



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	       =	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(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__UpperCAmelCase	       =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class     lowerCAmelCase_		:
       UpperCAmelCase__    : Optional[str]       					=  field(
           default="cifar10"	,   metadata={"help": "Name of a dataset from the datasets package"}							)
       UpperCAmelCase__    : Optional[str]       					=  field(
           default=a__	,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
       UpperCAmelCase__    : Optional[str]       					=  field(
           default=a__	,   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=a__	,   metadata={"help": "A folder containing the training data."}							)
       UpperCAmelCase__    : Optional[str]       					=  field(default=a__	,   metadata={"help": "A folder containing the validation data."}							)
       UpperCAmelCase__    : Optional[float]       					=  field(
           default=0.15	,   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=a__	,   metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           }	,   )
       UpperCAmelCase__    : Optional[int]       					=  field(
           default=a__	,   metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           }	,   )
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  List[Any]						     =     {}
          if self.train_dir is not None:
             UpperCamelCase   :  Any						     =     self.train_dir
          if self.validation_dir is not None:
             UpperCamelCase   :  Union[str, Any]						     =     self.validation_dir
          UpperCamelCase   :  List[str]						     =     data_files if data_files else None






@dataclass
class     lowerCAmelCase_		:
       UpperCAmelCase__    : str       					=  field(
           default=a__	,   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=a__	,   metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a__							)}	,   )
       UpperCAmelCase__    : Optional[str]       					=  field(
           default=a__	,   metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
       UpperCAmelCase__    : Optional[str]       					=  field(
           default=a__	,   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=a__	,   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=a__	,   metadata={"help": "Name or path of preprocessor config."}							)
       UpperCAmelCase__    : bool       					=  field(
           default=a__	,   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=a__	,   metadata={
               "help": (
                   "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
               )
           }	,   )
       UpperCAmelCase__    : Optional[int]       					=  field(
           default=a__	,   metadata={
               "help": (
                   "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
               )
           }	,   )
       UpperCAmelCase__    : Optional[int]       					=  field(
           default=a__	,   metadata={"help": "Stride to use for the encoder."}	,   )






class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_=192,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=0.6	)					->		Optional[Any]:
          UpperCamelCase   :  List[Any]						     =     input_size
          UpperCamelCase   :  Any						     =     mask_patch_size
          UpperCamelCase   :  Tuple						     =     model_patch_size
          UpperCamelCase   :  Optional[Any]						     =     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   :  Tuple						     =     self.input_size // self.mask_patch_size
          UpperCamelCase   :  int						     =     self.mask_patch_size // self.model_patch_size

          UpperCamelCase   :  Union[str, Any]						     =     self.rand_size**2
          UpperCamelCase   :  str						     =     int(np.ceil(self.token_count * self.mask_ratio	)	)






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

          UpperCamelCase   :  Union[str, Any]						     =     mask.reshape((self.rand_size, self.rand_size)	)
          UpperCamelCase   :  str						     =     mask.repeat(self.scale,      axis=0	).repeat(self.scale,      axis=1	)

          return torch.tensor(mask.flatten()	)






def 		UpperCamelCase     (       snake_case__	:   int     )     ->					int:
   UpperCamelCase   :  List[Any]						     =     torch.stack([example['pixel_values'] for example in examples]     )
   UpperCamelCase   :  Optional[Any]						     =     torch.stack([example['mask'] for example in examples]     )
   return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 		UpperCamelCase     (       )     ->					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.

   UpperCamelCase   :  Union[str, 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.
      UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  List[str]						     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
   else:
      UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  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'	,					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()

   UpperCamelCase   :  Optional[int]						     =     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.
   UpperCamelCase   :  Dict						     =     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   :  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	,					snake_case__     ) and data_args.train_val_split > 0.0:
      UpperCamelCase   :  List[str]						     =     ds['train'].train_test_split(data_args.train_val_split     )
      UpperCamelCase   :  str						     =     split['train']
      UpperCamelCase   :  Any						     =     split['test']

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

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

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

   if training_args.do_train:
      UpperCamelCase   :  Optional[int]						     =     ds['train'].column_names
   else:
      UpperCamelCase   :  Optional[int]						     =     ds['validation'].column_names

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

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

   # Write model card and (optionally) push to hub
   UpperCamelCase   :  List[str]						     =     {
       '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 re
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					str:

   if len(re.findall('[ATCG]'	,					snake_case__     )     ) != len(snake_case__     ):
      raise ValueError('Invalid Strand'     )

   return dna.translate(dna.maketrans('ATCG'	,					'TAGC'     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



