


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
__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")




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




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







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




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





   """simple docstring"""




   UpperCamelCase         =					torch.stack([example["""pixel_values"""] for example in examples]	)
   return {"pixel_values": pixel_values}






def 							_lowercase    (		):





   """simple docstring"""




   UpperCamelCase         =					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  ,  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_mae"""       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_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(SCREAMING_SNAKE_CASE_	)
   transformers.utils.logging.set_verbosity(SCREAMING_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       ,  SCREAMING_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"""]

   # Load pretrained model and image processor
   #
   # 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:
      UpperCamelCase         =					ViTMAEConfig.from_pretrained(model_args.config_name       ,  **SCREAMING_SNAKE_CASE_	)
   elif model_args.model_name_or_path:
      UpperCamelCase         =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
   else:
      UpperCamelCase         =					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         =					ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,  **SCREAMING_SNAKE_CASE_	)
   elif model_args.model_name_or_path:
      UpperCamelCase         =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,  **SCREAMING_SNAKE_CASE_	)
   else:
      UpperCamelCase         =					ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      UpperCamelCase         =					ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path       ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)       ,  config=SCREAMING_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         =					ViTMAEForPreTraining(SCREAMING_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 MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      UpperCamelCase         =					image_processor.size["""shortest_edge"""]
   else:
      UpperCamelCase         =					(image_processor.size["""height"""], image_processor.size["""width"""])
   UpperCamelCase         =					Compose(
       [
           Lambda(lambda SCREAMING_SNAKE_CASE_	: img.convert("""RGB"""	) if img.mode != "RGB" else img	),
           RandomResizedCrop(SCREAMING_SNAKE_CASE_       ,  scale=(0.2, 1.0)       ,  interpolation=InterpolationMode.BICUBIC	),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean       ,  std=image_processor.image_std	),
       ]	)

   def preprocess_images(SCREAMING_SNAKE_CASE_   :		Optional[Any]	):
      UpperCamelCase         =					[transforms(SCREAMING_SNAKE_CASE_	) 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         =					ds["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
      # Set the training transforms
      ds["train"].set_transform(SCREAMING_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(SCREAMING_SNAKE_CASE_	)

   # Compute absolute learning rate
   UpperCamelCase         =					(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      UpperCamelCase         =					training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   UpperCamelCase         =					Trainer(
       model=SCREAMING_SNAKE_CASE_       ,  args=SCREAMING_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=SCREAMING_SNAKE_CASE_       ,  data_collator=SCREAMING_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=SCREAMING_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"""       ,  SCREAMING_SNAKE_CASE_	)
      trainer.save_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





   """simple docstring"""




   main()


if __name__ == "__main__":
   main()








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			for i in range(len(SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Exception	):





			"""simple docstring"""




			UpperCamelCase         =					[
			    """CUDA out of memory.""",  # CUDA OOM
			    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
			    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
			]
			if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		callable = None       ,  SCREAMING_SNAKE_CASE_   :		int = 128	):





			"""simple docstring"""




			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE_       ,  starting_batch_size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					starting_batch_size

			def decorator(*SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  **SCREAMING_SNAKE_CASE_   :		Tuple	):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						UpperCamelCase         =					list(inspect.signature(SCREAMING_SNAKE_CASE_	).parameters.keys()	)
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE_	) < (len(SCREAMING_SNAKE_CASE_	) + 1):
									UpperCamelCase         =					""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]       ,  args[1:]	)]	)
									raise TypeError(
									    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'	)
						while True:
									if batch_size == 0:
												raise RuntimeError("""No executable batch size found, reached zero."""	)
									try:
												return function(SCREAMING_SNAKE_CASE_       ,  *SCREAMING_SNAKE_CASE_       ,  **SCREAMING_SNAKE_CASE_	)
									except Exception as e:
												if should_reduce_batch_size(SCREAMING_SNAKE_CASE_	):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator




