





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





         """simple docstring"""



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






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






def lowerCamelCase__   (							snake_case_     :  int	)				->    Tuple:
   __snake_case								=       torch.stack([example['''pixel_values'''] for example in examples]	)
   return {"pixel_values": pixel_values}






def lowerCamelCase__   (							)				->    List[Any]:
   # 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.

   __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'''	,				snake_case_	,				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(snake_case_	)
   transformers.utils.logging.set_verbosity(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	,				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	,				**snake_case_	)
   elif model_args.model_name_or_path:
      __snake_case								=       ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,				**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	,				**snake_case_	)
   elif model_args.model_name_or_path:
      __snake_case								=       ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,				**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=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(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 snake_case_	: img.convert('''RGB'''	) if img.mode != "RGB" else img	),
           RandomResizedCrop(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(snake_case_     :  List[str]	):
      __snake_case								=       [transforms(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(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(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=snake_case_	,				args=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=snake_case_	,				data_collator=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=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'''	,				snake_case_	)
      trainer.save_metrics('''eval'''	,				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(**snake_case_	)
   else:
      trainer.create_model_card(**snake_case_	)






def lowerCamelCase__   (							snake_case_     :  List[str]	)				->    str:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()








import re
import string

import numpy as np

import datasets


snake_case_           =      '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

snake_case_           =      '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

snake_case_           =      '\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION  )
class 		SCREAMING_SNAKE_CASE__    (	datasets.Metric  ):


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''    ,      id='''sequence'''				),
                     '''references''': datasets.Value('''string'''    ,      id='''sequence'''				),
                 }				)    ,      reference_urls=[]    ,      )


      def        a			(self	:				List[Any]    ,      a__	:				int    ,      a__	:				Optional[int]    ,      a__	:				Optional[Any]=None    ,      a__	:				Any=False    ,      a__	:				Dict=False    ,      a__	:				Tuple=False    ,      ):





         """simple docstring"""



         if regexes_to_ignore is not None:
            for s in regexes_to_ignore:
               __snake_case								=       np.array([re.sub(a__    ,      ''''''    ,      a__				) for x in predictions]				)
               __snake_case								=       np.array([re.sub(a__    ,      ''''''    ,      a__				) for x in references]				)
         else:
            __snake_case								=       np.asarray(a__				)
            __snake_case								=       np.asarray(a__				)

         if ignore_case:
            __snake_case								=       np.char.lower(a__				)
            __snake_case								=       np.char.lower(a__				)

         if ignore_punctuation:
            __snake_case								=       string.punctuation.maketrans(''''''    ,      ''''''    ,      string.punctuation				)
            __snake_case								=       np.char.translate(a__    ,      table=a__				)
            __snake_case								=       np.char.translate(a__    ,      table=a__				)

         if ignore_numbers:
            __snake_case								=       string.digits.maketrans(''''''    ,      ''''''    ,      string.digits				)
            __snake_case								=       np.char.translate(a__    ,      table=a__				)
            __snake_case								=       np.char.translate(a__    ,      table=a__				)

         __snake_case								=       predictions == references

         return {"exact_match": np.mean(a__				) * 100}

