




'''simple docstring'''







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
lowercase      :   List[str]      =       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 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""cifar10""",		metadata={"""help""": """Name of a dataset from the datasets package"""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default=a__,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default=a__,		metadata={"""help""": """The column name of the images in the files."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(default=a__,		metadata={"""help""": """A folder containing the training data."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(default=a__,		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=a__,		metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         },		)
     lowerCAmelCase_  :     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							)      ->				List[str]:
          snake_case			:      Any        =			{}
          if self.train_dir is not None:
               snake_case			:      Optional[int]        =			self.train_dir
          if self.validation_dir is not None:
               snake_case			:      Union[str, Any]        =			self.validation_dir
          snake_case			:      Optional[int]        =			data_files if data_files else None







@dataclass
class 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					field(
         default=a__,		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=a__,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}	)
     lowerCAmelCase_  :     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"""
             )
         },		)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default=a__,		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=a__,		metadata={"""help""": """Name or path of preprocessor config."""}	)
     lowerCAmelCase_  :     bool          =					field(
         default=a__,		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=a__,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}	)







@dataclass
class 					_a (a__	):





     '''simple docstring'''



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



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Optional[int]        =			torch.stack([example["""pixel_values"""] for example in examples]				)
     return {"pixel_values": pixel_values}



def        lowerCamelCase__				(      ):
     # 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.

     snake_case			:      List[str]        =			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.
          snake_case ,		snake_case ,		snake_case			:      Tuple        =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          snake_case ,		snake_case ,		snake_case			:      Optional[int]        =			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()

     snake_case			:      str        =			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.
     snake_case			:      Optional[int]        =			None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          snake_case			:      List[str]        =			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.
     snake_case			:      List[Any]        =			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.
     snake_case			:      List[str]        =			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:
          snake_case			:      Dict        =			ds["""train"""].train_test_split(data_args.train_val_split				)
          snake_case			:      Union[str, Any]        =			split["""train"""]
          snake_case			:      Union[str, Any]        =			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.
     snake_case			:      Dict        =			{
         """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:
          snake_case			:      Any        =			ViTMAEConfig.from_pretrained(model_args.config_name     ,   **__lowercase				)
     elif model_args.model_name_or_path:
          snake_case			:      Tuple        =			ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,   **__lowercase				)
     else:
          snake_case			:      Tuple        =			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:
          snake_case			:      Optional[Any]        =			ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,   **__lowercase				)
     elif model_args.model_name_or_path:
          snake_case			:      Dict        =			ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,   **__lowercase				)
     else:
          snake_case			:      List[Any]        =			ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          snake_case			:      str        =			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"""				)
          snake_case			:      Optional[Any]        =			ViTMAEForPreTraining(__lowercase				)

     if training_args.do_train:
          snake_case			:      Optional[Any]        =			ds["""train"""].column_names
     else:
          snake_case			:      Any        =			ds["""validation"""].column_names

     if data_args.image_column_name is not None:
          snake_case			:      List[str]        =			data_args.image_column_name
     elif "image" in column_names:
          snake_case			:      Optional[int]        =			"""image"""
     elif "img" in column_names:
          snake_case			:      int        =			"""img"""
     else:
          snake_case			:      Tuple        =			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:
          snake_case			:      List[Any]        =			image_processor.size["""shortest_edge"""]
     else:
          snake_case			:      Optional[Any]        =			(image_processor.size["""height"""], image_processor.size["""width"""])
     snake_case			:      Tuple        =			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				):
          snake_case			:      Dict        =			[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:
               snake_case			:      Any        =			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:
               snake_case			:      Optional[int]        =			(
                   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
     snake_case			:      Union[str, Any]        =			(
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          snake_case			:      Dict        =			training_args.base_learning_rate * total_train_batch_size / 256

     # Initialize our trainer
     snake_case			:      str        =			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:
          snake_case			:      str        =			None
          if training_args.resume_from_checkpoint is not None:
               snake_case			:      str        =			training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               snake_case			:      Optional[int]        =			last_checkpoint
          snake_case			:      Tuple        =			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:
          snake_case			:      Optional[int]        =			trainer.evaluate()
          trainer.log_metrics("""eval"""     ,   __lowercase				)
          trainer.save_metrics("""eval"""     ,   __lowercase				)

     # Write model card and (optionally) push to hub
     snake_case			:      Tuple        =			{
         """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				):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
      main()









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Optional[Any]      =       {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Any          =					"""pegasus"""
     lowerCAmelCase_  :     Dict          =					["""past_key_values"""]
     lowerCAmelCase_  :     str          =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







     def __init__(				self							,__a=50_265							,__a=1_024							,__a=12							,__a=4_096							,__a=16							,__a=12							,__a=4_096							,__a=16							,__a=0.0							,__a=0.0							,__a=True							,__a=True							,__a="gelu"							,__a=1_024							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=0							,__a=False							,__a=0							,__a=1							,__a=1							,**__a							,)      ->				int:
          snake_case			:      List[Any]        =			vocab_size
          snake_case			:      Optional[Any]        =			max_position_embeddings
          snake_case			:      List[Any]        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      List[Any]        =			encoder_layers
          snake_case			:      Optional[Any]        =			encoder_attention_heads
          snake_case			:      Union[str, Any]        =			decoder_ffn_dim
          snake_case			:      Tuple        =			decoder_layers
          snake_case			:      Union[str, Any]        =			decoder_attention_heads
          snake_case			:      Union[str, Any]        =			dropout
          snake_case			:      int        =			attention_dropout
          snake_case			:      int        =			activation_dropout
          snake_case			:      Optional[Any]        =			activation_function
          snake_case			:      Tuple        =			init_std
          snake_case			:      Union[str, Any]        =			encoder_layerdrop
          snake_case			:      int        =			decoder_layerdrop
          snake_case			:      Dict        =			use_cache
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=__a							,eos_token_id=__a							,is_encoder_decoder=__a							,decoder_start_token_id=__a							,forced_eos_token_id=__a							,**__a							,)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model



