


import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_A			=						get_logger(__name__)


_A			=						Path(__file__).parent / 'model_card_template.md'
_A			=						uuida().hex
_A			=						os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
_A			=						os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
_A			=						HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[Dict, str, None] = None    ):
     __UpperCamelCase   							=F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
     if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
          return ua + "; telemetry/off"
     if is_torch_available():
          ua += F'; torch/{_torch_version}'
     if is_flax_available():
          ua += F'; jax/{_jax_version}'
          ua += F'; flax/{_flax_version}'
     if is_onnx_available():
          ua += F'; onnxruntime/{_onnxruntime_version}'
     # CI will set this value to True
     if os.environ.get('DIFFUSERS_IS_CI'      ,					''    ).upper() in ENV_VARS_TRUE_VALUES:
          ua += "; is_ci/true"
     if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
          ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()    )
     elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
          ua += "; " + user_agent
     return ua

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None    ):
     if token is None:
          __UpperCamelCase   							=HfFolder.get_token()
     if organization is None:
          __UpperCamelCase   							=whoami(SCREAMING_SNAKE_CASE__    )['name']
          return F'{username}/{model_id}'
     else:
          return F'{organization}/{model_id}'

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     if not is_jinja_available():
          raise ValueError(
              'Modelcard rendering is based on Jinja templates.'
              ' Please make sure to have `jinja` installed before using `create_model_card`.'
              ' To install it, please run `pip install Jinja2`.'    )

     if hasattr(SCREAMING_SNAKE_CASE__      ,					'local_rank'    ) and args.local_rank not in [-1, 0]:
          return

     __UpperCamelCase   							=args.hub_token if hasattr(SCREAMING_SNAKE_CASE__      ,					'hub_token'    ) else None
     __UpperCamelCase   							=get_full_repo_name(SCREAMING_SNAKE_CASE__      ,					token=SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=ModelCard.from_template(
         card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
             language='en'      ,					license='apache-2.0'      ,					library_name='diffusers'      ,					tags=[]      ,					datasets=args.dataset_name      ,					metrics=[]      ,					)      ,					template_path=SCREAMING_SNAKE_CASE__      ,					model_name=SCREAMING_SNAKE_CASE__      ,					repo_name=SCREAMING_SNAKE_CASE__      ,					dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__      ,					'dataset_name'    ) else None      ,					learning_rate=args.learning_rate      ,					train_batch_size=args.train_batch_size      ,					eval_batch_size=args.eval_batch_size      ,					gradient_accumulation_steps=(
             args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE__      ,					'gradient_accumulation_steps'    ) else None
         )      ,					adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__      ,					'adam_beta1'    ) else None      ,					adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__      ,					'adam_beta2'    ) else None      ,					adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__      ,					'adam_weight_decay'    ) else None      ,					adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__      ,					'adam_epsilon'    ) else None      ,					lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__      ,					'lr_scheduler'    ) else None      ,					lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__      ,					'lr_warmup_steps'    ) else None      ,					ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__      ,					'ema_inv_gamma'    ) else None      ,					ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__      ,					'ema_power'    ) else None      ,					ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__      ,					'ema_max_decay'    ) else None      ,					mixed_precision=args.mixed_precision      ,					)

     __UpperCamelCase   							=os.path.join(args.output_dir      ,					'README.md'    )
     model_card.save(SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[str]      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None    ):
     if resolved_file is None or commit_hash is not None:
          return commit_hash
     __UpperCamelCase   							=str(Path(SCREAMING_SNAKE_CASE__    ).as_posix()    )
     __UpperCamelCase   							=re.search(r'snapshots/([^/]+)/'      ,					SCREAMING_SNAKE_CASE__    )
     if search is None:
          return None
     __UpperCamelCase   							=search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__    ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_A			=						os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
_A			=						os.path.join(hf_cache_home, 'diffusers')

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[str] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None    ):
     if new_cache_dir is None:
          __UpperCamelCase   							=DIFFUSERS_CACHE
     if old_cache_dir is None:
          __UpperCamelCase   							=old_diffusers_cache

     __UpperCamelCase   							=Path(SCREAMING_SNAKE_CASE__    ).expanduser()
     __UpperCamelCase   							=Path(SCREAMING_SNAKE_CASE__    ).expanduser()
     for old_blob_path in old_cache_dir.glob('**/blobs/*'    ):
          if old_blob_path.is_file() and not old_blob_path.is_symlink():
               __UpperCamelCase   							=new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__    )
               new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__      ,					exist_ok=SCREAMING_SNAKE_CASE__    )
               os.replace(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               try:
                    os.symlink(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               except OSError:
                    logger.warning(
                        'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'    )
     # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_A			=						os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
   _A			=						0
else:
   with open(cache_version_file) as f:
      try:
         _A			=						int(f.read())
      except ValueError:
         _A			=						0

