
'''simple docstring'''
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
A__   :		Optional[Any] 		=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')

A__   :		Tuple 		=list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A__   :		Optional[Any] 		=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class  __A				:
 lowerCamelCase				       =field(
     default='''cifar10'''    ,						metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}    ,						)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''A folder containing the training data.'''}			)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''A folder containing the validation data.'''}			)
 lowerCamelCase				       =field(
     default=0.15    ,						metadata={'''help''': '''Percent to split off of train for validation.'''}			)
 lowerCamelCase				       =field(default=32    ,						metadata={'''help''': '''The size of the square patches to use for masking.'''}			)
 lowerCamelCase				       =field(
     default=0.6    ,						metadata={'''help''': '''Percentage of patches to mask.'''}    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }    ,						)
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     int												=		{}
   if self.train_dir is not None:
     __A		:     List[Any]												=		self.train_dir
   if self.validation_dir is not None:
     __A		:     Optional[Any]												=		self.validation_dir
   __A		:     Tuple												=		data_files if data_files else None







@dataclass
class  __A				:
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						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				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE			)}    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						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				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}    ,						)
 lowerCamelCase				       =field(
     default='''main'''    ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}    ,						)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Name or path of preprocessor config.'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={
         '''help''': (
             '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
         )
     }    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={
         '''help''': (
             '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
         )
     }    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Stride to use for the encoder.'''}    ,						)







class  __A				:
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    Tuple=1_92				,   lowerCamelCase			:    str=32				,   lowerCamelCase			:    Any=4				,   lowerCamelCase			:    int=0.6       ):



   """simple docstring"""





   __A		:     Dict												=		input_size
   __A		:     List[str]												=		mask_patch_size
   __A		:     List[Any]												=		model_patch_size
   __A		:     List[Any]												=		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"""       )

   __A		:     Optional[Any]												=		self.input_size // self.mask_patch_size
   __A		:     Tuple												=		self.mask_patch_size // self.model_patch_size

   __A		:     str												=		self.rand_size**2
   __A		:     Any												=		int(np.ceil(self.token_count * self.mask_ratio       )       )






 def __call__(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		np.random.permutation(self.token_count       )[: self.mask_count]
   __A		:     List[Any]												=		np.zeros(self.token_count				,   dtype=lowerCamelCase       )
   __A		:     int												=		1

   __A		:     List[Any]												=		mask.reshape((self.rand_size, self.rand_size)       )
   __A		:     Union[str, Any]												=		mask.repeat(self.scale				,   axis=0       ).repeat(self.scale				,   axis=1       )

   return torch.tensor(mask.flatten()       )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str] )				->       Optional[int]:





  """simple docstring"""

  __A		:     Tuple												=		torch.stack([example["""pixel_values"""] for example in examples] )
  __A		:     Optional[Any]												=		torch.stack([example["""mask"""] for example in examples] )
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def        A_     (						)				->       Any:





  """simple docstring"""

  __A		:     Tuple												=		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.
    __A		:     str												=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    __A		:     List[str]												=		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"""			,     __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()

  __A		:     Optional[Any]												=		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.
  __A		:     Tuple												=		None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    __A		:     int												=		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.
  __A		:     int												=		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.
  __A		:     Dict												=		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:
    __A		:     Union[str, Any]												=		ds["""train"""].train_test_split(data_args.train_val_split )
    __A		:     List[str]												=		split["""train"""]
    __A		:     List[Any]												=		split["""test"""]

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  __A		:     List[str]												=		{
      """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:
    __A		:     Optional[Any]												=		AutoConfig.from_pretrained(model_args.config_name_or_path			,     **__SCREAMING_SNAKE_CASE )
  elif model_args.model_name_or_path:
    __A		:     Tuple												=		AutoConfig.from_pretrained(model_args.model_name_or_path			,     **__SCREAMING_SNAKE_CASE )
  else:
    __A		:     List[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(__SCREAMING_SNAKE_CASE			,     """decoder_type""" ):
    __A		:     Any												=		"""simmim"""

  # adapt config
  __A		:     Any												=		model_args.image_size if model_args.image_size is not None else config.image_size
  __A		:     Any												=		model_args.patch_size if model_args.patch_size is not None else config.patch_size
  __A		:     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:
    __A		:     Tuple												=		AutoImageProcessor.from_pretrained(model_args.image_processor_name			,     **__SCREAMING_SNAKE_CASE )
  elif model_args.model_name_or_path:
    __A		:     Dict												=		AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,     **__SCREAMING_SNAKE_CASE )
  else:
    __A		:     int												=		{
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    __A		:     Union[str, Any]												=		IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    __A		:     Tuple												=		AutoModelForMaskedImageModeling.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""" )
    __A		:     List[str]												=		AutoModelForMaskedImageModeling.from_config(__SCREAMING_SNAKE_CASE )

  if training_args.do_train:
    __A		:     str												=		ds["""train"""].column_names
  else:
    __A		:     List[Any]												=		ds["""validation"""].column_names

  if data_args.image_column_name is not None:
    __A		:     Optional[Any]												=		data_args.image_column_name
  elif "image" in column_names:
    __A		:     Optional[int]												=		"""image"""
  elif "img" in column_names:
    __A		:     Optional[int]												=		"""img"""
  else:
    __A		:     List[Any]												=		column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  __A		:     Optional[int]												=		Compose(
      [
          Lambda(lambda __SCREAMING_SNAKE_CASE : 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
  __A		:     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(__SCREAMING_SNAKE_CASE						:  Optional[Any] ):
    __A		:     Union[str, Any]												=		[transforms(__SCREAMING_SNAKE_CASE ) for image in examples[image_column_name]]
    __A		:     Tuple												=		[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:
      __A		:     List[str]												=		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:
      __A		:     str												=		(
          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 )

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

  # Write model card and (optionally) push to hub
  __A		:     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(**__SCREAMING_SNAKE_CASE )
  else:
    trainer.create_model_card(**__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     main()





'''simple docstring'''
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

    for i in range(__SCREAMING_SNAKE_CASE ):
      __A		:     int												=		requests.get(url + F"&page={i + 2}"			,     headers=__SCREAMING_SNAKE_CASE ).json()
      job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )

    return job_time
  except Exception:
    print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )

  return {}


if __name__ == "__main__":
     A__   :		Any 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
     A__   :		Any 		=parser.parse_args()

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"{k}: {v['duration']}")



