






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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									={}
       if self.train_dir is not None:
             SCREAMING_SNAKE_CASE__     :				List[str]									=self.train_dir
       if self.validation_dir is not None:
             SCREAMING_SNAKE_CASE__     :				Dict									=self.validation_dir
       SCREAMING_SNAKE_CASE__     :				int									=data_files if data_files else None






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






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





def  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






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





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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.
            SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_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''',	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()

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

      # create model
      if model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE__     :				Any									=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'''							)
            SCREAMING_SNAKE_CASE__     :				str									=ViTMAEForPreTraining(UpperCamelCase__							)

      if training_args.do_train:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ds['''train'''].column_names
      else:
            SCREAMING_SNAKE_CASE__     :				Dict									=ds['''validation'''].column_names

      if data_args.image_column_name is not None:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=data_args.image_column_name
      elif "image" in column_names:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									='''image'''
      elif "img" in column_names:
            SCREAMING_SNAKE_CASE__     :				Tuple									='''img'''
      else:
            SCREAMING_SNAKE_CASE__     :				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:
            SCREAMING_SNAKE_CASE__     :				Any									=image_processor.size['''shortest_edge''']
      else:
            SCREAMING_SNAKE_CASE__     :				int									=(image_processor.size['''height'''], image_processor.size['''width'''])
      SCREAMING_SNAKE_CASE__     :				Tuple									=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__				:						Dict							):
            SCREAMING_SNAKE_CASE__     :				int									=[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:
                  SCREAMING_SNAKE_CASE__     :				Tuple									=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:
                  SCREAMING_SNAKE_CASE__     :				Tuple									=(
                      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
      SCREAMING_SNAKE_CASE__     :				str									=(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=training_args.base_learning_rate * total_train_batch_size / 2_5_6

      # Initialize our trainer
      SCREAMING_SNAKE_CASE__     :				List[Any]									=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:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE__     :				str									=training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE__     :				Dict									=last_checkpoint
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=trainer.evaluate()
            trainer.log_metrics('''eval''',	UpperCamelCase__							)
            trainer.save_metrics('''eval''',	UpperCamelCase__							)

      # Write model card and (optionally) push to hub
      SCREAMING_SNAKE_CASE__     :				Dict									={
          '''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  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






      main()


if __name__ == "__main__":
  main()







'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
  raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parser.add_parser('''train'''					,      help='''CLI tool to train a model on a task.'''      )

       train_parser.add_argument(
           '''--train_data'''					,      type=__lowercase					,      required=__lowercase					,      help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''					,      )
       train_parser.add_argument(
           '''--column_label'''					,      type=__lowercase					,      default=0					,      help='''Column of the dataset csv file with example labels.'''      )
       train_parser.add_argument(
           '''--column_text'''					,      type=__lowercase					,      default=1					,      help='''Column of the dataset csv file with example texts.'''      )
       train_parser.add_argument(
           '''--column_id'''					,      type=__lowercase					,      default=2					,      help='''Column of the dataset csv file with example ids.'''      )
       train_parser.add_argument(
           '''--skip_first_row'''					,      action='''store_true'''					,      help='''Skip the first row of the csv file (headers).'''      )

       train_parser.add_argument('''--validation_data'''					,      type=__lowercase					,      default=''''''					,      help='''path to validation dataset.'''      )
       train_parser.add_argument(
           '''--validation_split'''					,      type=__lowercase					,      default=0.1					,      help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''					,      )

       train_parser.add_argument('''--output'''					,      type=__lowercase					,      default='''./'''					,      help='''path to saved the trained model.'''      )

       train_parser.add_argument(
           '''--task'''					,      type=__lowercase					,      default='''text_classification'''					,      help='''Task to train the model on.'''      )
       train_parser.add_argument(
           '''--model'''					,      type=__lowercase					,      default='''bert-base-uncased'''					,      help='''Model\'s name or path to stored model.'''      )
       train_parser.add_argument('''--train_batch_size'''					,      type=__lowercase					,      default=32					,      help='''Batch size for training.'''      )
       train_parser.add_argument('''--valid_batch_size'''					,      type=__lowercase					,      default=64					,      help='''Batch size for validation.'''      )
       train_parser.add_argument('''--learning_rate'''					,      type=__lowercase					,      default=3e-5					,      help='''Learning rate.'''      )
       train_parser.add_argument('''--adam_epsilon'''					,      type=__lowercase					,      default=1e-08					,      help='''Epsilon for Adam optimizer.'''      )
       train_parser.set_defaults(func=__lowercase      )




 def __init__(      self	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

       SCREAMING_SNAKE_CASE__     :				str									=args.column_label
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.column_text
       SCREAMING_SNAKE_CASE__     :				Tuple									=args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
       if args.task == "text_classification":
             SCREAMING_SNAKE_CASE__     :				List[str]									=TextClassificationPipeline.from_pretrained(args.model      )
       elif args.task == "token_classification":
             raise NotImplementedError
       elif args.task == "question_answering":
             raise NotImplementedError

       self.logger.info(F"Loading dataset from {args.train_data}"      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Processor.create_from_csv(
           args.train_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
             SCREAMING_SNAKE_CASE__     :				List[Any]									=Processor.create_from_csv(
                 args.validation_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=args.validation_split
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.train_batch_size
       SCREAMING_SNAKE_CASE__     :				Any									=args.valid_batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=args.learning_rate
       SCREAMING_SNAKE_CASE__     :				int									=args.adam_epsilon




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       self.pipeline.fit(
           self.train_dataset					,      validation_data=self.valid_dataset					,      validation_split=self.validation_split					,      learning_rate=self.learning_rate					,      adam_epsilon=self.adam_epsilon					,      train_batch_size=self.train_batch_size					,      valid_batch_size=self.valid_batch_size					,      )

       # Save trained pipeline
       self.pipeline.save_pretrained(self.output      )