





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
UpperCamelCase__   : List[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""")

UpperCamelCase__   : List[Any]					  = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCamelCase__   : List[str]					  = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     Optional[str]			    =       field(
					    default="cifar10"						,       metadata={"help": "Name of a dataset from the datasets package"}  )
					lowerCamelCase     :     Optional[str]			    =       field(
					    default=A_						,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}  )
					lowerCamelCase     :     Optional[str]			    =       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     :     Optional[str]			    =       field(default=A_						,       metadata={"help": "A folder containing the training data."}  )
					lowerCamelCase     :     Optional[str]			    =       field(default=A_						,       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=A_						,       metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }						,       )
					lowerCamelCase     :     Optional[int]			    =       field(
					    default=A_						,       metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }						,       )





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     {}
						if self.train_dir is not None:
							UpperCAmelCase_								=     self.train_dir
						if self.validation_dir is not None:
							UpperCAmelCase_								=     self.validation_dir
						UpperCAmelCase_								=     data_files if data_files else None







@dataclass
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     str			    =       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     :     Optional[str]			    =       field(
					    default=A_						,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A_  )}						,       )
					lowerCamelCase     :     Optional[str]			    =       field(
					    default=A_						,       metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
					lowerCamelCase     :     Optional[str]			    =       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     :     Optional[str]			    =       field(
					    default=A_						,       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=A_						,       metadata={"help": "Name or path of preprocessor config."}  )
					lowerCamelCase     :     bool			    =       field(
					    default=A_						,       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=A_						,       metadata={
					        "help": (
					            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
					        )
					    }						,       )
					lowerCamelCase     :     Optional[int]			    =       field(
					    default=A_						,       metadata={
					        "help": (
					            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
					        )
					    }						,       )
					lowerCamelCase     :     Optional[int]			    =       field(
					    default=A_						,       metadata={"help": "Stride to use for the encoder."}						,       )







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Optional[int] ,					__lowercase    :  Tuple=1_92 ,					__lowercase    :  int=32 ,					__lowercase    :  Optional[int]=4 ,					__lowercase    :  Any=0.6		):






						'''simple docstring'''







						UpperCAmelCase_								=     input_size
						UpperCAmelCase_								=     mask_patch_size
						UpperCAmelCase_								=     model_patch_size
						UpperCAmelCase_								=     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_								=     self.input_size // self.mask_patch_size
						UpperCAmelCase_								=     self.mask_patch_size // self.model_patch_size

						UpperCAmelCase_								=     self.rand_size**2
						UpperCAmelCase_								=     int(np.ceil(self.token_count * self.mask_ratio		)		)





					def __call__(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     np.random.permutation(self.token_count		)[: self.mask_count]
						UpperCAmelCase_								=     np.zeros(self.token_count ,					dtype=__lowercase		)
						UpperCAmelCase_								=     1

						UpperCAmelCase_								=     mask.reshape((self.rand_size, self.rand_size)		)
						UpperCAmelCase_								=     mask.repeat(self.scale ,					axis=0		).repeat(self.scale ,					axis=1		)

						return torch.tensor(mask.flatten()		)




def    A_(			A   ):
	UpperCAmelCase_								=     torch.stack([example["""pixel_values"""] for example in examples]   )
	UpperCAmelCase_								=     torch.stack([example["""mask"""] for example in examples]   )
	return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def    A_(			):
	# 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.

	UpperCAmelCase_								=     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_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
	else:
		UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     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"""				,      A				,      A   )

	# 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_								=     training_args.get_process_log_level()
	logger.setLevel(A   )
	transformers.utils.logging.set_verbosity(A   )
	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_								=     None
	if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCAmelCase_								=     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_								=     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_								=     None if """validation""" in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split				,      A   ) and data_args.train_val_split > 0.0:
		UpperCAmelCase_								=     ds["""train"""].train_test_split(data_args.train_val_split   )
		UpperCAmelCase_								=     split["""train"""]
		UpperCAmelCase_								=     split["""test"""]

