



"""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:     List[str]										=	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:     Union[str, Any]										=	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A:     int										=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     Optional[str]		=		field(
       default='cifar10'   ,    metadata={'help': 'Name of a dataset from the datasets package'}				)
   __lowerCAmelCase						:     Optional[str]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
   __lowerCAmelCase						:     Optional[str]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}   ,    )
   __lowerCAmelCase						:     Optional[str]		=		field(default=_UpperCAmelCase   ,    metadata={'help': 'A folder containing the training data.'}				)
   __lowerCAmelCase						:     Optional[str]		=		field(default=_UpperCAmelCase   ,    metadata={'help': 'A folder containing the validation data.'}				)
   __lowerCAmelCase						:     Optional[float]		=		field(
       default=0.15   ,    metadata={'help': 'Percent to split off of train for validation.'}				)
   __lowerCAmelCase						:     int		=		field(default=32   ,    metadata={'help': 'The size of the square patches to use for masking.'}				)
   __lowerCAmelCase						:     float		=		field(
       default=0.6   ,    metadata={'help': 'Percentage of patches to mask.'}   ,    )
   __lowerCAmelCase						:     Optional[int]		=		field(
       default=_UpperCAmelCase   ,    metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }   ,    )
   __lowerCAmelCase						:     Optional[int]		=		field(
       default=_UpperCAmelCase   ,    metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }   ,    )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			{}
    if self.train_dir is not None:
     UpperCAmelCase			:      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    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     str		=		field(
       default=_UpperCAmelCase   ,    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						:     Optional[str]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCAmelCase				)}   ,    )
   __lowerCAmelCase						:     Optional[str]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
   __lowerCAmelCase						:     Optional[str]		=		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						:     Optional[str]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default='main'   ,    metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,    )
   __lowerCAmelCase						:     str		=		field(default=_UpperCAmelCase   ,    metadata={'help': 'Name or path of preprocessor config.'}				)
   __lowerCAmelCase						:     bool		=		field(
       default=_UpperCAmelCase   ,    metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       }   ,    )
   __lowerCAmelCase						:     Optional[int]		=		field(
       default=_UpperCAmelCase   ,    metadata={
           'help': (
               'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
           )
       }   ,    )
   __lowerCAmelCase						:     Optional[int]		=		field(
       default=_UpperCAmelCase   ,    metadata={
           'help': (
               'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
           )
       }   ,    )
   __lowerCAmelCase						:     Optional[int]		=		field(
       default=_UpperCAmelCase   ,    metadata={'help': 'Stride to use for the encoder.'}   ,    )



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=192   ,  _SCREAMING_SNAKE_CASE=32   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=0.6		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			input_size
    UpperCAmelCase			:      List[Any]			=			mask_patch_size
    UpperCAmelCase			:      Tuple			=			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[Any]			=			self.input_size // self.mask_patch_size
    UpperCAmelCase			:      List[str]			=			self.mask_patch_size // self.model_patch_size

    UpperCAmelCase			:      Optional[int]			=			self.rand_size**2
    UpperCAmelCase			:      Union[str, Any]			=			int(np.ceil(self.token_count * self.mask_ratio		)		)




   def __call__(     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			np.random.permutation(self.token_count		)[: self.mask_count]
    UpperCAmelCase			:      Optional[Any]			=			np.zeros(self.token_count   ,  dtype=lowercase__		)
    UpperCAmelCase			:      Optional[Any]			=			1

    UpperCAmelCase			:      Any			=			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    _snake_case		(		UpperCamelCase		:	Dict		):
 UpperCAmelCase			:      Any			=			torch.stack([example["""pixel_values"""] for example in examples]		)
 UpperCAmelCase			:      Optional[int]			=			torch.stack([example["""mask"""] for example in examples]		)
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def    _snake_case		(		):
 UpperCAmelCase			:      Any			=			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			:      Tuple			=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  UpperCAmelCase			:      List[Any]			=			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"""    ,	SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__		)

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

 # Create config
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 UpperCAmelCase			:      Dict			=			{
     """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			:      str			=			AutoConfig.from_pretrained(model_args.config_name_or_path    ,	**SCREAMING_SNAKE_CASE__		)
 elif model_args.model_name_or_path:
  UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(model_args.model_name_or_path    ,	**SCREAMING_SNAKE_CASE__		)
 else:
  UpperCAmelCase			:      Dict			=			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(SCREAMING_SNAKE_CASE__    ,	"""decoder_type"""		):
  UpperCAmelCase			:      str			=			"""simmim"""

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

 # create model
 if model_args.model_name_or_path:
  UpperCAmelCase			:      List[Any]			=			AutoModelForMaskedImageModeling.from_pretrained(
      model_args.model_name_or_path    ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)    ,	config=SCREAMING_SNAKE_CASE__    ,	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			:      Optional[int]			=			AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__		)

 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			:      str			=			"""image"""
 elif "img" in column_names:
  UpperCAmelCase			:      Dict			=			"""img"""
 else:
  UpperCAmelCase			:      Optional[int]			=			column_names[0]

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 UpperCAmelCase			:      List[str]			=			Compose(
     [
         Lambda(lambda UpperCamelCase		: 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			:      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(UpperCamelCase		:	List[Any]		):
  UpperCAmelCase			:      List[str]			=			[transforms(SCREAMING_SNAKE_CASE__		) 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			:      Optional[int]			=			ds["""train"""].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
  # Set the training transforms
  ds["train"].set_transform(SCREAMING_SNAKE_CASE__		)

 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			:      Union[str, Any]			=			(
       ds["""validation"""].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
   )
  # Set the validation transforms
  ds["validation"].set_transform(SCREAMING_SNAKE_CASE__		)

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

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

 # Write model card and (optionally) push to hub
 UpperCAmelCase			:      Optional[Any]			=			{
     """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(**SCREAMING_SNAKE_CASE__		)
 else:
  trainer.create_model_card(**SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
      main()





"""simple docstring"""
def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 return 1 if input_a == input_a else 0

def    _snake_case		(		):
 assert xnor_gate(0    ,	0		) == 1
 assert xnor_gate(0    ,	1		) == 0
 assert xnor_gate(1    ,	0		) == 0
 assert xnor_gate(1    ,	1		) == 1


if __name__ == "__main__":
      print(xnor_gate(0, 0))
      print(xnor_gate(0, 1))
      print(xnor_gate(1, 0))
      print(xnor_gate(1, 1))
