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__     :							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''')

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




@dataclass
class   lowercase_    :



	"""simple docstring"""





	UpperCAmelCase_    :   Optional[Any]  =							field(
	    default="""cifar10""" ,	metadata={"""help""": """Name of a dataset from the datasets package"""}		)
	UpperCAmelCase_    :   Dict  =							field(
	    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
	UpperCAmelCase_    :   Any  =							field(
	    default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""} ,	)
	UpperCAmelCase_    :   List[Any]  =							field(default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """A folder containing the training data."""}		)
	UpperCAmelCase_    :   List[str]  =							field(default=SCREAMING_SNAKE_CASE__ ,	metadata={"""help""": """A folder containing the validation data."""}		)
	UpperCAmelCase_    :   Optional[int]  =							field(
	    default=0.15 ,	metadata={"""help""": """Percent to split off of train for validation."""}		)
	UpperCAmelCase_    :   Union[str, Any]  =							field(default=32 ,	metadata={"""help""": """The size of the square patches to use for masking."""}		)
	UpperCAmelCase_    :   Tuple  =							field(
	    default=0.6 ,	metadata={"""help""": """Percentage of patches to mask."""} ,	)
	UpperCAmelCase_    :   Optional[Any]  =							field(
	    default=SCREAMING_SNAKE_CASE__ ,	metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of training examples to this """
	            """value if set."""
	        )
	    } ,	)
	UpperCAmelCase_    :   int  =							field(
	    default=SCREAMING_SNAKE_CASE__ ,	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[str]:
								lowerCAmelCase      						=						{}
								if self.train_dir is not None:
															lowerCAmelCase      						=						self.train_dir
								if self.validation_dir is not None:
															lowerCAmelCase      						=						self.validation_dir
								lowerCAmelCase      						=						data_files if data_files else None




@dataclass
class   lowercase_    :



	"""simple docstring"""





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




class   lowercase_    :



	"""simple docstring"""





	def __init__(				self						,     __SCREAMING_SNAKE_CASE=192						,     __SCREAMING_SNAKE_CASE=32						,     __SCREAMING_SNAKE_CASE=4						,     __SCREAMING_SNAKE_CASE=0.6							)		->List[str]:
								lowerCAmelCase      						=						input_size
								lowerCAmelCase      						=						mask_patch_size
								lowerCAmelCase      						=						model_patch_size
								lowerCAmelCase      						=						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'''							)

								lowerCAmelCase      						=						self.input_size // self.mask_patch_size
								lowerCAmelCase      						=						self.mask_patch_size // self.model_patch_size

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




	def __call__(				self							)		->Union[str, Any]:
								lowerCAmelCase      						=						np.random.permutation(self.token_count							)[: self.mask_count]
								lowerCAmelCase      						=						np.zeros(self.token_count						,     dtype=snake_case__							)
								lowerCAmelCase      						=						1

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

								return torch.tensor(mask.flatten()							)

def   SCREAMING_SNAKE_CASE_	(		snake_case__				)	->      str:
							lowerCAmelCase      						=						torch.stack([example['''pixel_values'''] for example in examples]				)
							lowerCAmelCase      						=						torch.stack([example['''mask'''] for example in examples]				)
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def   SCREAMING_SNAKE_CASE_	(		)	->      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.

							lowerCAmelCase      						=						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.
														lowerCAmelCase						, lowerCAmelCase						, lowerCAmelCase      						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														lowerCAmelCase						, lowerCAmelCase						, lowerCAmelCase      						=						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()

							lowerCAmelCase      						=						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.
							lowerCAmelCase      						=						None
							if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCAmelCase      						=						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.
							lowerCAmelCase      						=						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.
							lowerCAmelCase      						=						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:
														lowerCAmelCase      						=						ds['''train'''].train_test_split(data_args.train_val_split				)
														lowerCAmelCase      						=						split['''train''']
														lowerCAmelCase      						=						split['''test''']

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

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

							# create model
							if model_args.model_name_or_path:
														lowerCAmelCase      						=						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'''				)
														lowerCAmelCase      						=						AutoModelForMaskedImageModeling.from_config(snake_case__				)

							if training_args.do_train:
														lowerCAmelCase      						=						ds['''train'''].column_names
							else:
														lowerCAmelCase      						=						ds['''validation'''].column_names

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

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							lowerCAmelCase      						=						Compose(
							    [
							        Lambda(lambda 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
							lowerCAmelCase      						=						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(snake_case__				):
														lowerCAmelCase      						=						[transforms(snake_case__				) for image in examples[image_column_name]]
														lowerCAmelCase      						=						[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:
																					lowerCAmelCase      						=						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:
																					lowerCAmelCase      						=						(
																					    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
							lowerCAmelCase      						=						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:
														lowerCAmelCase      						=						None
														if training_args.resume_from_checkpoint is not None:
																					lowerCAmelCase      						=						training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowerCAmelCase      						=						last_checkpoint
														lowerCAmelCase      						=						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:
														lowerCAmelCase      						=						trainer.evaluate()
														trainer.log_metrics('''eval'''  ,    snake_case__				)
														trainer.save_metrics('''eval'''  ,    snake_case__				)

							# Write model card and (optionally) push to hub
							lowerCAmelCase      						=						{
							    '''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()









import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# Validation
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ) or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for day in days ):
				raise ValueError('The parameter days should be a list of integers' )

		if len(lowerCAmelCase ) != 3 or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for cost in costs ):
				raise ValueError('The parameter costs should be a list of three integers' )

		if len(lowerCAmelCase ) == 0:
				return 0

		if min(lowerCAmelCase ) <= 0:
				raise ValueError('All days elements should be greater than 0' )

		if max(lowerCAmelCase ) >= 366:
				raise ValueError('All days elements should be less than 366' )

		SCREAMING_SNAKE_CASE_					      =							set(lowerCAmelCase )

		@functools.cache
		def dynamic_programming(lowerCAmelCase ) -> int:
				if index > 365:
						return 0

				if index not in days_set:
						return dynamic_programming(index + 1 )

				return min(
				    costs[0] + dynamic_programming(index + 1 )    ,						costs[1] + dynamic_programming(index + 7 )    ,						costs[2] + dynamic_programming(index + 30 )    ,						)

		return dynamic_programming(1 )


if __name__ == "__main__":
						import doctest

						doctest.testmod()