.from_pretrained(model_args.image_processor_name				,     **__lowerCAmelCase)
 elif model_args.model_name_or_path:
  lowerCamelCase__  =     ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,     **__lowerCAmelCase)
 else:
  lowerCamelCase__  =     ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  lowerCamelCase__  =     ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path				,     from_tf=bool(".ckpt" in model_args.model_name_or_path)				,     config=__lowerCAmelCase				,     cache_dir=model_args.cache_dir				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     )
 else:
  logger.info("Training new model from scratch")
  lowerCamelCase__  =     ViTMAEForPreTraining(__lowerCAmelCase)

 if training_args.do_train:
  lowerCamelCase__  =     ds["train"].column_names
 else:
  lowerCamelCase__  =     ds["validation"].column_names

 if data_args.image_column_name is not None:
  lowerCamelCase__  =     data_args.image_column_name
 elif "image" in column_names:
  lowerCamelCase__  =     "image"
 elif "img" in column_names:
  lowerCamelCase__  =     "img"
 else:
  lowerCamelCase__  =     column_names[0]

 # transformations as done in original MAE paper
 # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
 if "shortest_edge" in image_processor.size:
  lowerCamelCase__  =     image_processor.size["shortest_edge"]
 else:
  lowerCamelCase__  =     (image_processor.size["height"], image_processor.size["width"])
 lowerCamelCase__  =     Compose(
     [
         Lambda(lambda lowercase__: img.convert("RGB") if img.mode != "RGB" else img),
         RandomResizedCrop(__lowerCAmelCase				,     scale=(0.2, 1.0)				,     interpolation=InterpolationMode.BICUBIC),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean				,     std=image_processor.image_std),
     ])

 def preprocess_images(lowercase__):
  lowerCamelCase__  =     [transforms(__lowerCAmelCase) for image in examples[image_column_name]]
  return examples

 if training_args.do_train:
  if "train" not in ds:
   raise ValueError("--do_train requires a train dataset")
  if data_args.max_train_samples is not None:
   lowerCamelCase__  =     ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
  # Set the training transforms
  ds["train"].set_transform(__lowerCAmelCase)

 if training_args.do_eval:
  if "validation" not in ds:
   raise ValueError("--do_eval requires a validation dataset")
  if data_args.max_eval_samples is not None:
   lowerCamelCase__  =     (
       ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
   )
  # Set the validation transforms
  ds["validation"].set_transform(__lowerCAmelCase)

 # Compute absolute learning rate
 lowerCamelCase__  =     (
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  lowerCamelCase__  =     training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 lowerCamelCase__  =     Trainer(
     model=__lowerCAmelCase				,     args=__lowerCAmelCase				,     train_dataset=ds["train"] if training_args.do_train else None				,     eval_dataset=ds["validation"] if training_args.do_eval else None				,     tokenizer=__lowerCAmelCase				,     data_collator=__lowerCAmelCase				,     )

 # Training
 if training_args.do_train:
  lowerCamelCase__  =     None
  if training_args.resume_from_checkpoint is not None:
   lowerCamelCase__  =     training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   lowerCamelCase__  =     last_checkpoint
  lowerCamelCase__  =     trainer.train(resume_from_checkpoint=__lowerCAmelCase)
  trainer.save_model()
  trainer.log_metrics("train"				,     train_result.metrics)
  trainer.save_metrics("train"				,     train_result.metrics)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  lowerCamelCase__  =     trainer.evaluate()
  trainer.log_metrics("eval"				,     __lowerCAmelCase)
  trainer.save_metrics("eval"				,     __lowerCAmelCase)

 # Write model card and (optionally) push to hub
 lowerCamelCase__  =     {
     "tasks": "masked-auto-encoding",
     "dataset": data_args.dataset_name,
     "tags": ["masked-auto-encoding"],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**__lowerCAmelCase)
 else:
  trainer.create_model_card(**__lowerCAmelCase)






def    lowerCamelCase_     (    lowercase__)     ->   List[str]:
 main()


if __name__ == "__main__":
 main()









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

