



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
snake_case    :						Union[str, Any]  =						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')

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




@dataclass
class 							lowerCAmelCase__  :
     __A     :    Optional[str]           =						field(
         default='cifar10'					,							metadata={'help': 'Name of a dataset from the datasets package'}						)
     __A     :    Optional[str]           =						field(
         default=UpperCamelCase					,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
     __A     :    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\'.'}					,							)
     __A     :    Optional[str]           =						field(default=UpperCamelCase					,							metadata={'help': 'A folder containing the training data.'}						)
     __A     :    Optional[str]           =						field(default=UpperCamelCase					,							metadata={'help': 'A folder containing the validation data.'}						)
     __A     :    Optional[float]           =						field(
         default=0.15					,							metadata={'help': 'Percent to split off of train for validation.'}						)
     __A     :    int           =						field(default=32					,							metadata={'help': 'The size of the square patches to use for masking.'}						)
     __A     :    float           =						field(
         default=0.6					,							metadata={'help': 'Percentage of patches to mask.'}					,							)
     __A     :    Optional[int]           =						field(
         default=UpperCamelCase					,							metadata={
             'help': (
                 'For debugging purposes or quicker training, truncate the number of training examples to this '
                 'value if set.'
             )
         }					,							)
     __A     :    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  _lowercase			(       self      :     List[str]):
           A__	:							Optional[Any]						    = {}
           if self.train_dir is not None:
                 A__	:							str						    = self.train_dir
           if self.validation_dir is not None:
                 A__	:							int						    = self.validation_dir
           A__	:							int						    = data_files if data_files else None




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




class 							lowerCAmelCase__  :







     def __init__(       self      :     List[Any]			,		_A      :     Tuple=192			,		_A      :     str=32			,		_A      :     List[str]=4			,		_A      :     Optional[Any]=0.6):
           A__	:							Optional[int]						    = input_size
           A__	:							Optional[Any]						    = mask_patch_size
           A__	:							List[Any]						    = model_patch_size
           A__	:							Any						    = 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")

           A__	:							List[Any]						    = self.input_size // self.mask_patch_size
           A__	:							Dict						    = self.mask_patch_size // self.model_patch_size

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







     def __call__(       self      :     str):
           A__	:							str						    = np.random.permutation(self.token_count)[: self.mask_count]
           A__	:							Optional[int]						    = np.zeros(self.token_count			,		dtype=_A)
           A__	:							Dict						    = 1

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

           return torch.tensor(mask.flatten())




def 	snake_case__       (					__lowercase       )		->		Optional[int]:







      """simple docstring"""

      A__	:							int						    = torch.stack([example["pixel_values"] for example in examples]       )
      A__	:							int						    = torch.stack([example["mask"] for example in examples]       )
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 	snake_case__       (					)		->		Optional[Any]:







      """simple docstring"""

      A__	:							Union[str, 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.
            A__	:							Optional[Any]						    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
      else:
            A__	:							Tuple						    = 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()

      A__	:							Tuple						    = 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.
      A__	:							Tuple						    = None
      if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
            A__	:							List[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.
      A__	:							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.
      A__	:							Optional[Any]						    = 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:
            A__	:							int						    = ds["train"].train_test_split(data_args.train_val_split       )
            A__	:							Any						    = split["train"]
            A__	:							Tuple						    = split["test"]

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

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

      # create model
      if model_args.model_name_or_path:
            A__	:							int						    = 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"       )
            A__	:							Optional[int]						    = AutoModelForMaskedImageModeling.from_config(__lowercase       )

      if training_args.do_train:
            A__	:							Optional[Any]						    = ds["train"].column_names
      else:
            A__	:							Any						    = ds["validation"].column_names

