



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
__UpperCAmelCase			  =			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_						:
       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    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    str		      =      {}
          if self.train_dir is not None:
             UpperCamelCase       :    Optional[Any]		      =      self.train_dir
          if self.validation_dir is not None:
             UpperCamelCase       :    List[str]		      =      self.validation_dir
          UpperCamelCase       :    List[str]		      =      data_files if data_files else None






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






def 						UpperCamelCase      (  snake_case__			:       int      )      ->	Any:
   UpperCamelCase       :    List[Any]		      =      torch.stack([example['pixel_values'] for example in examples]      )
   return {"pixel_values": pixel_values}
def 						UpperCamelCase      (  )      ->	str:
   # 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       :    Dict		      =      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       :    str		      =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
   else:
      UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry('run_mae'			,	snake_case__			,	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       :    int		      =      training_args.get_process_log_level()
   logger.setLevel(snake_case__      )
   transformers.utils.logging.set_verbosity(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       :    List[Any]		      =      None
   if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
      UpperCamelCase       :    Optional[int]		      =      get_last_checkpoint(training_args.output_dir      )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             'Use --overwrite_output_dir to overcome.'      )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'      )

    # Initialize our dataset.
   UpperCamelCase       :    str		      =      load_dataset(
       data_args.dataset_name			,	data_args.dataset_config_name			,	data_files=data_args.data_files			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)

   # If we don't have a validation split, split off a percentage of train as validation.
   UpperCamelCase       :    Dict		      =      None if 'validation' in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split			,	snake_case__      ) and data_args.train_val_split > 0.0:
      UpperCamelCase       :    List[Any]		      =      ds['train'].train_test_split(data_args.train_val_split      )
      UpperCamelCase       :    Optional[int]		      =      split['train']
      UpperCamelCase       :    List[str]		      =      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       :    Tuple		      =      {
       '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       :    int		      =      ViTMAEConfig.from_pretrained(model_args.config_name			,	**snake_case__      )
   elif model_args.model_name_or_path:
      UpperCamelCase       :    Dict		      =      ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,	**snake_case__      )
   else:
      UpperCamelCase       :    str		      =      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       :    Tuple		      =      ViTImageProcessor.from_pretrained(model_args.image_processor_name			,	**snake_case__      )
   elif model_args.model_name_or_path:
      UpperCamelCase       :    int		      =      ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,	**snake_case__      )
   else:
      UpperCamelCase       :    List[Any]		      =      ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      UpperCamelCase       :    Tuple		      =      ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path			,	from_tf=bool('.ckpt' in model_args.model_name_or_path      )			,	config=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       :    List[str]		      =      ViTMAEForPreTraining(snake_case__      )

   if training_args.do_train:
      UpperCamelCase       :    Tuple		      =      ds['train'].column_names
   else:
      UpperCamelCase       :    Dict		      =      ds['validation'].column_names

   if data_args.image_column_name is not None:
      UpperCamelCase       :    Any		      =      data_args.image_column_name
   elif "image" in column_names:
      UpperCamelCase       :    Dict		      =      'image'
   elif "img" in column_names:
      UpperCamelCase       :    Optional[int]		      =      'img'
   else:
      UpperCamelCase       :    str		      =      column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      UpperCamelCase       :    Optional[int]		      =      image_processor.size['shortest_edge']
   else:
      UpperCamelCase       :    Optional[Any]		      =      (image_processor.size['height'], image_processor.size['width'])
   UpperCamelCase       :    List[Any]		      =      Compose(
       [
           Lambda(lambda snake_case__      : img.convert('RGB'      ) if img.mode != "RGB" else img      ),
           RandomResizedCrop(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(snake_case__			:       List[Any]      ):
      UpperCamelCase       :    Dict		      =      [transforms(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       :    List[Any]		      =      ds['train'].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
      # Set the training transforms
      ds["train"].set_transform(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       :    str		      =      (
             ds['validation'].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
         )
      # Set the validation transforms
      ds["validation"].set_transform(snake_case__      )

