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


        '''simple docstring'''


        lowercase_			:       List[str]									=			{}
        if self.train_dir is not None:
             lowercase_			:       Union[str, Any]									=			self.train_dir
        if self.validation_dir is not None:
             lowercase_			:       Union[str, Any]									=			self.validation_dir
        lowercase_			:       List[Any]									=			data_files if data_files else None







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







@dataclass
class  UpperCamelCase	(    lowercase_						):
   lowercase							=						field(
       default=1e-3		,   metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}						)






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       Dict									=			torch.stack([example['pixel_values'] for example in examples]   )
     return {"pixel_values": pixel_values}



def     lowercase__( ):
     # 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.

     lowercase_			:       Tuple									=			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.
          lowercase_							,						lowercase_							,						lowercase_			:       Union[str, Any]									=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
     else:
          lowercase_							,						lowercase_							,						lowercase_			:       Optional[Any]									=			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'  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_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()

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

     # create model
     if model_args.model_name_or_path:
          lowercase_			:       Any									=			ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path  ,    from_tf=bool('.ckpt' in model_args.model_name_or_path   )  ,    config=__SCREAMING_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'   )
          lowercase_			:       int									=			ViTMAEForPreTraining(__SCREAMING_SNAKE_CASE   )

     if training_args.do_train:
          lowercase_			:       Any									=			ds['train'].column_names
     else:
          lowercase_			:       Dict									=			ds['validation'].column_names

     if data_args.image_column_name is not None:
          lowercase_			:       List[Any]									=			data_args.image_column_name
     elif "image" in column_names:
          lowercase_			:       List[Any]									=			'image'
     elif "img" in column_names:
          lowercase_			:       Optional[Any]									=			'img'
     else:
          lowercase_			:       int									=			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:
          lowercase_			:       Dict									=			image_processor.size['shortest_edge']
     else:
          lowercase_			:       Dict									=			(image_processor.size['height'], image_processor.size['width'])
     lowercase_			:       Any									=			Compose(
         [
             Lambda(lambda __SCREAMING_SNAKE_CASE   : img.convert('RGB'   ) if img.mode != "RGB" else img   ),
             RandomResizedCrop(__SCREAMING_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(__SCREAMING_SNAKE_CASE							:  Dict   ):
          lowercase_			:       int									=			[transforms(__SCREAMING_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:
               lowercase_			:       List[Any]									=			ds['train'].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
          # Set the training transforms
          ds["train"].set_transform(__SCREAMING_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:
               lowercase_			:       Dict									=			(
                   ds['validation'].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
               )
          # Set the validation transforms
          ds["validation"].set_transform(__SCREAMING_SNAKE_CASE   )

     # Compute absolute learning rate
     lowercase_			:       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:
          lowercase_			:       List[Any]									=			training_args.base_learning_rate * total_train_batch_size / 2_56

     # Initialize our trainer
     lowercase_			:       List[str]									=			Trainer(
         model=__SCREAMING_SNAKE_CASE  ,    args=__SCREAMING_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=__SCREAMING_SNAKE_CASE  ,    data_collator=__SCREAMING_SNAKE_CASE  ,    )

     # Training
     if training_args.do_train:
          lowercase_			:       Tuple									=			None
          if training_args.resume_from_checkpoint is not None:
               lowercase_			:       str									=			training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               lowercase_			:       str									=			last_checkpoint
          lowercase_			:       Tuple									=			trainer.train(resume_from_checkpoint=__SCREAMING_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:
          lowercase_			:       Tuple									=			trainer.evaluate()
          trainer.log_metrics('eval'  ,    __SCREAMING_SNAKE_CASE   )
          trainer.save_metrics('eval'  ,    __SCREAMING_SNAKE_CASE   )

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()


"""simple docstring"""







from argparse import ArgumentParser

from .env import EnvironmentCommand



def     lowercase__( ):
     lowercase_			:       List[Any]									=			ArgumentParser('Diffusers CLI tool'  ,    usage='diffusers-cli <command> [<args>]'   )
     lowercase_			:       int									=			parser.add_subparsers(help='diffusers-cli command helpers'   )

     # Register commands
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE   )

     # Let's go
     lowercase_			:       str									=			parser.parse_args()

     if not hasattr(__SCREAMING_SNAKE_CASE  ,    'func'   ):
          parser.print_help()
          exit(1   )

     # Run
     lowercase_			:       Optional[Any]									=			args.func(__SCREAMING_SNAKE_CASE   )
     service.run()


if __name__ == "__main__":
       main()

