




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=__lowerCamelCase ,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default=__lowerCamelCase ,     metadata={'help': 'The column name of the images in the files.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default=__lowerCamelCase ,     metadata={'help': 'A folder containing the training data.'})
      UpperCAmelCase__      :	Optional[str]    		=   field(default=__lowerCamelCase ,     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=__lowerCamelCase ,     metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          } ,     )
      UpperCAmelCase__      :	Optional[int]    		=   field(
          default=__lowerCamelCase ,     metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                  'value if set.'
              )
          } ,     )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						{}
             if self.train_dir is not None:
                    __lowerCamelCase   =						self.train_dir
             if self.validation_dir is not None:
                    __lowerCamelCase   =						self.validation_dir
             __lowerCamelCase   =						data_files if data_files else None





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	str    		=   field(
          default=__lowerCamelCase ,     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=__lowerCamelCase ,     metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'})
      UpperCAmelCase__      :	Optional[str]    		=   field(
          default=__lowerCamelCase ,     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=__lowerCamelCase ,     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=__lowerCamelCase ,     metadata={'help': 'Name or path of preprocessor config.'})
      UpperCAmelCase__      :	bool    		=   field(
          default=__lowerCamelCase ,     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=__lowerCamelCase ,     metadata={'help': 'Whether or not to train with normalized pixel values as target.'})





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




def 					lowerCamelCase__       (						A__       :					List[str]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						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.
              __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
       else:
              __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						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"""  ,			A__  ,			A__	)

       # 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()

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

       # create model
       if model_args.model_name_or_path:
              __lowerCamelCase   =						ViTMAEForPreTraining.from_pretrained(
                  model_args.model_name_or_path  ,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)  ,			config=A__  ,			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"""	)
              __lowerCamelCase   =						ViTMAEForPreTraining(A__	)

       if training_args.do_train:
              __lowerCamelCase   =						ds["""train"""].column_names
       else:
              __lowerCamelCase   =						ds["""validation"""].column_names

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

       def preprocess_images(A__       :					Optional[int]	):
              __lowerCamelCase   =						[transforms(A__	) 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:
                     __lowerCamelCase   =						ds["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
              # Set the training transforms
              ds["train"].set_transform(A__	)

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

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

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

       # Training
       if training_args.do_train:
              __lowerCamelCase   =						None
              if training_args.resume_from_checkpoint is not None:
                     __lowerCamelCase   =						training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __lowerCamelCase   =						last_checkpoint
              __lowerCamelCase   =						trainer.train(resume_from_checkpoint=A__	)
              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:
              __lowerCamelCase   =						trainer.evaluate()
              trainer.log_metrics("""eval"""  ,			A__	)
              trainer.save_metrics("""eval"""  ,			A__	)

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




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       main()


if __name__ == "__main__":
       main()







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizer
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizerFast
      UpperCAmelCase__      :	Tuple    		=   True
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,       UpperCamelCase_   )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]

