





'''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
_SCREAMING_SNAKE_CASE							     =			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")

_SCREAMING_SNAKE_CASE							     =			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_SCREAMING_SNAKE_CASE							     =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






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



     def      lowerCAmelCase			(   self							:			str						)->	str:
        snake_case 						=   {}
        if self.train_dir is not None:
           snake_case 						=   self.train_dir
        if self.validation_dir is not None:
           snake_case 						=   self.validation_dir
        snake_case 						=   data_files if data_files else None




@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






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




class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			Dict   ,       __snake_case							:			Dict=1_92   ,       __snake_case							:			Any=32   ,       __snake_case							:			str=4   ,       __snake_case							:			Union[str, Any]=0.6						)->	List[Any]:
        snake_case 						=   input_size
        snake_case 						=   mask_patch_size
        snake_case 						=   model_patch_size
        snake_case 						=   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"""						)

        snake_case 						=   self.input_size // self.mask_patch_size
        snake_case 						=   self.mask_patch_size // self.model_patch_size

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



     def __call__(   self							:			str						)->	str:
        snake_case 						=   np.random.permutation(self.token_count						)[: self.mask_count]
        snake_case 						=   np.zeros(self.token_count   ,       dtype=__snake_case						)
        snake_case 						=   1

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

        return torch.tensor(mask.flatten()						)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]		)		->  int:
   snake_case 						=   torch.stack([example["""pixel_values"""] for example in examples]		)
   snake_case 						=   torch.stack([example["""mask"""] for example in examples]		)
   return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 	__lowerCamelCase			(      )		->  Union[str, Any]:
   # 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.

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

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

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

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

   # create model
   if model_args.model_name_or_path:
      snake_case 						=   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"""		)
      snake_case 						=   AutoModelForMaskedImageModeling.from_config(__lowerCAmelCase		)

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

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

   # transformations as done in original SimMIM paper
   # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
   snake_case 						=   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
   snake_case 						=   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       :							Any		):
      snake_case 						=   [transforms(__lowerCAmelCase		) for image in examples[image_column_name]]
      snake_case 						=   [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:
         snake_case 						=   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:
         snake_case 						=   (
             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
   snake_case 						=   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:
      snake_case 						=   None
      if training_args.resume_from_checkpoint is not None:
         snake_case 						=   training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         snake_case 						=   last_checkpoint
      snake_case 						=   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:
      snake_case 						=   trainer.evaluate()
      trainer.log_metrics("""eval"""      ,    __lowerCAmelCase		)
      trainer.save_metrics("""eval"""      ,    __lowerCAmelCase		)

   # Write model card and (optionally) push to hub
   snake_case 						=   {
       """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'''




import os
from collections.abc import Iterator





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  Iterator[str]:
   for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase		):
      snake_case 						=   [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(__lowerCAmelCase		)[1] in (".py", ".ipynb"):
            yield os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		).lstrip("""./"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Any:
   return F'''{i * "  "}*''' if i else "\n##"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   old_path.split(os.sep		)
   for i, new_part in enumerate(new_path.split(os.sep		)		):
      if (i + 1 > len(__lowerCAmelCase		) or old_parts[i] != new_part) and new_part:
         print(F'''{md_prefix(__lowerCAmelCase		)} {new_part.replace("_"      ,    " "		).title()}'''		)
   return new_path





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  None:
   snake_case 						=   """"""
   for filepath in sorted(good_file_paths(__lowerCAmelCase		)		):
      snake_case						,    snake_case 						=   os.path.split(__lowerCAmelCase		)
      if filepath != old_path:
         snake_case 						=   print_path(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   (filepath.count(os.sep		) + 1) if filepath else 0
      snake_case 						=   F'''{filepath}/{filename}'''.replace(""" """      ,    """%20"""		)
      snake_case 						=   os.path.splitext(filename.replace("""_"""      ,    """ """		).title()		)[0]
      print(F'''{md_prefix(__lowerCAmelCase		)} [{filename}]({url})'''		)


if __name__ == "__main__":
 print_directory_md(".")

