






"""simple docstring"""







import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
				import bitsandbytes as bnb

from copy import deepcopy


_lowerCAmelCase            =	logging.getLogger(__name__)


def      UpperCamelCase       ( _A  ,				_A  ,				_A = None  ,				_A = None  ,				_A = None  ,				_A = None  ,				_A = None  ,				_A = False  ,				)     ->       Dict:
				lowercase    :      int       = bnb_quantization_config.load_in_abit
				lowercase    :      Union[str, Any]       = bnb_quantization_config.load_in_abit

				if load_in_abit and not is_abit_bnb_available():
								raise ImportError(
								    """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
								    """ make sure you have the latest version of `bitsandbytes` installed.""" )
				if load_in_abit and not is_abit_bnb_available():
								raise ValueError(
								    """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
								    """make sure you have the latest version of `bitsandbytes` installed.""" )

				lowercase    :      Optional[Any]       = []
				# custom device map
				if isinstance(_lowercase  ,				_lowercase ) and len(device_map.keys() ) > 1:
								lowercase    :      List[Any]       = [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

				# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
				if bnb_quantization_config.skip_modules is None:
								lowercase    :      Dict       = get_keys_to_not_convert(_lowercase )

				# add cpu modules to skip modules only for 4-bit modules
				if load_in_abit:
								bnb_quantization_config.skip_modules.extend(_lowercase )
				lowercase    :      Any       = bnb_quantization_config.skip_modules

				# We add the modules we want to keep in full precision
				if bnb_quantization_config.keep_in_fpaa_modules is None:
								lowercase    :      Any       = []
				lowercase    :      int       = bnb_quantization_config.keep_in_fpaa_modules
				modules_to_not_convert.extend(_lowercase )

				# compatibility with peft
				lowercase    :      List[Any]       = load_in_abit
				lowercase    :      Any       = load_in_abit

				lowercase    :      Dict       = get_parameter_device(_lowercase )
				if model_device.type != "meta":
								# quantization of an already loaded model
								logger.warning(
								    """It is not recommended to quantize a loaded model. """
								    """The model should be instantiated under the `init_empty_weights` context manager.""" )
								lowercase    :      Union[str, Any]       = replace_with_bnb_layers(_lowercase  ,				_lowercase  ,				modules_to_not_convert=_lowercase )
								# convert param to the right dtype
								lowercase    :      Tuple       = bnb_quantization_config.torch_dtype
								for name, param in model.state_dict().items():
												if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
																param.to(torch.floataa )
																if param.dtype != torch.floataa:
																				lowercase    :      Tuple       = name.replace(""".weight"""  ,				"""""" ).replace(""".bias"""  ,				"""""" )
																				lowercase    :      Union[str, Any]       = getattr(_lowercase  ,				_lowercase  ,				_lowercase )
																				if param is not None:
																								param.to(torch.floataa )
												elif torch.is_floating_point(_lowercase ):
																param.to(_lowercase )
								if model_device.type == "cuda":
												# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
												model.cuda(torch.cuda.current_device() )
												torch.cuda.empty_cache()
								elif torch.cuda.is_available():
												model.to(torch.cuda.current_device() )
								else:
												raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
								logger.info(
								    F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
								    """We move the model to cuda.""" )
								return model

				elif weights_location is None:
								raise RuntimeError(
								    F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )

				else:
								with init_empty_weights():
												lowercase    :      List[str]       = replace_with_bnb_layers(
												    _lowercase  ,				_lowercase  ,				modules_to_not_convert=_lowercase )

								lowercase    :      Optional[int]       = get_quantized_model_device_map(
								    _lowercase  ,				_lowercase  ,				_lowercase  ,				max_memory=_lowercase  ,				no_split_module_classes=_lowercase  ,				)
								if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
												lowercase    :      Any       = True

								lowercase    :      int       = any(x in list(device_map.values() ) for x in ["""cpu""", """disk"""] )

								load_checkpoint_in_model(
								    _lowercase  ,				_lowercase  ,				_lowercase  ,				dtype=bnb_quantization_config.torch_dtype  ,				offload_folder=_lowercase  ,				offload_state_dict=_lowercase  ,				keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules  ,				offload_abit_bnb=load_in_abit and offload  ,				)
								return dispatch_model(_lowercase  ,				device_map=_lowercase  ,				offload_dir=_lowercase )


def      UpperCamelCase       ( _A  ,				_A  ,				_A=None  ,				_A=None  ,				_A=None )     ->       str:
				if device_map is None:
								if torch.cuda.is_available():
												lowercase    :      int       = {"""""": torch.cuda.current_device()}
								else:
												raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
								logger.info("""The device_map was not initialized.""" """Setting device_map to `{\'\':torch.cuda.current_device()}`.""" )

				if isinstance(_lowercase  ,				_lowercase ):
								if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
												raise ValueError(
												    """If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or """
												    """\'sequential\'.""" )

								lowercase    :      Any       = {}
								special_dtypes.update(
								    {
								        name: bnb_quantization_config.torch_dtype
								        for name, _ in model.named_parameters()
								        if any(m in name for m in bnb_quantization_config.skip_modules )
								    } )
								special_dtypes.update(
								    {
								        name: torch.floataa
								        for name, _ in model.named_parameters()
								        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
								    } )

								lowercase    :      Dict       = {}
								lowercase    :      List[str]       = special_dtypes
								lowercase    :      List[Any]       = no_split_module_classes
								lowercase    :      Tuple       = bnb_quantization_config.target_dtype

