


'''simple docstring'''



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    :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")



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default="cifar10"			,						metadata={"help": "Name of a dataset from the datasets package"} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "The column name of the images in the files."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(default=_a			,						metadata={"help": "A folder containing the training data."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(default=_a			,						metadata={"help": "A folder containing the validation data."} )
     SCREAMING_SNAKE_CASE :				Optional[float]							     =     field(
         default=0.15			,						metadata={"help": "Percent to split off of train for validation."} )
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     field(
         default=_a			,						metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				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  :	int    )		->							Optional[int]:
          __UpperCAmelCase			:				int													=    {}
          if self.train_dir is not None:
               __UpperCAmelCase			:				List[str]													=    self.train_dir
          if self.validation_dir is not None:
               __UpperCAmelCase			:				Optional[Any]													=    self.validation_dir
          __UpperCAmelCase			:				Union[str, Any]													=    data_files if data_files else None



@dataclass
class 			a       :


     """simple docstring"""






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



@dataclass
class 			a       (	_a ):


     """simple docstring"""






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




def 			_a			(		_lowercase      :			str						):





     '''simple docstring'''






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




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    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.
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Union[str, Any]													=    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'''    ,      _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			:				int													=    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			:				int													=    None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          __UpperCAmelCase			:				Optional[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.
     __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			:				Optional[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			:				Tuple													=    ds['''train'''].train_test_split(data_args.train_val_split						)
          __UpperCAmelCase			:				str													=    split['''train''']
          __UpperCAmelCase			:				Any													=    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.
     __UpperCAmelCase			:				int													=    {
         '''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:
          __UpperCAmelCase			:				Union[str, Any]													=    ViTMAEConfig.from_pretrained(model_args.config_name    ,      **_lowercase						)
     elif model_args.model_name_or_path:
          __UpperCAmelCase			:				List[str]													=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,      **_lowercase						)
     else:
          __UpperCAmelCase			:				Union[str, Any]													=    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:
          __UpperCAmelCase			:				Any													=    ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,      **_lowercase						)
     elif model_args.model_name_or_path:
          __UpperCAmelCase			:				List[str]													=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,      **_lowercase						)
     else:
          __UpperCAmelCase			:				int													=    ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          __UpperCAmelCase			:				int													=    ViTMAEForPreTraining.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													=    ViTMAEForPreTraining(_lowercase						)

     if training_args.do_train:
          __UpperCAmelCase			:				Any													=    ds['''train'''].column_names
     else:
          __UpperCAmelCase			:				List[Any]													=    ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          __UpperCAmelCase			:				List[str]													=    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			:				Optional[int]													=    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:
          __UpperCAmelCase			:				str													=    image_processor.size['''shortest_edge''']
     else:
          __UpperCAmelCase			:				int													=    (image_processor.size['''height'''], image_processor.size['''width'''])
     __UpperCAmelCase			:				Optional[int]													=    Compose(
         [
             Lambda(lambda _lowercase						: img.convert('''RGB'''						) if img.mode != "RGB" else img						),
             RandomResizedCrop(_lowercase    ,      scale=(0.2, 1.0)    ,      interpolation=InterpolationMode.BICUBIC						),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean    ,      std=image_processor.image_std						),
         ]						)

     def preprocess_images(_lowercase      :			int						):
          __UpperCAmelCase			:				Tuple													=    [transforms(_lowercase						) 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:
               __UpperCAmelCase			:				Dict													=    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						)

     # Compute absolute learning rate
     __UpperCAmelCase			:				Optional[Any]													=    (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          __UpperCAmelCase			:				Optional[int]													=    training_args.base_learning_rate * total_train_batch_size / 256

     # 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			:				Optional[Any]													=    None
          if training_args.resume_from_checkpoint is not None:
               __UpperCAmelCase			:				Optional[int]													=    training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __UpperCAmelCase			:				int													=    last_checkpoint
          __UpperCAmelCase			:				Optional[Any]													=    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			:				Union[str, Any]													=    trainer.evaluate()
          trainer.log_metrics('''eval'''    ,      _lowercase						)
          trainer.save_metrics('''eval'''    ,      _lowercase						)

     # Write model card and (optionally) push to hub
     __UpperCAmelCase			:				Optional[int]													=    {
         '''tasks''': '''masked-auto-encoding''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''masked-auto-encoding'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_lowercase						)
     else:
          trainer.create_model_card(**_lowercase						)




def 			_a			(		_lowercase      :			Optional[Any]						):





     '''simple docstring'''






     main()


if __name__ == "__main__":
      main()



'''simple docstring'''



import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__UpperCAmelCase    :str				       =     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__UpperCAmelCase    :Tuple				       =     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

__UpperCAmelCase    :List[Any]				       =     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"



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


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Dict    )		->							str:
          if version.parse(scb.__version__    ) < version.parse('''1.4.12'''    ):
               raise ImportWarning(
                   '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                   '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''    )
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string'''							,				id='''sequence'''    ),
                      '''references''': datasets.Sequence(datasets.Value('''string'''							,				id='''sequence'''    )							,				id='''references'''    ),
                  }    )							,				codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']							,				reference_urls=[
                  '''https://github.com/m-popovic/chrF''',
              ]							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	str							,				snake_case  :	int							,				snake_case  :	int = CHRF.CHAR_ORDER							,				snake_case  :	int = CHRF.WORD_ORDER							,				snake_case  :	int = CHRF.BETA							,				snake_case  :	bool = False							,				snake_case  :	bool = False							,				snake_case  :	bool = False							,				)		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    len(references[0]    )
          if any(len(snake_case    ) != references_per_prediction for refs in references    ):
               raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
          __UpperCAmelCase			:				int													=    [[refs[i] for refs in references] for i in range(snake_case    )]

          __UpperCAmelCase			:				int													=    CHRF(snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    sb_chrf.corpus_score(snake_case							,				snake_case    )

          return {
              "score": output.score,
              "char_order": output.char_order,
              "word_order": output.word_order,
              "beta": output.beta,
          }