


import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__			:      Dict								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""input_ids""", """attention_mask"""]






							def __init__(      self			,						lowerCAmelCase__="</s>"			,						lowerCAmelCase__="<unk>"			,						lowerCAmelCase__="<pad>"			,						lowerCAmelCase__=1_2_5			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if extra_ids > 0 and additional_special_tokens is None:
													SCREAMING_SNAKE_CASE_		:     Any					=					[F'''<extra_id_{i}>''' for i in range(lowerCAmelCase__       )]
										elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													SCREAMING_SNAKE_CASE_		:     str					=					len(set(filter(lambda lowerCAmelCase__       : bool('extra_id' in str(lowerCAmelCase__       )       )			,						lowerCAmelCase__       )       )       )
													if extra_tokens != extra_ids:
																raise ValueError(
																    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																    ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
																    ' extra_ids tokens'       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else pad_token
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else eos_token
										SCREAMING_SNAKE_CASE_		:     Any					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else unk_token

										super().__init__(
										    eos_token=lowerCAmelCase__			,						unk_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						extra_ids=lowerCAmelCase__			,						additional_special_tokens=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					extra_ids

										SCREAMING_SNAKE_CASE_		:     Dict					=					2**8  # utf is 8 bits

										# define special tokens dict
										SCREAMING_SNAKE_CASE_		:     Dict[int, str]					=					{
										    self.pad_token: 0,
										    self.eos_token: 1,
										    self.unk_token: 2,
										}
										SCREAMING_SNAKE_CASE_		:     int					=					len(self.special_tokens_encoder       )
										SCREAMING_SNAKE_CASE_		:     str					=					len(lowerCAmelCase__       )
										for i, token in enumerate(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.vocab_size + i - n
										SCREAMING_SNAKE_CASE_		:     Dict[str, int]					=					{v: k for k, v in self.special_tokens_encoder.items()}






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self._utf_vocab_size + self._num_special_tokens + self._extra_ids






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False       ):





										"""simple docstring"""




										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCAmelCase__			,						token_ids_a=lowerCAmelCase__			,						already_has_special_tokens=lowerCAmelCase__       )

										# normal case: some special tokens
										if token_ids_a is None:
													return ([0] * len(lowerCAmelCase__       )) + [1]
										return ([0] * len(lowerCAmelCase__       )) + [1] + ([0] * len(lowerCAmelCase__       )) + [1]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if len(lowerCAmelCase__       ) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
													    ' eos tokens being added.'       )
													return token_ids
										else:
													return token_ids + [self.eos_token_id]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[self.eos_token_id]

										if token_ids_a is None:
													return len(token_ids_a + eos       ) * [0]
										return len(token_ids_a + eos + token_ids_a + eos       ) * [0]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self._add_eos_if_not_present(lowerCAmelCase__       )
										if token_ids_a is None:
													return token_ids_a
										else:
													SCREAMING_SNAKE_CASE_		:     Dict					=					self._add_eos_if_not_present(lowerCAmelCase__       )
													return token_ids_a + token_ids_a






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[chr(lowerCAmelCase__       ) for i in text.encode('utf-8'       )]
										return tokens






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if token in self.special_tokens_encoder:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
													SCREAMING_SNAKE_CASE_		:     str					=					self.added_tokens_encoder[token]
										elif len(lowerCAmelCase__       ) != 1:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.unk_token_id
										else:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ord(lowerCAmelCase__       ) + self._num_special_tokens
										return token_id






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if index in self.special_tokens_decoder:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.special_tokens_decoder[index]
										else:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					chr(index - self._num_special_tokens       )
										return token






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					b''
										for token in tokens:
													if token in self.special_tokens_decoder:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					self.special_tokens_decoder[token].encode('utf-8'       )
													elif token in self.added_tokens_decoder:
																SCREAMING_SNAKE_CASE_		:     Any					=					self.special_tokens_decoder[token].encode('utf-8'       )
													elif token in self.special_tokens_encoder:
																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					token.encode('utf-8'       )
													elif token in self.added_tokens_encoder:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					token.encode('utf-8'       )
													else:
																SCREAMING_SNAKE_CASE_		:     int					=					bytes([ord(lowerCAmelCase__       )]       )
													bstring += tok_string
										SCREAMING_SNAKE_CASE_		:     int					=					bstring.decode('utf-8'			,						errors='ignore'       )
										return string






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										return ()












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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       {}
       if self.train_dir is not None:
            A_          =       self.train_dir
       if self.validation_dir is not None:
            A_          =       self.validation_dir
       A_          =       data_files if data_files else None





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





@dataclass
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						float							=   field(
      default=1E-3     ,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )




def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				List[str]:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       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.
          A_   ,    A_   ,    A_          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          A_   ,    A_   ,    A_          =       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'''  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # 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()

     A_          =       training_args.get_process_log_level()
     logger.setLevel(_UpperCAmelCase				)
     transformers.utils.logging.set_verbosity(_UpperCAmelCase				)
     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.
     A_          =       None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          A_          =       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.
     A_          =       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.
     A_          =       None if '''validation''' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split  ,						_UpperCAmelCase				) and data_args.train_val_split > 0.0:
          A_          =       ds['''train'''].train_test_split(data_args.train_val_split				)
          A_          =       split['''train''']
          A_          =       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.
     A_          =       {
         '''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:
          A_          =       ViTMAEConfig.from_pretrained(model_args.config_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       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:
          A_          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          A_          =       ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path  ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)  ,						config=_UpperCAmelCase  ,						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'''				)
          A_          =       ViTMAEForPreTraining(_UpperCAmelCase				)

     if training_args.do_train:
          A_          =       ds['''train'''].column_names
     else:
          A_          =       ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          A_          =       data_args.image_column_name
     elif "image" in column_names:
          A_          =       '''image'''
     elif "img" in column_names:
          A_          =       '''img'''
     else:
          A_          =       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:
          A_          =       image_processor.size['''shortest_edge''']
     else:
          A_          =       (image_processor.size['''height'''], image_processor.size['''width'''])
     A_          =       Compose(
         [
             Lambda(lambda _UpperCAmelCase				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCAmelCase  ,						scale=(0.2, 1.0)  ,						interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean  ,						std=image_processor.image_std				),
         ]				)

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

     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:
               A_          =       (
                   ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCAmelCase				)

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

     # Initialize our trainer
     A_          =       Trainer(
         model=_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						train_dataset=ds['''train'''] if training_args.do_train else None  ,						eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,						tokenizer=_UpperCAmelCase  ,						data_collator=_UpperCAmelCase  ,						)

     # Training
     if training_args.do_train:
          A_          =       None
          if training_args.resume_from_checkpoint is not None:
               A_          =       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               A_          =       last_checkpoint
          A_          =       trainer.train(resume_from_checkpoint=_UpperCAmelCase				)
          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:
          A_          =       trainer.evaluate()
          trainer.log_metrics('''eval'''  ,						_UpperCAmelCase				)
          trainer.save_metrics('''eval'''  ,						_UpperCAmelCase				)

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()



