'''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_:     Dict  =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_:     Optional[Any]  =list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
SCREAMING_SNAKE_CASE_:     Any  =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        __A      :
    a__       :						Optional[str]          =  field(
        default="""cifar10"""		,	metadata={"""help""": """Name of a dataset from the datasets package"""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}		,	)
    a__       :						Optional[str]          =  field(default=UpperCamelCase__		,	metadata={"""help""": """A folder containing the training data."""}	)
    a__       :						Optional[str]          =  field(default=UpperCamelCase__		,	metadata={"""help""": """A folder containing the validation data."""}	)
    a__       :						Optional[float]          =  field(
        default=0.1_5		,	metadata={"""help""": """Percent to split off of train for validation."""}	)
    a__       :						int          =  field(default=32		,	metadata={"""help""": """The size of the square patches to use for masking."""}	)
    a__       :						float          =  field(
        default=0.6		,	metadata={"""help""": """Percentage of patches to mask."""}		,	)
    a__       :						Optional[int]          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }		,	)
    a__       :						Optional[int]          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }		,	)



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   {}
         if self.train_dir is not None:
              UpperCAmelCase_           =   self.train_dir
         if self.validation_dir is not None:
              UpperCAmelCase_           =   self.validation_dir
         UpperCAmelCase_           =   data_files if data_files else None




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




class        __A      :



    def __init__(self    :    int   ,			__a    :    Union[str, Any]=192   ,			__a    :    str=32   ,			__a    :    List[Any]=4   ,			__a    :    Any=0.6 ):
         UpperCAmelCase_           =   input_size
         UpperCAmelCase_           =   mask_patch_size
         UpperCAmelCase_           =   model_patch_size
         UpperCAmelCase_           =   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_           =   self.input_size // self.mask_patch_size
         UpperCAmelCase_           =   self.mask_patch_size // self.model_patch_size

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






    def __call__(self    :    Dict ):
         UpperCAmelCase_           =   np.random.permutation(self.token_count )[: self.mask_count]
         UpperCAmelCase_           =   np.zeros(self.token_count   ,			dtype=__a )
         UpperCAmelCase_           =   1

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

         return torch.tensor(mask.flatten() )





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Union[str, Any]:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   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_           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   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_           =   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_           =   None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_           =   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_           =   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_           =   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_           =   ds["train"].train_test_split(data_args.train_val_split			)
          UpperCAmelCase_           =   split["train"]
          UpperCAmelCase_           =   split["test"]

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

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

     # create model
     if model_args.model_name_or_path:
          UpperCAmelCase_           =   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_           =   AutoModelForMaskedImageModeling.from_config(snake_case_			)

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

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

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

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





'''simple docstring'''





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

         super().__init__(__a   ,			[AffineTransform(loc=self.loc   ,			scale=self.scale   ,			event_dim=__a )] )



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return self.base_dist.mean * self.scale + self.loc



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



    def __init__(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Dict[str, int]   ,			__a    :    Callable[..., Tuple[torch.Tensor]]   ,			**__a    :    List[str] ):
         super().__init__(**__a )
         UpperCAmelCase_           =   args_dim
         UpperCAmelCase_           =   nn.ModuleList([nn.Linear(__a   ,			__a ) for dim in args_dim.values()] )
         UpperCAmelCase_           =   domain_map






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   self._base_distribution(__a )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(__a   ,			loc=__a   ,			scale=__a   ,			event_dim=self.event_dim )



    @property
    def 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.event_shape )



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



    def 					_lowercase						(self    :    List[str]   ,			__a    :    int ):
         return ParameterProjection(
             in_features=__a   ,			args_dim=self.args_dim   ,			domain_map=LambdaLayer(self.domain_map )   ,			)



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






    @staticmethod
    def 					_lowercase						(__a    :    torch.Tensor ):
         return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=__a   ,			logits=__a )
         else:
              return Independent(self.distribution_class(total_count=__a   ,			logits=__a )   ,			1 )






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

         if scale is not None:
              # See scaling property of Gamma.
              logits += scale.log()

         return self._base_distribution((total_count, logits) )




