




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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          =			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')

__a          =			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a          =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''cifar10'''    ,						metadata={'''help''': '''Name of a dataset from the datasets package'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}    ,						)
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''A folder containing the training data.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						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=32    ,						metadata={'''help''': '''The size of the square patches to use for masking.'''}       )
    lowerCAmelCase        =				field(
        default=0.6    ,						metadata={'''help''': '''Percentage of patches to mask.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                '''value if set.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                '''value if set.'''
            )
        }    ,						)





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      {}
         if self.train_dir is not None:
              UpperCAmelCase_      :							List[Any]       =      self.train_dir
         if self.validation_dir is not None:
              UpperCAmelCase_      :							Tuple       =      self.validation_dir
         UpperCAmelCase_      :							int       =      data_files if data_files else None







@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
                '''checkpoint identifier on the hub. '''
                '''Don\'t set if you want to train a model from scratch.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_a       )}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						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=_a    ,						metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}    ,						)
    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=_a    ,						metadata={'''help''': '''Name or path of preprocessor config.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                '''with private models).'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': (
                '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Stride to use for the encoder.'''}    ,						)







class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=192       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=0.6  )			->		Dict:
         UpperCAmelCase_      :							Dict       =      input_size
         UpperCAmelCase_      :							Union[str, Any]       =      mask_patch_size
         UpperCAmelCase_      :							List[Any]       =      model_patch_size
         UpperCAmelCase_      :							Dict       =      mask_ratio

         if self.input_size % self.mask_patch_size != 0:
              raise ValueError('''Input size must be divisible by mask patch size'''  )
         if self.mask_patch_size % self.model_patch_size != 0:
              raise ValueError('''Mask patch size must be divisible by model patch size'''  )

         UpperCAmelCase_      :							List[str]       =      self.input_size // self.mask_patch_size
         UpperCAmelCase_      :							Any       =      self.mask_patch_size // self.model_patch_size

         UpperCAmelCase_      :							Dict       =      self.rand_size**2
         UpperCAmelCase_      :							List[Any]       =      int(np.ceil(self.token_count * self.mask_ratio  )  )





    def __call__(				self  )			->		List[str]:
         UpperCAmelCase_      :							Any       =      np.random.permutation(self.token_count  )[: self.mask_count]
         UpperCAmelCase_      :							int       =      np.zeros(self.token_count       ,dtype=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[Any]       =      1

         UpperCAmelCase_      :							Optional[int]       =      mask.reshape((self.rand_size, self.rand_size)  )
         UpperCAmelCase_      :							List[Any]       =      mask.repeat(self.scale       ,axis=0  ).repeat(self.scale       ,axis=1  )

         return torch.tensor(mask.flatten()  )







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      torch.stack([example['''pixel_values'''] for example in examples]				)
     UpperCAmelCase_      :							List[str]       =      torch.stack([example['''mask'''] for example in examples]				)
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     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.
          UpperCAmelCase_      :							Optional[Any]       =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          UpperCAmelCase_      :							str       =      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_mim'''			,						_lowercase			,						_lowercase				)

     # 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()

     UpperCAmelCase_      :							List[str]       =      training_args.get_process_log_level()
     logger.setLevel(_lowercase				)
     transformers.utils.logging.set_verbosity(_lowercase				)
     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.
     UpperCAmelCase_      :							Tuple       =      None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_      :							Any       =      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.
     UpperCAmelCase_      :							int       =      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.
     UpperCAmelCase_      :							str       =      None if '''validation''' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split			,						_lowercase				) and data_args.train_val_split > 0.0:
          UpperCAmelCase_      :							Optional[int]       =      ds['''train'''].train_test_split(data_args.train_val_split				)
          UpperCAmelCase_      :							str       =      split['''train''']
          UpperCAmelCase_      :							Tuple       =      split['''test''']

     # Create config
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     UpperCAmelCase_      :							Any       =      {
         '''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_or_path:
          UpperCAmelCase_      :							Union[str, Any]       =      AutoConfig.from_pretrained(model_args.config_name_or_path			,						**_lowercase				)
     elif model_args.model_name_or_path:
          UpperCAmelCase_      :							Union[str, Any]       =      AutoConfig.from_pretrained(model_args.model_name_or_path			,						**_lowercase				)
     else:
          UpperCAmelCase_      :							List[str]       =      CONFIG_MAPPING[model_args.model_type]()
          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}'''				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
     if hasattr(_lowercase			,						'''decoder_type'''				):
          UpperCAmelCase_      :							Tuple       =      '''simmim'''