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

   # Initialize our trainer
   UpperCamelCase       :    List[Any]		      =      Trainer(
       model=snake_case__			,	args=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=snake_case__			,	data_collator=snake_case__			,	)

   # Training
   if training_args.do_train:
      UpperCamelCase       :    Optional[Any]		      =      None
      if training_args.resume_from_checkpoint is not None:
         UpperCamelCase       :    Dict		      =      training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         UpperCamelCase       :    Optional[int]		      =      last_checkpoint
      UpperCamelCase       :    Tuple		      =      trainer.train(resume_from_checkpoint=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       :    Dict		      =      trainer.evaluate()
      trainer.log_metrics('eval'			,	snake_case__      )
      trainer.save_metrics('eval'			,	snake_case__      )

   # Write model card and (optionally) push to hub
   UpperCamelCase       :    Optional[Any]		      =      {
       'tasks': 'masked-auto-encoding',
       'dataset': data_args.dataset_name,
       'tags': ['masked-auto-encoding'],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**snake_case__      )
   else:
      trainer.create_model_card(**snake_case__      )
def 						UpperCamelCase      (  snake_case__			:       int      )      ->	Optional[int]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    'table-transformer'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Union[str, Any]  					=    {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    Any=1_0_0			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[Any]=2_0_4_8			,						lowerCamelCase__	:    List[str]=8			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[str]=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=8			,						lowerCamelCase__	:    Union[str, Any]=0.0			,						lowerCamelCase__	:    Any=0.0			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]="relu"			,						lowerCamelCase__	:    Optional[int]=2_5_6			,						lowerCamelCase__	:    List[str]=0.1			,						lowerCamelCase__	:    str=0.0			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Dict=0.02			,						lowerCamelCase__	:    Union[str, Any]=1.0			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[int]="sine"			,						lowerCamelCase__	:    str="resnet50"			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    Any=1			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    List[str]=1			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Tuple=2			,						lowerCamelCase__	:    List[str]=0.1			,						**lowerCamelCase__	:    List[str]			,						) ->			Dict:
         """simple docstring"""





         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
                     A_							       =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
               elif isinstance(lowerCamelCase__			,						lowerCamelCase__       ):
                     A_							       =		backbone_config.get('''model_type'''       )
                     A_							       =		CONFIG_MAPPING[backbone_model_type]
                     A_							       =		config_class.from_dict(lowerCamelCase__       )
               # set timm attributes to None
               A_       ,A_       ,A_							       =		None, None, None

         A_							       =		use_timm_backbone
         A_							       =		backbone_config
         A_							       =		num_channels
         A_							       =		num_queries
         A_							       =		d_model
         A_							       =		encoder_ffn_dim
         A_							       =		encoder_layers
         A_							       =		encoder_attention_heads
         A_							       =		decoder_ffn_dim
         A_							       =		decoder_layers
         A_							       =		decoder_attention_heads
         A_							       =		dropout
         A_							       =		attention_dropout
         A_							       =		activation_dropout
         A_							       =		activation_function
         A_							       =		init_std
         A_							       =		init_xavier_std
         A_							       =		encoder_layerdrop
         A_							       =		decoder_layerdrop
         A_							       =		encoder_layers
         A_							       =		auxiliary_loss
         A_							       =		position_embedding_type
         A_							       =		backbone
         A_							       =		use_pretrained_backbone
         A_							       =		dilation
         # Hungarian matcher
         A_							       =		class_cost
         A_							       =		bbox_cost
         A_							       =		giou_cost
         # Loss coefficients
         A_							       =		mask_loss_coefficient
         A_							       =		dice_loss_coefficient
         A_							       =		bbox_loss_coefficient
         A_							       =		giou_loss_coefficient
         A_							       =		eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase__			,						**lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         return self.encoder_attention_heads





   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			int:
         """simple docstring"""





         return self.d_model



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    version.parse('1.11'			)

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-5





   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         return 1_2





