





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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						{}
     if self.train_dir is not None:
       lowercase__			    =						self.train_dir
     if self.validation_dir is not None:
       lowercase__			    =						self.validation_dir
     lowercase__			    =						data_files if data_files else None






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






@dataclass
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	float				      = field(
       default=1E-3    ,			metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}   )






def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  lowercase__			    =						torch.stack([example['pixel_values'] for example in examples]	)
  return {"pixel_values": pixel_values}




def     __UpperCamelCase						()       ->     List[str]:
  # 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__			    =						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__			    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
  else:
    lowercase__    ,					lowercase__    ,					lowercase__			    =						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__			    =						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__			    =						None
  if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
    lowercase__			    =						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__			    =						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__			    =						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__			    =						ds['train'].train_test_split(data_args.train_val_split	)
    lowercase__			    =						split['train']
    lowercase__			    =						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__			    =						{
      '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__			    =						ViTMAEConfig.from_pretrained(model_args.config_name	,		**_SCREAMING_SNAKE_CASE	)
  elif model_args.model_name_or_path:
    lowercase__			    =						ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,		**_SCREAMING_SNAKE_CASE	)
  else:
    lowercase__			    =						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__			    =						ViTImageProcessor.from_pretrained(model_args.image_processor_name	,		**_SCREAMING_SNAKE_CASE	)
  elif model_args.model_name_or_path:
    lowercase__			    =						ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,		**_SCREAMING_SNAKE_CASE	)
  else:
    lowercase__			    =						ViTImageProcessor()

  # create model
  if model_args.model_name_or_path:
    lowercase__			    =						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__			    =						ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE	)

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

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

  # Initialize our trainer
  lowercase__			    =						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__			    =						None
    if training_args.resume_from_checkpoint is not None:
      lowercase__			    =						training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowercase__			    =						last_checkpoint
    lowercase__			    =						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__			    =						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__			    =						{
      '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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Dict:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()










from __future__ import annotations

import math
from collections.abc import Callable




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 100	,		)       ->     float:
  lowercase__			    =						x_start
  lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    lowercase__			    =						(x_end - x_start) / steps + xa
    lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa	,		fxa - fxa	)

    # Increment step
    lowercase__			    =						xa
    lowercase__			    =						fxa

  return length


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return math.sin(10 * x	)

      print("""f(x) = sin(10 * x)""")
      print("""The length of the curve from x = -10 to x = 10 is:""")
      lowercase_					     =	10
      while i <= 100_000:
            print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
            i *= 10