								# get max_memory for each device.
								if device_map != "sequential":
												lowercase    :      Optional[Any]       = get_balanced_memory(
												    _lowercase  ,				low_zero=(device_map == """balanced_low_0""")  ,				max_memory=_lowercase  ,				**_lowercase  ,				)

								lowercase    :      Tuple       = max_memory
								lowercase    :      Union[str, Any]       = infer_auto_device_map(_lowercase  ,				**_lowercase )

				if isinstance(_lowercase  ,				_lowercase ):
								# check if don't have any quantized module on the cpu
								lowercase    :      Optional[int]       = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

								lowercase    :      Optional[Any]       = {
								    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
								}
								for device in ["cpu", "disk"]:
												if device in device_map_without_some_modules.values():
																if bnb_quantization_config.load_in_abit:
																				raise ValueError(
																				    """\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        """ )
																else:
																				logger.info(
																				    """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit""" )
								del device_map_without_some_modules
				return device_map


def      UpperCamelCase       ( _A  ,				_A  ,				_A=None  ,				_A=None )     ->       Tuple:

				if modules_to_not_convert is None:
								lowercase    :      List[str]       = []

				lowercase      ,      lowercase    :      List[Any]       = _replace_with_bnb_layers(
				    _lowercase  ,				_lowercase  ,				_lowercase  ,				_lowercase )
				if not has_been_replaced:
								logger.warning(
								    """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
								    """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
								    """ Please double check your model architecture, or submit an issue on github if you think this is"""
								    """ a bug.""" )
				return model


def      UpperCamelCase       ( _A  ,				_A  ,				_A=None  ,				_A=None  ,				)     ->       Union[str, Any]:
				lowercase    :      Optional[Any]       = False
				for name, module in model.named_children():
								if current_key_name is None:
												lowercase    :      int       = []
								current_key_name.append(_lowercase )
								if isinstance(_lowercase  ,				nn.Linear ) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												lowercase    :      str       = """.""".join(_lowercase )
												lowercase    :      Any       = True
												for key in modules_to_not_convert:
																if (
																    (key in current_key_name_str) and (key + "." in current_key_name_str)
																) or key == current_key_name_str:
																				lowercase    :      str       = False
																				break
												if proceed:
																# Load bnb module with empty weight and replace ``nn.Linear` module
																if bnb_quantization_config.load_in_abit:
																				lowercase    :      Union[str, Any]       = bnb.nn.LinearabitLt(
																				    module.in_features  ,				module.out_features  ,				module.bias is not None  ,				has_fpaa_weights=_lowercase  ,				threshold=bnb_quantization_config.llm_inta_threshold  ,				)
																elif bnb_quantization_config.load_in_abit:
																				lowercase    :      Dict       = bnb.nn.Linearabit(
																				    module.in_features  ,				module.out_features  ,				module.bias is not None  ,				bnb_quantization_config.bnb_abit_compute_dtype  ,				compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant  ,				quant_type=bnb_quantization_config.bnb_abit_quant_type  ,				)
																else:
																				raise ValueError("""load_in_8bit and load_in_4bit can\'t be both False""" )
																lowercase    :      Optional[int]       = module.weight.data
																if module.bias is not None:
																				lowercase    :      Any       = module.bias.data
																bnb_module.requires_grad_(_lowercase )
																setattr(_lowercase  ,				_lowercase  ,				_lowercase )
																lowercase    :      List[str]       = True
								if len(list(module.children() ) ) > 0:
												lowercase      ,      lowercase    :      Dict       = _replace_with_bnb_layers(
												    _lowercase  ,				_lowercase  ,				_lowercase  ,				_lowercase )
												lowercase    :      Any       = has_been_replaced | _has_been_replaced
								# Remove the last key for recursion
								current_key_name.pop(-1 )
				return model, has_been_replaced


def      UpperCamelCase       ( _A )     ->       Union[str, Any]:
				# Create a copy of the model
				with init_empty_weights():
								lowercase    :      Tuple       = deepcopy(_lowercase )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

				lowercase    :      Any       = find_tied_parameters(_lowercase )
				# For compatibility with Accelerate < 0.18
				if isinstance(_lowercase  ,				_lowercase ):
								lowercase    :      int       = sum(list(tied_params.values() )  ,				[] ) + list(tied_params.keys() )
				else:
								lowercase    :      int       = sum(_lowercase  ,				[] )
				lowercase    :      Optional[Any]       = len(_lowercase ) > 0