     # adapt config
     UpperCAmelCase_      :							str       =      model_args.image_size if model_args.image_size is not None else config.image_size
     UpperCAmelCase_      :							List[str]       =      model_args.patch_size if model_args.patch_size is not None else config.patch_size
     UpperCAmelCase_      :							List[Any]       =      (
         model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
     )

     config.update(
         {
             '''image_size''': model_args.image_size,
             '''patch_size''': model_args.patch_size,
             '''encoder_stride''': model_args.encoder_stride,
         }				)

     # create image processor
     if model_args.image_processor_name:
          UpperCAmelCase_      :							Dict       =      AutoImageProcessor.from_pretrained(model_args.image_processor_name			,						**_lowercase				)
     elif model_args.model_name_or_path:
          UpperCAmelCase_      :							Any       =      AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,						**_lowercase				)
     else:
          UpperCAmelCase_      :							Tuple       =      {
              conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
          }
          UpperCAmelCase_      :							List[str]       =      IMAGE_PROCESSOR_TYPES[model_args.model_type]()

     # create model
     if model_args.model_name_or_path:
          UpperCAmelCase_      :							Tuple       =      AutoModelForMaskedImageModeling.from_pretrained(
              model_args.model_name_or_path			,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)			,						config=_lowercase			,						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'''				)
          UpperCAmelCase_      :							Union[str, Any]       =      AutoModelForMaskedImageModeling.from_config(_lowercase				)

     if training_args.do_train:
          UpperCAmelCase_      :							str       =      ds['''train'''].column_names
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          UpperCAmelCase_      :							Any       =      data_args.image_column_name
     elif "image" in column_names:
          UpperCAmelCase_      :							Tuple       =      '''image'''
     elif "img" in column_names:
          UpperCAmelCase_      :							Optional[Any]       =      '''img'''
     else:
          UpperCAmelCase_      :							List[Any]       =      column_names[0]

     # transformations as done in original SimMIM paper
     # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
     UpperCAmelCase_      :							Union[str, Any]       =      Compose(
         [
             Lambda(lambda _lowercase				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
             RandomResizedCrop(model_args.image_size			,						scale=(0.67, 1.0)			,						ratio=(3.0 / 4.0, 4.0 / 3.0)				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean			,						std=image_processor.image_std				),
         ]				)

     # create mask generator
     UpperCAmelCase_      :							Union[str, Any]       =      MaskGenerator(
         input_size=model_args.image_size			,						mask_patch_size=data_args.mask_patch_size			,						model_patch_size=model_args.patch_size			,						mask_ratio=data_args.mask_ratio			,						)

     def preprocess_images(_lowercase				):
          UpperCAmelCase_      :							Tuple       =      [transforms(_lowercase				) for image in examples[image_column_name]]
          UpperCAmelCase_      :							Any       =      [mask_generator() for i in range(len(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:
               UpperCAmelCase_      :							Union[str, Any]       =      ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
          # Set the training transforms
          ds["train"].set_transform(_lowercase				)

     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:
               UpperCAmelCase_      :							str       =      (
                   ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_lowercase				)

     # Initialize our trainer
     UpperCAmelCase_      :							Dict       =      Trainer(
         model=_lowercase			,						args=_lowercase			,						train_dataset=ds['''train'''] if training_args.do_train else None			,						eval_dataset=ds['''validation'''] if training_args.do_eval else None			,						tokenizer=_lowercase			,						data_collator=_lowercase			,						)

     # Training
     if training_args.do_train:
          UpperCAmelCase_      :							str       =      None
          if training_args.resume_from_checkpoint is not None:
               UpperCAmelCase_      :							str       =      training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               UpperCAmelCase_      :							Tuple       =      last_checkpoint
          UpperCAmelCase_      :							Union[str, Any]       =      trainer.train(resume_from_checkpoint=_lowercase				)
          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:
          UpperCAmelCase_      :							Optional[Any]       =      trainer.evaluate()
          trainer.log_metrics('''eval'''			,						_lowercase				)
          trainer.save_metrics('''eval'''			,						_lowercase				)

     # Write model card and (optionally) push to hub
     UpperCAmelCase_      :							List[str]       =      {
         '''finetuned_from''': model_args.model_name_or_path,
         '''tasks''': '''masked-image-modeling''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''masked-image-modeling'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_lowercase				)
     else:
          trainer.create_model_card(**_lowercase				)


if __name__ == "__main__":
     main()





def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))