if cache_version < 1:
   _A			=						os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
   if old_cache_is_not_empty:
      logger.warning(
          'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
          'existing cached models. This is a one-time operation, you can interrupt it or run it '
          'later by calling `diffusers.utils.hub_utils.move_cache()`.'
      )
      try:
         move_cache()
      except Exception as e:
         _A			=						'\n'.join(traceback.format_tb(e.__traceback__))
         logger.error(
             f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
             'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
             'message and we will do our best to help.'
         )

if cache_version < 1:
   try:
      os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
      with open(cache_version_file, 'w') as f:
         f.write('1')
   except Exception:
      logger.warning(
          f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
          'the directory exists and can be written to.'
      )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None    ):
     if variant is not None:
          __UpperCamelCase   							=weights_name.split('.'    )
          __UpperCamelCase   							=splits[:-1] + [variant] + splits[-1:]
          __UpperCamelCase   							='.'.join(SCREAMING_SNAKE_CASE__    )

     return weights_name





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					*,
    SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[str]=None      ,					):
     __UpperCamelCase   							=str(SCREAMING_SNAKE_CASE__    )
     if os.path.isfile(SCREAMING_SNAKE_CASE__    ):
          return pretrained_model_name_or_path
     elif os.path.isdir(SCREAMING_SNAKE_CASE__    ):
          if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    ):
               # Load from a PyTorch checkpoint
               __UpperCamelCase   							=os.path.join(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    ):
               __UpperCamelCase   							=os.path.join(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               return model_file
          else:
               raise EnvironmentError(
                   F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'    )
     else:
          # 1. First check if deprecated way of loading from branches is used
          if (
              revision in DEPRECATED_REVISION_ARGS
              and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
              and version.parse(version.parse(SCREAMING_SNAKE_CASE__    ).base_version    ) >= version.parse('0.20.0'    )
          ):
               try:
                    __UpperCamelCase   							=hf_hub_download(
                        SCREAMING_SNAKE_CASE__      ,					filename=_add_variant(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )      ,					cache_dir=SCREAMING_SNAKE_CASE__      ,					force_download=SCREAMING_SNAKE_CASE__      ,					proxies=SCREAMING_SNAKE_CASE__      ,					resume_download=SCREAMING_SNAKE_CASE__      ,					local_files_only=SCREAMING_SNAKE_CASE__      ,					use_auth_token=SCREAMING_SNAKE_CASE__      ,					user_agent=SCREAMING_SNAKE_CASE__      ,					subfolder=SCREAMING_SNAKE_CASE__      ,					revision=revision or commit_hash      ,					)
                    warnings.warn(
                        F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'      ,					SCREAMING_SNAKE_CASE__      ,					)
                    return model_file
               except:  # noqa: E722
                    warnings.warn(
                        F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )}\' so that the correct variant file can be added.'      ,					SCREAMING_SNAKE_CASE__      ,					)
          try:
               # 2. Load model file as usual
               __UpperCamelCase   							=hf_hub_download(
                   SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					cache_dir=SCREAMING_SNAKE_CASE__      ,					force_download=SCREAMING_SNAKE_CASE__      ,					proxies=SCREAMING_SNAKE_CASE__      ,					resume_download=SCREAMING_SNAKE_CASE__      ,					local_files_only=SCREAMING_SNAKE_CASE__      ,					use_auth_token=SCREAMING_SNAKE_CASE__      ,					user_agent=SCREAMING_SNAKE_CASE__      ,					subfolder=SCREAMING_SNAKE_CASE__      ,					revision=revision or commit_hash      ,					)
               return model_file

          except RepositoryNotFoundError:
               raise EnvironmentError(
                   F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
                   'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
                   'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
                   'login`.'    )
          except RevisionNotFoundError:
               raise EnvironmentError(
                   F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
                   'this model name. Check the model page at '
                   F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'    )
          except EntryNotFoundError:
               raise EnvironmentError(
                   F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'    )
          except HTTPError as err:
               raise EnvironmentError(
                   F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'    )
          except ValueError:
               raise EnvironmentError(
                   F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
                   F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
                   F' directory containing a file named {weights_name} or'
                   ' \nCheckout your internet connection or see how to run the library in'
                   ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'    )
          except EnvironmentError:
               raise EnvironmentError(
                   F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
                   '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
                   F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
                   F'containing a file named {weights_name}'    )









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
_A			=						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__	:



    """simple docstring"""






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






    def _a (      self   )  ->	Optional[int]:
         __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__	:



    """simple docstring"""






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






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=torch.stack([example['pixel_values'] for example in examples]    )
     return {"pixel_values": pixel_values}

def 						_UpperCAmelCase    (      ):
     # 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'      ,					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 / 2_56

     # 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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
   main()