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

	# adapt config
	UpperCAmelCase_								=     model_args.image_size if model_args.image_size is not None else config.image_size
	UpperCAmelCase_								=     model_args.patch_size if model_args.patch_size is not None else config.patch_size
	UpperCAmelCase_								=     (
	    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_								=     AutoImageProcessor.from_pretrained(model_args.image_processor_name				,      **A   )
	elif model_args.model_name_or_path:
		UpperCAmelCase_								=     AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,      **A   )
	else:
		UpperCAmelCase_								=     {
		    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
		}
		UpperCAmelCase_								=     IMAGE_PROCESSOR_TYPES[model_args.model_type]()

	# create model
	if model_args.model_name_or_path:
		UpperCAmelCase_								=     AutoModelForMaskedImageModeling.from_pretrained(
		    model_args.model_name_or_path				,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )				,      config=A				,      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_								=     AutoModelForMaskedImageModeling.from_config(A   )

	if training_args.do_train:
		UpperCAmelCase_								=     ds["""train"""].column_names
	else:
		UpperCAmelCase_								=     ds["""validation"""].column_names

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

	# transformations as done in original SimMIM paper
	# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
	UpperCAmelCase_								=     Compose(
	    [
	        Lambda(lambda A   : 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_								=     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(A   ):
		UpperCAmelCase_								=     [transforms(A   ) for image in examples[image_column_name]]
		UpperCAmelCase_								=     [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_								=     ds["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
		# Set the training transforms
		ds["train"].set_transform(A   )

	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_								=     (
			    ds["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
			)
		# Set the validation transforms
		ds["validation"].set_transform(A   )

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

	# Training
	if training_args.do_train:
		UpperCAmelCase_								=     None
		if training_args.resume_from_checkpoint is not None:
			UpperCAmelCase_								=     training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCAmelCase_								=     last_checkpoint
		UpperCAmelCase_								=     trainer.train(resume_from_checkpoint=A   )
		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_								=     trainer.evaluate()
		trainer.log_metrics("""eval"""				,      A   )
		trainer.save_metrics("""eval"""				,      A   )

	# Write model card and (optionally) push to hub
	UpperCAmelCase_								=     {
	    """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(**A   )
	else:
		trainer.create_model_card(**A   )


if __name__ == "__main__":
				main()









from math import factorial

UpperCamelCase__   : dict[str, int]					  = {str(digit): factorial(digit) for digit in range(10)}




def    A_(			A   ):
	if not isinstance(A				,      A   ):
		raise TypeError("""Parameter number must be int"""   )

	if number < 0:
		raise ValueError("""Parameter number must be greater than or equal to 0"""   )

	# Converts number in string to iterate on its digits and adds its factorial.
	return sum(DIGIT_FACTORIAL[digit] for digit in str(A   )   )




def    A_(			A = 60				,      A = 1000000   ):

	if not isinstance(A				,      A   ) or not isinstance(A				,      A   ):
		raise TypeError("""Parameters chain_length and number_limit must be int"""   )

	if chain_length <= 0 or number_limit <= 0:
		raise ValueError(
		    """Parameters chain_length and number_limit must be greater than 0"""   )

	# the counter for the chains with the exact desired length
	UpperCAmelCase_								=     0
	# the cached sizes of the previous chains
	UpperCAmelCase_								=     {}

	for start_chain_element in range(1				,      A   ):
		# The temporary set will contain the elements of the chain
		UpperCAmelCase_								=     set()
		UpperCAmelCase_								=     0

		# Stop computing the chain when you find a cached size, a repeating item or the
		# length is greater then the desired one.
		UpperCAmelCase_								=     start_chain_element
		while (
		    chain_element not in chain_sets_lengths
		    and chain_element not in chain_set
		    and chain_set_length <= chain_length
		):
			chain_set.add(A   )
			chain_set_length += 1
			UpperCAmelCase_								=     digit_factorial_sum(A   )

		if chain_element in chain_sets_lengths:
			chain_set_length += chain_sets_lengths[chain_element]

		UpperCAmelCase_								=     chain_set_length

		# If chain contains the exact amount of elements increase the counter
		if chain_set_length == chain_length:
			chains_counter += 1

	return chains_counter


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"{solution()}")


