
'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
lowercase							=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')






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="cifar10" ,metadata={"help": "Name of a dataset from the datasets package"}    )
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={"help": "The column name of the images in the files."}    )
       UpperCAmelCase										=field(default=UpperCamelCase_ ,metadata={"help": "A folder containing the training data."}    )
       UpperCAmelCase										=field(default=UpperCamelCase_ ,metadata={"help": "A folder containing the validation data."}    )
       UpperCAmelCase										=field(
           default=0.15 ,metadata={"help": "Percent to split off of train for validation."}    )
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           } ,)
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           } ,)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   ={}
          if self.train_dir is not None:
             _UpperCAmelCase       :       Optional[Any]							   =self.train_dir
          if self.validation_dir is not None:
             _UpperCAmelCase       :       Optional[Any]							   =self.validation_dir
          _UpperCAmelCase       :       str							   =data_files if data_files else None






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={
               "help": (
                   "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
               )
           } ,)
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}    )
       UpperCAmelCase										=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"
               )
           } ,)
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}    )
       UpperCAmelCase										=field(
           default="main" ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,)
       UpperCAmelCase										=field(default=UpperCamelCase_ ,metadata={"help": "Name or path of preprocessor config."}    )
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           } ,)
       UpperCAmelCase										=field(
           default=0.75 ,metadata={"help": "The ratio of the number of masked tokens in the input sequence."}    )
       UpperCAmelCase										=field(
           default=UpperCamelCase_ ,metadata={"help": "Whether or not to train with normalized pixel values as target."}    )






@dataclass
class  __magic_name__							(		UpperCamelCase_    ):
       UpperCAmelCase										=field(
           default=1e-3 ,metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}    )






def lowerCamelCase__      (						__lowerCamelCase	:							Any							):







   '''simple docstring'''




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





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
   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       :       int							   =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
   else:
      _UpperCAmelCase       :       Dict							   =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_mae'	,   __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()

   _UpperCAmelCase       :       List[Any]							   =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.
   _UpperCAmelCase       :       Any							   =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       :       str							   =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       :       Any							   =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:
      _UpperCAmelCase       :       List[Any]							   =ds["""train"""].train_test_split(data_args.train_val_split							)
      _UpperCAmelCase       :       List[str]							   =split["""train"""]
      _UpperCAmelCase       :       int							   =split["""test"""]

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   _UpperCAmelCase       :       int							   ={
       """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:
      _UpperCAmelCase       :       List[Any]							   =ViTMAEConfig.from_pretrained(model_args.config_name	,   **__lowerCAmelCase							)
   elif model_args.model_name_or_path:
      _UpperCAmelCase       :       List[str]							   =ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,   **__lowerCAmelCase							)
   else:
      _UpperCAmelCase       :       int							   =ViTMAEConfig()
      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}"							)

    # adapt config
   config.update(
       {
           'mask_ratio': model_args.mask_ratio,
           'norm_pix_loss': model_args.norm_pix_loss,
       }							)

   # create image processor
   if model_args.image_processor_name:
      _UpperCAmelCase       :       int							   =ViTImageProcessor.from_pretrained(model_args.image_processor_name	,   **__lowerCAmelCase							)
   elif model_args.model_name_or_path:
      _UpperCAmelCase       :       Tuple							   =ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,   **__lowerCAmelCase							)
   else:
      _UpperCAmelCase       :       Optional[Any]							   =ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _UpperCAmelCase       :       List[str]							   =ViTMAEForPreTraining.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'							)
      _UpperCAmelCase       :       Union[str, Any]							   =ViTMAEForPreTraining(__lowerCAmelCase							)

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

   if data_args.image_column_name is not None:
      _UpperCAmelCase       :       List[Any]							   =data_args.image_column_name
   elif "image" in column_names:
      _UpperCAmelCase       :       Tuple							   ="""image"""
   elif "img" in column_names:
      _UpperCAmelCase       :       Dict							   ="""img"""
   else:
      _UpperCAmelCase       :       List[str]							   =column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      _UpperCAmelCase       :       str							   =image_processor.size["""shortest_edge"""]
   else:
      _UpperCAmelCase       :       List[str]							   =(image_processor.size["""height"""], image_processor.size["""width"""])
   _UpperCAmelCase       :       Any							   =Compose(
       [
           Lambda(lambda __lowerCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
           RandomResizedCrop(__lowerCAmelCase	,   scale=(0.2, 1.0)	,   interpolation=InterpolationMode.BICUBIC							),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean	,   std=image_processor.image_std							),
       ]							)

   def preprocess_images(__lowerCamelCase	:							List[Any]							):
      _UpperCAmelCase       :       Optional[int]							   =[transforms(__lowerCAmelCase							) for image in 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       :       Union[str, Any]							   =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:
         _UpperCAmelCase       :       Any							   =(
             ds["""validation"""].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
         )
      # Set the validation transforms
      ds["validation"].set_transform(__lowerCAmelCase							)

   # Compute absolute learning rate
   _UpperCAmelCase       :       Dict							   =(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _UpperCAmelCase       :       List[str]							   =training_args.base_learning_rate * total_train_batch_size / 2_5_6

   # Initialize our trainer
   _UpperCAmelCase       :       str							   =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:
      _UpperCAmelCase       :       List[Any]							   =None
      if training_args.resume_from_checkpoint is not None:
         _UpperCAmelCase       :       int							   =training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _UpperCAmelCase       :       Tuple							   =last_checkpoint
      _UpperCAmelCase       :       Tuple							   =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:
      _UpperCAmelCase       :       List[Any]							   =trainer.evaluate()
      trainer.log_metrics('eval'	,   __lowerCAmelCase							)
      trainer.save_metrics('eval'	,   __lowerCAmelCase							)

   # Write model card and (optionally) push to hub
   _UpperCAmelCase       :       str							   ={
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__lowerCAmelCase							)
   else:
      trainer.create_model_card(**__lowerCAmelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple							):







   '''simple docstring'''




   main()


if __name__ == "__main__":
 main()





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="glpn"






       def __init__(      self  ,							snake_case=3  ,							snake_case=4  ,							snake_case=[2, 2, 2, 2]  ,							snake_case=[8, 4, 2, 1]  ,							snake_case=[3_2, 6_4, 1_6_0, 2_5_6]  ,							snake_case=[7, 3, 3, 3]  ,							snake_case=[4, 2, 2, 2]  ,							snake_case=[1, 2, 5, 8]  ,							snake_case=[4, 4, 4, 4]  ,							snake_case="gelu"  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=0.1  ,							snake_case=1E-6  ,							snake_case=6_4  ,							snake_case=1_0  ,							snake_case=-1  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(**snake_case)

          _UpperCAmelCase       :       Any							   =num_channels
          _UpperCAmelCase       :       List[str]							   =num_encoder_blocks
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       str							   =sr_ratios
          _UpperCAmelCase       :       Dict							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =patch_sizes
          _UpperCAmelCase       :       Any							   =strides
          _UpperCAmelCase       :       List[str]							   =mlp_ratios
          _UpperCAmelCase       :       Dict							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[Any]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Tuple							   =drop_path_rate
          _UpperCAmelCase       :       str							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[int]							   =decoder_hidden_size
          _UpperCAmelCase       :       List[str]							   =max_depth
          _UpperCAmelCase       :       Dict							   =head_in_index

