






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
__magic_name__:					List[str]							=     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")

__magic_name__:					List[Any]							=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__magic_name__:					int							=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 	snake_case__ :
 lowercase__						:					Optional[str]											=   field(
     default='''cifar10'''						,  metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
 lowercase__						:					Optional[str]											=   field(
     default=_lowerCAmelCase						,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
 lowercase__						:					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\'.'''}						,  )
 lowercase__						:					Optional[str]											=   field(default=_lowerCAmelCase						,  metadata={'''help''': '''A folder containing the training data.'''}				)
 lowercase__						:					Optional[str]											=   field(default=_lowerCAmelCase						,  metadata={'''help''': '''A folder containing the validation data.'''}				)
 lowercase__						:					Optional[float]											=   field(
     default=0.15						,  metadata={'''help''': '''Percent to split off of train for validation.'''}				)
 lowercase__						:					int											=   field(default=32						,  metadata={'''help''': '''The size of the square patches to use for masking.'''}				)
 lowercase__						:					float											=   field(
     default=0.6						,  metadata={'''help''': '''Percentage of patches to mask.'''}						,  )
 lowercase__						:					Optional[int]											=   field(
     default=_lowerCAmelCase						,  metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }						,  )
 lowercase__						:					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       __magic_name__    (    self			)							->				Optional[Any]:
       __magic_name__      :       str       						= {}
       if self.train_dir is not None:
             __magic_name__      :       int       						= self.train_dir
       if self.validation_dir is not None:
             __magic_name__      :       Union[str, Any]       						= self.validation_dir
       __magic_name__      :       List[str]       						= data_files if data_files else None





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





class 	snake_case__ :
 def __init__(    self      ,       lowerCAmelCase__=1_92      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=0.6			)							->				Optional[int]:
       __magic_name__      :       str       						= input_size
       __magic_name__      :       Dict       						= mask_patch_size
       __magic_name__      :       Optional[Any]       						= model_patch_size
       __magic_name__      :       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"""			)

       __magic_name__      :       int       						= self.input_size // self.mask_patch_size
       __magic_name__      :       Dict       						= self.mask_patch_size // self.model_patch_size

       __magic_name__      :       List[Any]       						= self.rand_size**2
       __magic_name__      :       str       						= int(np.ceil(self.token_count * self.mask_ratio			)			)







 def __call__(    self			)							->				Dict:
       __magic_name__      :       Dict       						= np.random.permutation(self.token_count			)[: self.mask_count]
       __magic_name__      :       Optional[Any]       						= np.zeros(self.token_count      ,       dtype=lowerCAmelCase__			)
       __magic_name__      :       Dict       						= 1

       __magic_name__      :       int       						= mask.reshape((self.rand_size, self.rand_size)			)
       __magic_name__      :       int       						= mask.repeat(self.scale      ,       axis=0			).repeat(self.scale      ,       axis=1			)

       return torch.tensor(mask.flatten()			)



def    UpperCamelCase						(	_A			):






      """simple docstring"""







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

def    UpperCamelCase						(	):






      """simple docstring"""







      __magic_name__      :       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.
            __magic_name__      :       int       						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            __magic_name__      :       List[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""",   _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()

      __magic_name__      :       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.
      __magic_name__      :       Optional[int]       						= None
      if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
            __magic_name__      :       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.
      __magic_name__      :       List[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.
      __magic_name__      :       Dict       						= 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:
            __magic_name__      :       Dict       						= ds["""train"""].train_test_split(data_args.train_val_split			)
            __magic_name__      :       str       						= split["""train"""]
            __magic_name__      :       Union[str, Any]       						= split["""test"""]

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

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

      # create model
      if model_args.model_name_or_path:
            __magic_name__      :       List[str]       						= 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"""			)
            __magic_name__      :       Optional[int]       						= AutoModelForMaskedImageModeling.from_config(_A			)

      if training_args.do_train:
            __magic_name__      :       Any       						= ds["""train"""].column_names
      else:
            __magic_name__      :       List[str]       						= ds["""validation"""].column_names

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

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

      # Write model card and (optionally) push to hub
      __magic_name__      :       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()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
