


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






     def  a_					(			self		)       ->    List[str]:
          _a						=		{}
          if self.train_dir is not None:
               _a						=		self.train_dir
          if self.validation_dir is not None:
               _a						=		self.validation_dir
          _a						=		data_files if data_files else None







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







class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase=192     ,							__UpperCamelCase=32     ,							__UpperCamelCase=4     ,							__UpperCamelCase=0.6		)       ->    Dict:
          _a						=		input_size
          _a						=		mask_patch_size
          _a						=		model_patch_size
          _a						=		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						=		self.input_size // self.mask_patch_size
          _a						=		self.mask_patch_size // self.model_patch_size

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






     def __call__(			self		)       ->    Optional[Any]:
          _a						=		np.random.permutation(self.token_count		)[: self.mask_count]
          _a						=		np.zeros(self.token_count     ,							dtype=__UpperCamelCase		)
          _a						=		1

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

          return torch.tensor(mask.flatten()		)







def __UpperCamelCase   (			__lowerCamelCase		: List[str]				)      ->     List[str]:







     '''simple docstring'''






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






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
   main()





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