      if data_args.image_column_name is not None:
            A__	:							Optional[Any]						    = data_args.image_column_name
      elif "image" in column_names:
            A__	:							Dict						    = "image"
      elif "img" in column_names:
            A__	:							Dict						    = "img"
      else:
            A__	:							Union[str, Any]						    = column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      A__	:							str						    = 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
      A__	:							List[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       ):
            A__	:							Any						    = [transforms(__lowercase       ) for image in examples[image_column_name]]
            A__	:							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:
                  A__	:							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:
                  A__	:							List[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
      A__	:							str						    = 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:
            A__	:							Dict						    = None
            if training_args.resume_from_checkpoint is not None:
                  A__	:							int						    = training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  A__	:							Dict						    = last_checkpoint
            A__	:							str						    = 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:
            A__	:							Dict						    = trainer.evaluate()
            trainer.log_metrics("eval"							,     __lowercase       )
            trainer.save_metrics("eval"							,     __lowercase       )

      # Write model card and (optionally) push to hub
      A__	:							List[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(**__lowercase       )
      else:
            trainer.create_model_card(**__lowercase       )


if __name__ == "__main__":
  main()




from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt
def 	snake_case__       (					__lowercase       )		->		bool:







						"""simple docstring"""

						A__	:							int						    = int(number**0.5       )
						return number == sq * sq
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		tuple[int, int]:







						"""simple docstring"""

						A__	:							int						    = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
						A__	:							int						    = x_den * y_den * z_den
						A__	:							int						    = gcd(__lowercase							,     __lowercase       )
						top //= hcf
						bottom //= hcf
						return top, bottom
def 	snake_case__       (					__lowercase = 3_5       )		->		int:







						"""simple docstring"""

						A__	:							set						    = set()
						A__	:							int
						A__	:							Fraction						    = Fraction(0       )
						A__	:							tuple[int, int]

						for x_num in range(1							,     order + 1       ):
												for x_den in range(x_num + 1							,     order + 1       ):
																		for y_num in range(1							,     order + 1       ):
																								for y_den in range(y_num + 1							,     order + 1       ):
																														# n=1
																														A__	:							Any						    = x_num * y_den + x_den * y_num
																														A__	:							List[Any]						    = x_den * y_den
																														A__	:							List[Any]						    = gcd(__lowercase							,     __lowercase       )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																				A__	:							List[Any]						    = add_three(
																																				    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																				unique_s.add(__lowercase       )

																														# n=2
																														A__	:							Any						    = (
																														    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																														)
																														A__	:							Optional[int]						    = x_den * x_den * y_den * y_den
																														if is_sq(__lowercase       ) and is_sq(__lowercase       ):
																																				A__	:							Union[str, Any]						    = int(sqrt(__lowercase       )       )
																																				A__	:							int						    = int(sqrt(__lowercase       )       )
																																				A__	:							Any						    = gcd(__lowercase							,     __lowercase       )
																																				z_num //= hcf
																																				z_den //= hcf
																																				if 0 < z_num < z_den <= order:
																																										A__	:							List[Any]						    = add_three(
																																										    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																										unique_s.add(__lowercase       )

                    # n=-1
																														A__	:							Tuple						    = x_num * y_num
																														A__	:							int						    = x_den * y_num + x_num * y_den
																														A__	:							List[str]						    = gcd(__lowercase							,     __lowercase       )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																				A__	:							str						    = add_three(
																																				    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																				unique_s.add(__lowercase       )

																														# n=2
																														A__	:							Any						    = x_num * x_num * y_num * y_num
																														A__	:							List[str]						    = (
																														    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																														)
																														if is_sq(__lowercase       ) and is_sq(__lowercase       ):
																																				A__	:							Optional[int]						    = int(sqrt(__lowercase       )       )
																																				A__	:							List[Any]						    = int(sqrt(__lowercase       )       )
																																				A__	:							Union[str, Any]						    = gcd(__lowercase							,     __lowercase       )
																																				z_num //= hcf
																																				z_den //= hcf
																																				if 0 < z_num < z_den <= order:
																																										A__	:							Optional[Any]						    = add_three(
																																										    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																										unique_s.add(__lowercase       )

						for num, den in unique_s:
												total += Fraction(__lowercase							,     __lowercase       )

						return total.denominator + total.numerator


if __name__ == "__main__":
		print(f"""{solution() = }""")