


'''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     :						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')

__lowercase     :						List[Any]	=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowercase     :						Optional[int]	=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 							__UpperCamelCase    :
	A_           =   field(
	    default="cifar10"		,				metadata={"help": "Name of a dataset from the datasets package"} )
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}		,				)
	A_           =   field(default=lowerCAmelCase_		,				metadata={"help": "A folder containing the training data."} )
	A_           =   field(default=lowerCAmelCase_		,				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=lowerCAmelCase_		,				metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }		,				)
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }		,				)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  {}
						if self.train_dir is not None:
											__a      :	int    =  self.train_dir
						if self.validation_dir is not None:
											__a      :	Optional[int]    =  self.validation_dir
						__a      :	Optional[int]    =  data_files if data_files else None





@dataclass
class 							__UpperCamelCase    :
	A_           =   field(
	    default=lowerCAmelCase_		,				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=lowerCAmelCase_		,				metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase_ )}		,				)
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={"help": "Pretrained config name or path if not the same as model_name"} )
	A_           =   field(
	    default=lowerCAmelCase_		,				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=lowerCAmelCase_		,				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=lowerCAmelCase_		,				metadata={"help": "Name or path of preprocessor config."} )
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }		,				)
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={
	        "help": (
	            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
	        )
	    }		,				)
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={
	        "help": (
	            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
	        )
	    }		,				)
	A_           =   field(
	    default=lowerCAmelCase_		,				metadata={"help": "Stride to use for the encoder."}		,				)





class 							__UpperCamelCase    :




	def __init__(       self						,    __a=192						,    __a=32						,    __a=4						,    __a=0.6							):

						'''simple docstring'''





						__a      :	Optional[int]    =  input_size
						__a      :	str    =  mask_patch_size
						__a      :	Optional[int]    =  model_patch_size
						__a      :	Optional[int]    =  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      :	int    =  self.input_size // self.mask_patch_size
						__a      :	Union[str, Any]    =  self.mask_patch_size // self.model_patch_size

						__a      :	List[str]    =  self.rand_size**2
						__a      :	Any    =  int(np.ceil(self.token_count * self.mask_ratio							)							)





	def __call__(       self							):

						'''simple docstring'''





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

						__a      :	Optional[int]    =  mask.reshape((self.rand_size, self.rand_size)							)
						__a      :	Tuple    =  mask.repeat(self.scale						,    axis=0							).repeat(self.scale						,    axis=1							)

						return torch.tensor(mask.flatten()							)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple       ):
					__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   lowerCamelCase							():
					# 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      :	Optional[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      :	Tuple    =  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'						,				_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()

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

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

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

					# create model
					if model_args.model_name_or_path:
										__a      :	Tuple    =  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'       )
										__a      :	Union[str, Any]    =  AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE       )

					if training_args.do_train:
										__a      :	List[str]    =  ds['train'].column_names
					else:
										__a      :	int    =  ds['validation'].column_names

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

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					__a      :	List[str]    =  Compose(
					    [
					        Lambda(lambda _SCREAMING_SNAKE_CASE       : img.convert('RGB'       ) if img.mode != "RGB" else img       ),
					        RandomResizedCrop(model_args.image_size						,				scale=(0.6_7, 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				:       Any       ):
										__a      :	Optional[int]    =  [transforms(_SCREAMING_SNAKE_CASE       ) for image in examples[image_column_name]]
										__a      :	Optional[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:
															__a      :	List[Any]    =  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:
															__a      :	Optional[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
					__a      :	Any    =  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:
										__a      :	Optional[int]    =  None
										if training_args.resume_from_checkpoint is not None:
															__a      :	Optional[Any]    =  training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__a      :	Dict    =  last_checkpoint
										__a      :	Optional[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:
										__a      :	str    =  trainer.evaluate()
										trainer.log_metrics('eval'						,				_SCREAMING_SNAKE_CASE       )
										trainer.save_metrics('eval'						,				_SCREAMING_SNAKE_CASE       )

					# Write model card and (optionally) push to hub
					__a      :	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'''



import os






def   lowerCamelCase							():
					with open(os.path.dirname(_SCREAMING_SNAKE_CASE       ) + '/p022_names.txt'       ) as file:
										__a      :	List[Any]    =  str(file.readlines()[0]       )
										__a      :	str    =  names.replace('"'						,				''       ).split(','       )

					names.sort()

					__a      :	Union[str, Any]    =  0
					__a      :	Tuple    =  0

					for i, name in enumerate(_SCREAMING_SNAKE_CASE       ):
										for letter in name:
															name_score += ord(_SCREAMING_SNAKE_CASE       ) - 64

										total_score += (i + 1) * name_score
										__a      :	Any    =  0
					return total_score


if __name__ == "__main__":
	print(solution())



