'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
lowerCAmelCase_ : 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")





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       default='cifar10' ,				metadata={'help': 'Name of a dataset from the datasets package'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'The column name of the images in the files.'} )
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'A folder containing the training data.'} )
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'A folder containing the validation data.'} )
   lowerCamelCase_    					=field(
       default=0.15 ,				metadata={'help': 'Percent to split off of train for validation.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       } ,				)
   lowerCamelCase_    					=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				:	Optional[Any])      ->					Optional[Any]:
         lowercase_     =      {}
         if self.train_dir is not None:
               lowercase_     =      self.train_dir
         if self.validation_dir is not None:
               lowercase_     =      self.validation_dir
         lowercase_     =      data_files if data_files else None





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
   lowerCamelCase_    					=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'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
   lowerCamelCase_    					=field(
       default='main' ,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,				)
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Name or path of preprocessor config.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=0.75 ,				metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )





@dataclass
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(
       default=1e-3 ,				metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )




def __a (	__lowerCamelCase			:						Tuple				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      torch.stack([example["pixel_values"] for example in examples]				)
      return {"pixel_values": pixel_values}


def __a (	)	->							str:
      '''simple docstring'''


      lowercase_     =      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
      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.
            lowercase_ ,							lowercase_ ,							lowercase_     =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            lowercase_ ,							lowercase_ ,							lowercase_     =      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_mae" ,		__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()

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

      # Load pretrained model and image processor
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowercase_     =      {
          "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:
            lowercase_     =      ViTMAEConfig.from_pretrained(model_args.config_name ,		**__lowerCamelCase				)
      elif model_args.model_name_or_path:
            lowercase_     =      ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,		**__lowerCamelCase				)
      else:
            lowercase_     =      ViTMAEConfig()
            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}'				)

    # adapt config
      config.update(
          {
              "mask_ratio": model_args.mask_ratio,
              "norm_pix_loss": model_args.norm_pix_loss,
          }				)

      # create image processor
      if model_args.image_processor_name:
            lowercase_     =      ViTImageProcessor.from_pretrained(model_args.image_processor_name ,		**__lowerCamelCase				)
      elif model_args.model_name_or_path:
            lowercase_     =      ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,		**__lowerCamelCase				)
      else:
            lowercase_     =      ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            lowercase_     =      ViTMAEForPreTraining.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"				)
            lowercase_     =      ViTMAEForPreTraining(__lowerCamelCase				)

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

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

      # transformations as done in original MAE paper
      # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
      if "shortest_edge" in image_processor.size:
            lowercase_     =      image_processor.size["shortest_edge"]
      else:
            lowercase_     =      (image_processor.size["height"], image_processor.size["width"])
      lowercase_     =      Compose(
          [
              Lambda(lambda __lowerCamelCase				: img.convert("RGB"				) if img.mode != "RGB" else img				),
              RandomResizedCrop(__lowerCamelCase ,		scale=(0.2, 1.0) ,		interpolation=InterpolationMode.BICUBIC				),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean ,		std=image_processor.image_std				),
          ]				)

      def preprocess_images(__lowerCamelCase			:						str				):
            lowercase_     =      [transforms(__lowerCamelCase				) for image in 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:
                  lowercase_     =      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:
                  lowercase_     =      (
                      ds["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(__lowerCamelCase				)

      # Compute absolute learning rate
      lowercase_     =      (
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            lowercase_     =      training_args.base_learning_rate * total_train_batch_size / 256

      # Initialize our trainer
      lowercase_     =      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:
            lowercase_     =      None
            if training_args.resume_from_checkpoint is not None:
                  lowercase_     =      training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowercase_     =      last_checkpoint
            lowercase_     =      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:
            lowercase_     =      trainer.evaluate()
            trainer.log_metrics("eval" ,		__lowerCamelCase				)
            trainer.save_metrics("eval" ,		__lowerCamelCase				)

      # Write model card and (optionally) push to hub
      lowercase_     =      {
          "tasks": "masked-auto-encoding",
          "dataset": data_args.dataset_name,
          "tags": ["masked-auto-encoding"],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**__lowerCamelCase				)
      else:
            trainer.create_model_card(**__lowerCamelCase				)


def __a (	__lowerCamelCase			:						Optional[int]				)	->							Any:
      '''simple docstring'''


      main()


if __name__ == "__main__":
       main()







'''simple docstring'''

def __a (	__lowerCamelCase			:						int = 200				)	->							int:
      '''simple docstring'''


      lowercase_     =      [1, 2, 5, 10, 20, 50, 100, 200]
      lowercase_     =      [0] * (pence + 1)
      lowercase_     =      1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(__lowerCamelCase ,		pence + 1 ,		1				):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682






