


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
__lowerCAmelCase			=							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''')

__lowerCAmelCase			=							list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCAmelCase			=							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





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




        '''simple docstring'''
        lowercase__:       str        =       {}
        if self.train_dir is not None:
               lowercase__:       List[Any]        =       self.train_dir
        if self.validation_dir is not None:
               lowercase__:       Optional[Any]        =       self.validation_dir
        lowercase__:       Any        =       data_files if data_files else None





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





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__=192    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=0.6			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       input_size
        lowercase__:       List[str]        =       mask_patch_size
        lowercase__:       Dict        =       model_patch_size
        lowercase__:       int        =       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'			)

        lowercase__:       Union[str, Any]        =       self.input_size // self.mask_patch_size
        lowercase__:       List[str]        =       self.mask_patch_size // self.model_patch_size

        lowercase__:       Optional[Any]        =       self.rand_size**2
        lowercase__:       Dict        =       int(np.ceil(self.token_count * self.mask_ratio			)			)
 def __call__(       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       np.random.permutation(self.token_count			)[: self.mask_count]
        lowercase__:       Dict        =       np.zeros(self.token_count    ,			dtype=lowerCAmelCase__			)
        lowercase__:       str        =       1

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

        return torch.tensor(mask.flatten()			)





def        snake_case_					(  snake_case					)  ->    int:
       lowercase__:       List[str]        =       torch.stack([example['pixel_values'] for example in examples]					)
       lowercase__:       Optional[int]        =       torch.stack([example['mask'] for example in examples]					)
       return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def        snake_case_					(  )  ->    Tuple:
       # 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__:       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.
              lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              lowercase__      ,						lowercase__      ,						lowercase__:       List[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_mim'      ,							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()

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

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

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

       # create model
       if model_args.model_name_or_path:
              lowercase__:       Tuple        =       AutoModelForMaskedImageModeling.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'					)
              lowercase__:       Dict        =       AutoModelForMaskedImageModeling.from_config(snake_case					)

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

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

       # transformations as done in original SimMIM paper
       # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
       lowercase__:       Any        =       Compose(
           [
               Lambda(lambda snake_case					: img.convert('RGB'					) if img.mode != "RGB" else img					),
               RandomResizedCrop(model_args.image_size      ,							scale=(0.6_7, 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
       lowercase__:       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(snake_case					):
              lowercase__:       str        =       [transforms(snake_case					) for image in examples[image_column_name]]
              lowercase__:       List[Any]        =       [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:
                     lowercase__:       Any        =       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:
                     lowercase__:       Optional[int]        =       (
                         ds['validation'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
                     )
              # Set the validation transforms
              ds["validation"].set_transform(snake_case					)

       # Initialize our trainer
       lowercase__:       Union[str, Any]        =       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:
              lowercase__:       int        =       None
              if training_args.resume_from_checkpoint is not None:
                     lowercase__:       Tuple        =       training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     lowercase__:       Union[str, Any]        =       last_checkpoint
              lowercase__:       int        =       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:
              lowercase__:       Dict        =       trainer.evaluate()
              trainer.log_metrics('eval'      ,							snake_case					)
              trainer.save_metrics('eval'      ,							snake_case					)

       # Write model card and (optionally) push to hub
       lowercase__:       List[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(**snake_case					)
       else:
              trainer.create_model_card(**snake_case					)


if __name__ == "__main__":
 main()






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

import datasets


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

__lowerCAmelCase			=							'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__lowerCAmelCase			=							'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> 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."]
        >>> 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."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> 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."]
        >>> 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."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> 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."]
        >>> 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."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

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