				# Check if it is a base model
				lowercase    :      List[str]       = False
				if hasattr(_lowercase  ,				"""base_model_prefix""" ):
								lowercase    :      str       = not hasattr(_lowercase  ,				model.base_model_prefix )

				# Ignore this for base models (BertModel, GPT2Model, etc.)
				if (not has_tied_params) and is_base_model:
								return []

				# otherwise they have an attached head
				lowercase    :      Tuple       = list(model.named_children() )
				lowercase    :      Union[str, Any]       = [list_modules[-1][0]]

				# add last module together with tied weights
				lowercase    :      int       = set(_lowercase ) - set(_lowercase )
				lowercase    :      int       = list(set(_lowercase ) ) + list(_lowercase )

				# remove ".weight" from the keys
				lowercase    :      Any       = [""".weight""", """.bias"""]
				lowercase    :      List[Any]       = []
				for name in list_untouched:
								for name_to_remove in names_to_remove:
												if name_to_remove in name:
																lowercase    :      Any       = name.replace(_lowercase  ,				"""""" )
								filtered_module_names.append(_lowercase )

				return filtered_module_names


def      UpperCamelCase       ( _A )     ->       Any:
				for m in model.modules():
								if isinstance(_lowercase  ,				bnb.nn.Linearabit ):
												return True
				return False


def      UpperCamelCase       ( _A )     ->       Tuple:
				return next(parameter.parameters() ).device


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       Dict:
				# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
				if fpaa_statistics is None:
								set_module_tensor_to_device(_lowercase  ,				_lowercase  ,				0  ,				dtype=_lowercase  ,				value=_lowercase )
								lowercase    :      Dict       = param_name
								lowercase    :      str       = model
								if "." in tensor_name:
												lowercase    :      List[Any]       = tensor_name.split(""".""" )
												for split in splits[:-1]:
																lowercase    :      Union[str, Any]       = getattr(_lowercase  ,				_lowercase )
																if new_module is None:
																				raise ValueError(F"""{module} has no attribute {split}.""" )
																lowercase    :      List[str]       = new_module
												lowercase    :      Any       = splits[-1]
								# offload weights
								lowercase    :      int       = False
								offload_weight(module._parameters[tensor_name]  ,				_lowercase  ,				_lowercase  ,				index=_lowercase )
								if hasattr(module._parameters[tensor_name]  ,				"""SCB""" ):
												offload_weight(
												    module._parameters[tensor_name].SCB  ,				param_name.replace("""weight"""  ,				"""SCB""" )  ,				_lowercase  ,				index=_lowercase  ,				)
				else:
								offload_weight(_lowercase  ,				_lowercase  ,				_lowercase  ,				index=_lowercase )
								offload_weight(_lowercase  ,				param_name.replace("""weight"""  ,				"""SCB""" )  ,				_lowercase  ,				index=_lowercase )

				set_module_tensor_to_device(_lowercase  ,				_lowercase  ,				"""meta"""  ,				dtype=_lowercase  ,				value=torch.empty(*param.size() ) )


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       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default="cifar10"			,	metadata={"help": "Name of a dataset from the datasets package"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The column name of the images in the files."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the training data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the validation data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=0.15			,	metadata={"help": "Percent to split off of train for validation."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	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       _lowerCAmelCase  :

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




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=1E-3			,	metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}							)




def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase						    =					torch.stack([example['pixel_values'] for example in examples]			)
     return {"pixel_values": pixel_values}

def  __lowerCamelCase					( )       ->      List[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.

     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'						,     _lowercase						,     _lowercase			)

     # 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(_lowercase			)
     transformers.utils.logging.set_verbosity(_lowercase			)
     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						,     _lowercase			) 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						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     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						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     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=_lowercase						,     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(_lowercase			)

     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 _lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
             RandomResizedCrop(_lowercase						,     scale=(0.2, 1.0)						,     interpolation=InterpolationMode.BICUBIC			),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean						,     std=image_processor.image_std			),
         ]			)

     def preprocess_images(_lowercase			):
          UpperCamelCase						    =					[transforms(_lowercase			) 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(_lowercase			)

     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(_lowercase			)

     # 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=_lowercase						,     args=_lowercase						,     train_dataset=ds['train'] if training_args.do_train else None						,     eval_dataset=ds['validation'] if training_args.do_eval else None						,     tokenizer=_lowercase						,     data_collator=_lowercase						,     )

     # 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=_lowercase			)
          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'						,     _lowercase			)
          trainer.save_metrics('eval'						,     _lowercase			)

     # 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(**_lowercase			)
     else:
          trainer.create_model_card(**_lowercase			)

def  __lowerCamelCase					( _lowercase			)       ->      int:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()


