


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_		:

 '''simple docstring'''

 __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 		UpperCamelCase__  (			self      ):
       snake_case_ =   {}
       if self.train_dir is not None:
             snake_case_ =   self.train_dir
       if self.validation_dir is not None:
             snake_case_ =   self.validation_dir
       snake_case_ =   data_files if data_files else None







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

 __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 lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						field(
     default=1E-3      ,		metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."} )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

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






def  __lowerCAmelCase					()->						str:




      """simple docstring"""

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

      snake_case_ =   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.
      snake_case_ =   None
      if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
            snake_case_ =   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.
      snake_case_ =   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.
      snake_case_ =   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:
            snake_case_ =   ds['''train'''].train_test_split(data_args.train_val_split      )
            snake_case_ =   split['''train''']
            snake_case_ =   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.
      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,
      }
      if model_args.config_name:
            snake_case_ =   ViTMAEConfig.from_pretrained(model_args.config_name      ,      **SCREAMING_SNAKE_CASE      )
      elif model_args.model_name_or_path:
            snake_case_ =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,      **SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   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:
            snake_case_ =   ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,      **SCREAMING_SNAKE_CASE      )
      elif model_args.model_name_or_path:
            snake_case_ =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,      **SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            snake_case_ =   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'''      )
            snake_case_ =   ViTMAEForPreTraining(SCREAMING_SNAKE_CASE      )

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

      if data_args.image_column_name is not None:
            snake_case_ =   data_args.image_column_name
      elif "image" in column_names:
            snake_case_ =   '''image'''
      elif "img" in column_names:
            snake_case_ =   '''img'''
      else:
            snake_case_ =   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:
            snake_case_ =   image_processor.size['''shortest_edge''']
      else:
            snake_case_ =   (image_processor.size['''height'''], image_processor.size['''width'''])
      snake_case_ =   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      ):
            snake_case_ =   [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:
                  snake_case_ =   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:
                  snake_case_ =   (
                      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
      snake_case_ =   (
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            snake_case_ =   training_args.base_learning_rate * total_train_batch_size / 256

      # Initialize our trainer
      snake_case_ =   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:
            snake_case_ =   None
            if training_args.resume_from_checkpoint is not None:
                  snake_case_ =   training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  snake_case_ =   last_checkpoint
            snake_case_ =   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:
            snake_case_ =   trainer.evaluate()
            trainer.log_metrics('''eval'''      ,      SCREAMING_SNAKE_CASE      )
            trainer.save_metrics('''eval'''      ,      SCREAMING_SNAKE_CASE      )

      # Write model card and (optionally) push to hub
      snake_case_ =   {
          '''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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      main()


if __name__ == "__main__":
   main()



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase    =					{"""UserAgent""": UserAgent().random}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						dict:




      """simple docstring"""

      snake_case_ =   script.contents[0]
      snake_case_ =   json.loads(data[data.find('''{"config"'''      ) : -1]      )
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   F'''https://www.instagram.com/{username}/'''
       snake_case_ =   self.get_json()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   requests.get(self.url	,   headers=_UpperCAmelCase      ).text
       snake_case_ =   BeautifulSoup(_UpperCAmelCase	,   '''html.parser'''      ).find_all('''script'''      )
       try:
             return extract_user_profile(scripts[4]      )
       except (json.decoder.JSONDecodeError, KeyError):
             return extract_user_profile(scripts[3]      )



 def __repr__(			self      ):
       return F'''{self.__class__.__name__}(\'{self.username}\')'''



 def __str__(			self      ):
       return F'''{self.fullname} ({self.username}) is {self.biography}'''



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["username"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["full_name"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["biography"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["business_email"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["external_url"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["edge_followed_by"]["count"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["edge_follow"]["count"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["edge_owner_to_timeline_media"]["count"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["profile_pic_url_hd"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["is_verified"]





 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["is_private"]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = "github"      )->						None:




      """simple docstring"""

      import os

      if os.environ.get('''CI'''      ):
            return  # test failing on GitHub Actions
      snake_case_ =   InstagramUser(SCREAMING_SNAKE_CASE      )
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data      ,      SCREAMING_SNAKE_CASE      )
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 150
      assert instagram_user.number_of_followers > 12_0000
      assert instagram_user.number_of_followings > 15
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.'''      )
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   UpperCAmelCase    =					InstagramUser("""github""")
   print(instagram_user)
   print(f'''{instagram_user.number_of_posts = }''')
   print(f'''{instagram_user.number_of_followers = }''')
   print(f'''{instagram_user.number_of_followings = }''')
   print(f'''{instagram_user.email = }''')
   print(f'''{instagram_user.website = }''')
   print(f'''{instagram_user.profile_picture_url = }''')
   print(f'''{instagram_user.is_verified = }''')
   print(f'''{instagram_user.is_private = }''')