"""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
lowercase__ 	=     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')

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




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





								'''simple docstring'''





								a__:					List[Any]      							=     {}
								if self.train_dir is not None:
										a__:					Any      							=     self.train_dir
								if self.validation_dir is not None:
										a__:					Union[str, Any]      							=     self.validation_dir
								a__:					List[str]      							=     data_files if data_files else None







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







class        __snake_case	:



						def __init__(						self   ,      lowercase=1_92   ,      lowercase=32   ,      lowercase=4   ,      lowercase=0.6)     ->      Optional[Any]:





								'''simple docstring'''





								a__:					List[Any]      							=     input_size
								a__:					Any      							=     mask_patch_size
								a__:					Tuple      							=     model_patch_size
								a__:					Optional[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__:					Tuple      							=     self.input_size // self.mask_patch_size
								a__:					int      							=     self.mask_patch_size // self.model_patch_size

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



						def __call__(						self)     ->      str:





								'''simple docstring'''





								a__:					Union[str, Any]      							=     np.random.permutation(self.token_count)[: self.mask_count]
								a__:					Tuple      							=     np.zeros(self.token_count   ,      dtype=SCREAMING_SNAKE_CASE_)
								a__:					int      							=     1

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

								return torch.tensor(mask.flatten())







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
		a__:					List[Any]      							=     torch.stack([example['pixel_values'] for example in examples]       )
		a__:					Optional[Any]      							=     torch.stack([example['mask'] for example in examples]       )
		return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 						__a       (     )     ->Optional[int]:
		# 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.

		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__:					List[str]      							=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
		else:
				a__:					Union[str, 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'		,					snake_case__		,					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()

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

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

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

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

		if training_args.do_train:
				a__:					Optional[int]      							=     ds['train'].column_names
		else:
				a__:					Optional[int]      							=     ds['validation'].column_names

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

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		a__:					Optional[int]      							=     Compose(
		    [
		        Lambda(lambda _SCREAMING_SNAKE_CASE       : 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__:					Optional[int]      							=     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(_SCREAMING_SNAKE_CASE       ):
				a__:					Any      							=     [transforms(snake_case__       ) for image in examples[image_column_name]]
				a__:					Tuple      							=     [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__:					Tuple      							=     ds['train'].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
				# Set the training transforms
				ds["train"].set_transform(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:
						a__:					str      							=     (
						    ds['validation'].shuffle(seed=training_args.seed       ).select(range(data_args.max_eval_samples       )       )
						)
				# Set the validation transforms
				ds["validation"].set_transform(snake_case__       )

		# Initialize our trainer
		a__:					Any      							=     Trainer(
		    model=snake_case__		,					args=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=snake_case__		,					data_collator=snake_case__		,					)

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

		# Write model card and (optionally) push to hub
		a__:					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(**snake_case__       )
		else:
				trainer.create_model_card(**snake_case__       )


if __name__ == "__main__":
	main()



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE = 1000000       )     ->int:
  a__:					Any      							=     limit + 1
  a__:					List[str]      							=     [0] * limit
  for first_term in range(1		,					_SCREAMING_SNAKE_CASE       ):
    for n in range(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Any      							=     first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:					Optional[int]      							=     sum(1 for x in frequency[1:limit] if x == 10       )

  return count


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


