import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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''')

UpperCAmelCase_			    =							list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_			    =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class  __magic_name__  :





   """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. If not set, will try to use \'image\' or \'img\'.'''}      ,	)
   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=0.1_5      ,	metadata={'''help''': '''Percent to split off of train for validation.'''}    )
   lowerCAmelCase      :    int  			=   field(default=3_2      ,	metadata={'''help''': '''The size of the square patches to use for masking.'''}    )
   lowerCAmelCase      :    float  			=   field(
       default=0.6      ,	metadata={'''help''': '''Percentage of patches to mask.'''}      ,	)
   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   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= {}
    if self.train_dir is not None:
     _UpperCamelCase:     Tuple							= self.train_dir
    if self.validation_dir is not None:
     _UpperCamelCase:     Dict							= self.validation_dir
    _UpperCamelCase:     Any							= data_files if data_files else None




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    str  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
               '''checkpoint identifier on the hub. '''
               '''Don\'t set if you want to train a model from scratch.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[str]  			=   field(
       default=__a      ,	metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__a    )}      ,	)
   lowerCAmelCase      :    Optional[str]  			=   field(
       default=__a      ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
   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 (cache) the pretrained models/datasets downloaded from the hub'''}      ,	)
   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      :    Optional[int]  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   field(
       default=__a      ,	metadata={'''help''': '''Stride to use for the encoder.'''}      ,	)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  str=192		,  _lowercase					:  Optional[Any]=32		,  _lowercase					:  str=4		,  _lowercase					:  Union[str, Any]=0.6							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= input_size
    _UpperCamelCase:     int							= mask_patch_size
    _UpperCamelCase:     Tuple							= model_patch_size
    _UpperCamelCase:     List[str]							= mask_ratio

    if self.input_size % self.mask_patch_size != 0:
     raise ValueError('''Input size must be divisible by mask patch size'''							)
    if self.mask_patch_size % self.model_patch_size != 0:
     raise ValueError('''Mask patch size must be divisible by model patch size'''							)

    _UpperCamelCase:     str							= self.input_size // self.mask_patch_size
    _UpperCamelCase:     Optional[Any]							= self.mask_patch_size // self.model_patch_size

    _UpperCamelCase:     Dict							= self.rand_size**2
    _UpperCamelCase:     int							= int(np.ceil(self.token_count * self.mask_ratio							)							)



   def __call__(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= np.random.permutation(self.token_count							)[: self.mask_count]
    _UpperCamelCase:     int							= np.zeros(self.token_count		,  dtype=_lowercase							)
    _UpperCamelCase:     Optional[Any]							= 1

    _UpperCamelCase:     List[Any]							= mask.reshape((self.rand_size, self.rand_size)							)
    _UpperCamelCase:     Tuple							= mask.repeat(self.scale		,  axis=0							).repeat(self.scale		,  axis=1							)

    return torch.tensor(mask.flatten()							)




def       lowerCAmelCase_     (		lowercase:							Union[str, Any]		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.stack([example['''pixel_values'''] for example in examples]		)
 _UpperCamelCase:     List[str]							= torch.stack([example['''mask'''] for example in examples]		)
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def       lowerCAmelCase_     (		)    ->				Union[str, Any]:







 '''simple docstring'''
 # 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:     Any							= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
 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:     List[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_mim'''  ,					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()

 _UpperCamelCase:     Union[str, Any]							= 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.
 _UpperCamelCase:     Any							= None
 if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
  _UpperCamelCase:     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.
 _UpperCamelCase:     Union[str, 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.
 _UpperCamelCase:     Union[str, Any]							= 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:
  _UpperCamelCase:     Any							= ds['''train'''].train_test_split(data_args.train_val_split		)
  _UpperCamelCase:     Dict							= split['''train''']
  _UpperCamelCase:     Any							= split['''test''']

 # Create config
 # 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_or_path:
  _UpperCamelCase:     Tuple							= AutoConfig.from_pretrained(model_args.config_name_or_path  ,					**lowercase		)
 elif model_args.model_name_or_path:
  _UpperCamelCase:     str							= AutoConfig.from_pretrained(model_args.model_name_or_path  ,					**lowercase		)
 else:
  _UpperCamelCase:     Union[str, Any]							= CONFIG_MAPPING[model_args.model_type]()
  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}"""		)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
 if hasattr(lowercase  ,					'''decoder_type'''		):
  _UpperCamelCase:     str							= '''simmim'''

 # adapt config
 _UpperCamelCase:     Tuple							= model_args.image_size if model_args.image_size is not None else config.image_size
 _UpperCamelCase:     Union[str, Any]							= model_args.patch_size if model_args.patch_size is not None else config.patch_size
 _UpperCamelCase:     List[str]							= (
     model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
 )

 config.update(
     {
         '''image_size''': model_args.image_size,
         '''patch_size''': model_args.patch_size,
         '''encoder_stride''': model_args.encoder_stride,
     }		)

 # create image processor
 if model_args.image_processor_name:
  _UpperCamelCase:     Dict							= AutoImageProcessor.from_pretrained(model_args.image_processor_name  ,					**lowercase		)
 elif model_args.model_name_or_path:
  _UpperCamelCase:     int							= AutoImageProcessor.from_pretrained(model_args.model_name_or_path  ,					**lowercase		)
 else:
  _UpperCamelCase:     Dict							= {
      conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
  }
  _UpperCamelCase:     Tuple							= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

 # create model
 if model_args.model_name_or_path:
  _UpperCamelCase:     Optional[int]							= AutoModelForMaskedImageModeling.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'''		)
  _UpperCamelCase:     Any							= AutoModelForMaskedImageModeling.from_config(lowercase		)

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

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

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 _UpperCamelCase:     Dict							= Compose(
     [
         Lambda(lambda lowercase		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
         RandomResizedCrop(model_args.image_size  ,					scale=(0.67, 1.0)  ,					ratio=(3.0 / 4.0, 4.0 / 3.0)		),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean  ,					std=image_processor.image_std		),
     ]		)

 # create mask generator
 _UpperCamelCase:     Tuple							= MaskGenerator(
     input_size=model_args.image_size  ,					mask_patch_size=data_args.mask_patch_size  ,					model_patch_size=model_args.patch_size  ,					mask_ratio=data_args.mask_ratio  ,					)

 def preprocess_images(lowercase:							Dict		):
  _UpperCamelCase:     Optional[Any]							= [transforms(lowercase		) for image in examples[image_column_name]]
  _UpperCamelCase:     Optional[int]							= [mask_generator() for i in range(len(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:     Tuple							= 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:
   _UpperCamelCase:     Union[str, Any]							= (
       ds['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
   )
  # Set the validation transforms
  ds["validation"].set_transform(lowercase		)

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

 # Write model card and (optionally) push to hub
 _UpperCamelCase:     Union[str, Any]							= {
     '''finetuned_from''': model_args.model_name_or_path,
     '''tasks''': '''masked-image-modeling''',
     '''dataset''': data_args.dataset_name,
     '''tags''': ['''masked-image-modeling'''],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**lowercase		)
 else:
  trainer.create_model_card(**lowercase		)


if __name__ == "__main__":
  main()
import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  int=None		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
        '''instead.'''		,  _lowercase		,  )
    super().__init__(args=_lowercase		,  **_lowercase							)