'''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
UpperCamelCase__     :	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[str]				=			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCamelCase__     :	Optional[int]				=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 				_lowercase	:
      '''simple docstring'''


      UpperCAmelCase_      :      Optional[str]      							=   field(
          default='''cifar10'''		,metadata={'''help''': '''Name of a dataset from the datasets package'''}   )
      UpperCAmelCase_      :      Optional[str]      							=   field(
          default=lowerCAmelCase		,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
      UpperCAmelCase_      :      Optional[str]      							=   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_      :      Optional[str]      							=   field(default=lowerCAmelCase		,metadata={'''help''': '''A folder containing the training data.'''}   )
      UpperCAmelCase_      :      Optional[str]      							=   field(default=lowerCAmelCase		,metadata={'''help''': '''A folder containing the validation data.'''}   )
      UpperCAmelCase_      :      Optional[float]      							=   field(
          default=0.1_5		,metadata={'''help''': '''Percent to split off of train for validation.'''}   )
      UpperCAmelCase_      :      int      							=   field(default=32		,metadata={'''help''': '''The size of the square patches to use for masking.'''}   )
      UpperCAmelCase_      :      float      							=   field(
          default=0.6		,metadata={'''help''': '''Percentage of patches to mask.'''}		,)
      UpperCAmelCase_      :      Optional[int]      							=   field(
          default=lowerCAmelCase		,metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }		,)
      UpperCAmelCase_      :      Optional[int]      							=   field(
          default=lowerCAmelCase		,metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }		,)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  {}
         if self.train_dir is not None:
            UpperCAmelCase__							:				str   				=  self.train_dir
         if self.validation_dir is not None:
            UpperCAmelCase__							:				List[Any]   				=  self.validation_dir
         UpperCAmelCase__							:				List[str]   				=  data_files if data_files else None




@dataclass
class 				_lowercase	:
      '''simple docstring'''


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




class 				_lowercase	:
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_=192					,lowerCamelCase_=32					,lowerCamelCase_=4					,lowerCamelCase_=0.6							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  input_size
         UpperCAmelCase__							:				Tuple   				=  mask_patch_size
         UpperCAmelCase__							:				List[Any]   				=  model_patch_size
         UpperCAmelCase__							:				List[str]   				=  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__							:				Union[str, Any]   				=  self.input_size // self.mask_patch_size
         UpperCAmelCase__							:				Tuple   				=  self.mask_patch_size // self.model_patch_size

         UpperCAmelCase__							:				Any   				=  self.rand_size**2
         UpperCAmelCase__							:				Optional[Any]   				=  int(np.ceil(self.token_count * self.mask_ratio							)							)




      def __call__(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.random.permutation(self.token_count							)[: self.mask_count]
         UpperCAmelCase__							:				Any   				=  np.zeros(self.token_count					,dtype=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  1

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

         return torch.tensor(mask.flatten()							)







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  torch.stack([example['''pixel_values'''] for example in examples]    )
   UpperCAmelCase__							:				Optional[int]   				=  torch.stack([example['''mask'''] for example in examples]    )
   return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def __UpperCamelCase(   ):
   '''simple docstring'''
   # 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__							:				Optional[int]   				=  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__							:				Any   				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
   else:
      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  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__							:				List[Any]   				=  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__							:				int   				=  None
   if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
      UpperCAmelCase__							:				Optional[int]   				=  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__							:				Optional[Any]   				=  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__							:				Tuple   				=  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__							:				Dict   				=  ds['''train'''].train_test_split(data_args.train_val_split    )
      UpperCAmelCase__							:				Union[str, Any]   				=  split['''train''']
      UpperCAmelCase__							:				List[Any]   				=  split['''test''']

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

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

   # create model
   if model_args.model_name_or_path:
      UpperCAmelCase__							:				Dict   				=  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__							:				int   				=  AutoModelForMaskedImageModeling.from_config(_A    )

   if training_args.do_train:
      UpperCAmelCase__							:				int   				=  ds['''train'''].column_names
   else:
      UpperCAmelCase__							:				Dict   				=  ds['''validation'''].column_names

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

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

   # Write model card and (optionally) push to hub
   UpperCAmelCase__							:				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(**_A    )
   else:
      trainer.create_model_card(**_A    )


if __name__ == "__main__":
  main()





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




