'''simple docstring'''

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: 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""")

__a: Dict          =   list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a: Union[str, Any]          =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    field(
           default="cifar10"    ,						metadata={"help": "Name of a dataset from the datasets package"}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}    ,						)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "A folder containing the training data."}					)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "A folder containing the validation data."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=0.15    ,						metadata={"help": "Percent to split off of train for validation."}					)
       SCREAMING_SNAKE_CASE							=    field(default=3_2    ,						metadata={"help": "The size of the square patches to use for masking."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=0.6    ,						metadata={"help": "Percentage of patches to mask."}    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           }    ,						)

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    List[str]  =					{}
        if self.train_dir is not None:
         lowercase__				:    str  =					self.train_dir
        if self.validation_dir is not None:
         lowercase__				:    Union[str, Any]  =					self.validation_dir
        lowercase__				:    Any  =					data_files if data_files else None



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    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."
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a__					)}    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
       SCREAMING_SNAKE_CASE							=    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"
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default="main"    ,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,						)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Name or path of preprocessor config."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": (
                   "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": (
                   "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
               )
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "Stride to use for the encoder."}    ,						)



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase=192	,   __lowerCAmelCase=32	,   __lowerCAmelCase=4	,   __lowerCAmelCase=0.6 )  ->  Optional[int]:
        lowercase__				:    List[str]  =					input_size
        lowercase__				:    str  =					mask_patch_size
        lowercase__				:    List[Any]  =					model_patch_size
        lowercase__				:    int  =					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''' )

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

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


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

        lowercase__				:    Union[str, Any]  =					mask.reshape((self.rand_size, self.rand_size) )
        lowercase__				:    List[str]  =					mask.repeat(self.scale	,   axis=0 ).repeat(self.scale	,   axis=1 )

        return torch.tensor(mask.flatten() )


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Tuple  =					torch.stack([example['''pixel_values'''] for example in examples]     )
 lowercase__				:    Union[str, Any]  =					torch.stack([example['''mask'''] for example in examples]     )
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 			__UpperCamelCase       (   ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowercase__				:    Optional[int]  =					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.
  lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
 else:
  lowercase__ ,							lowercase__ ,							lowercase__				:    int  =					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'''		,     UpperCAmelCase		,     UpperCAmelCase     )

 # 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()

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

 # Create config
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowercase__				:    List[str]  =					{
     '''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:
  lowercase__				:    Union[str, Any]  =					AutoConfig.from_pretrained(model_args.config_name_or_path		,     **UpperCAmelCase     )
 elif model_args.model_name_or_path:
  lowercase__				:    Optional[Any]  =					AutoConfig.from_pretrained(model_args.model_name_or_path		,     **UpperCAmelCase     )
 else:
  lowercase__				:    Optional[int]  =					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(UpperCAmelCase		,     '''decoder_type'''     ):
  lowercase__				:    Tuple  =					'''simmim'''

 # adapt config
 lowercase__				:    List[Any]  =					model_args.image_size if model_args.image_size is not None else config.image_size
 lowercase__				:    Dict  =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
 lowercase__				:    Tuple  =					(
     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:
  lowercase__				:    List[Any]  =					AutoImageProcessor.from_pretrained(model_args.image_processor_name		,     **UpperCAmelCase     )
 elif model_args.model_name_or_path:
  lowercase__				:    Any  =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,     **UpperCAmelCase     )
 else:
  lowercase__				:    Tuple  =					{
      conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
  }
  lowercase__				:    Tuple  =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

 # create model
 if model_args.model_name_or_path:
  lowercase__				:    List[str]  =					AutoModelForMaskedImageModeling.from_pretrained(
      model_args.model_name_or_path		,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )		,     config=UpperCAmelCase		,     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'''     )
  lowercase__				:    Tuple  =					AutoModelForMaskedImageModeling.from_config(UpperCAmelCase     )

 if training_args.do_train:
  lowercase__				:    List[str]  =					ds['''train'''].column_names
 else:
  lowercase__				:    int  =					ds['''validation'''].column_names

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

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 lowercase__				:    Dict  =					Compose(
     [
         Lambda(lambda UpperCAmelCase     : img.convert('''RGB'''     ) if img.mode != "RGB" else img     ),
         RandomResizedCrop(model_args.image_size		,     scale=(0.6_7, 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
 lowercase__				:    str  =					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(UpperCAmelCase     ):
  lowercase__				:    Optional[int]  =					[transforms(UpperCAmelCase     ) for image in examples[image_column_name]]
  lowercase__				:    List[str]  =					[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:
   lowercase__				:    Tuple  =					ds['''train'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
  # Set the training transforms
  ds["train"].set_transform(UpperCAmelCase     )

 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:
   lowercase__				:    Dict  =					(
       ds['''validation'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
   )
  # Set the validation transforms
  ds["validation"].set_transform(UpperCAmelCase     )

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

 # Training
 if training_args.do_train:
  lowercase__				:    int  =					None
  if training_args.resume_from_checkpoint is not None:
   lowercase__				:    List[Any]  =					training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   lowercase__				:    Dict  =					last_checkpoint
  lowercase__				:    Optional[int]  =					trainer.train(resume_from_checkpoint=UpperCAmelCase     )
  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:
  lowercase__				:    int  =					trainer.evaluate()
  trainer.log_metrics('''eval'''		,     UpperCAmelCase     )
  trainer.save_metrics('''eval'''		,     UpperCAmelCase     )

 # Write model card and (optionally) push to hub
 lowercase__				:    Optional[int]  =					{
     '''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(**UpperCAmelCase     )
 else:
  trainer.create_model_card(**UpperCAmelCase     )


if __name__ == "__main__":
  main()


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square(UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowercase__				:    int  =					update_area_of_max_square(UpperCAmelCase		,     col + 1     )
  lowercase__				:    Any  =					update_area_of_max_square(row + 1		,     col + 1     )
  lowercase__				:    str  =					update_area_of_max_square(row + 1		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    List[Any]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    List[Any]  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Dict  =					[0]
 update_area_of_max_square(0		,     0     )
 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square_using_dp_array(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowercase__				:    int  =					update_area_of_max_square_using_dp_array(UpperCAmelCase		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					update_area_of_max_square_using_dp_array(row + 1		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Any  =					update_area_of_max_square_using_dp_array(row + 1		,     UpperCAmelCase		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    Optional[int]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    Any  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   lowercase__				:    int  =					sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Any  =					[0]
 lowercase__				:    List[Any]  =					[[-1] * cols for _ in range(UpperCAmelCase     )]
 update_area_of_max_square_using_dp_array(0		,     0		,     UpperCAmelCase     )

 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[[0] * (cols + 1) for _ in range(rows + 1     )]
 lowercase__				:    str  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    str  =					dp_array[row][col + 1]
   lowercase__				:    Optional[Any]  =					dp_array[row + 1][col + 1]
   lowercase__				:    str  =					dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowercase__				:    Dict  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    str  =					max(dp_array[row][col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    Any  =					0

 return largest_square_area
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					[0] * (cols + 1)
 lowercase__				:    str  =					[0] * (cols + 1)
 lowercase__				:    Tuple  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    List[Any]  =					current_row[col + 1]
   lowercase__				:    Any  =					next_row[col + 1]
   lowercase__				:    Optional[Any]  =					next_row[col]

   if mat[row][col] == 1:
    lowercase__				:    List[str]  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    List[str]  =					max(current_row[col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    int  =					0
  lowercase__				:    int  =					current_row

 return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

