






"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCAmelCase							:    List[Any]  							=							logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowerCAmelCase							:    Tuple  							=							{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCAmelCase							:    str  							=							{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCAmelCase							:    Any  							=							sorted(arg_to_scheduler.keys())
lowerCAmelCase							:    Union[str, Any]  							=							"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class 					__magic_name__							( pl.LightningModule       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=None	,				_a="base"	,				_a=None	,				_a=None	,				_a=None	,				**_a	,				):





							"""simple docstring"""
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(lowerCamelCase_				)
							lowerCamelCase       =      0
							lowerCamelCase       =      Path(self.hparams.output_dir				)
							lowerCamelCase       =      self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										lowerCamelCase       =      AutoConfig.from_pretrained(
										    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,				**({"""num_labels""": num_labels} if num_labels is not None else {})	,				cache_dir=lowerCamelCase_	,				**lowerCamelCase_	,				)
							else:
										lowerCamelCase       =      config

							lowerCamelCase       =      ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
							for p in extra_model_params:
										if getattr(self.hparams	,				lowerCamelCase_	,				lowerCamelCase_				):
													assert hasattr(self.config	,				lowerCamelCase_				), f'model config doesn\'t have a `{p}` attribute'
													setattr(self.config	,				lowerCamelCase_	,				getattr(self.hparams	,				lowerCamelCase_				)				)

							if tokenizer is None:
										lowerCamelCase       =      AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,				cache_dir=lowerCamelCase_	,				)
							else:
										lowerCamelCase       =      tokenizer
							lowerCamelCase       =      MODEL_MODES[mode]
							if model is None:
										lowerCamelCase       =      self.model_type.from_pretrained(
										    self.hparams.model_name_or_path	,				from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path				)	,				config=self.config	,				cache_dir=lowerCamelCase_	,				)
							else:
										lowerCamelCase       =      model





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_type.from_pretrained(*lowerCamelCase_	,				**lowerCamelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      arg_to_scheduler[self.hparams.lr_scheduler]
							lowerCamelCase       =      get_schedule_func(
							    self.opt	,				num_warmup_steps=self.hparams.warmup_steps	,				num_training_steps=self.total_steps()				)
							lowerCamelCase       =      {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
							return scheduler





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model
							lowerCamelCase       =      ["""bias""", """LayerNorm.weight"""]
							lowerCamelCase       =      [
							    {
							        """params""": [
							            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)
							        ],  # check this named paramters
							        """weight_decay""": self.hparams.weight_decay,
							    },
							    {
							        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
							        """weight_decay""": 0.0,
							    },
							]
							if self.hparams.adafactor:
										lowerCamelCase       =      Adafactor(
										    lowerCamelCase_	,				lr=self.hparams.learning_rate	,				scale_parameter=lowerCamelCase_	,				relative_step=lowerCamelCase_				)

							else:
										lowerCamelCase       =      AdamW(
										    lowerCamelCase_	,				lr=self.hparams.learning_rate	,				eps=self.hparams.adam_epsilon				)
							lowerCamelCase       =      optimizer

							lowerCamelCase       =      self.get_lr_scheduler()

							return [optimizer], [scheduler]





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""
							return self.validation_step(lowerCamelCase_	,				lowerCamelCase_				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							return self.validation_end(lowerCamelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      max(1	,				self.hparams.gpus				)  # TODO: consider num_tpu_cores
							lowerCamelCase       =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""



							if stage == "test":
										lowerCamelCase       =      len(self.test_dataloader().dataset				)
							else:
										lowerCamelCase       =      self.get_dataloader("""train"""	,				self.hparams.train_batch_size	,				shuffle=lowerCamelCase_				)
										lowerCamelCase       =      len(self.train_dataloader().dataset				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = False				):





							"""simple docstring"""
							raise NotImplementedError("""You must implement this for your task"""				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.train_loader





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.get_dataloader("""dev"""	,				self.hparams.eval_batch_size	,				shuffle=lowerCamelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.get_dataloader("""test"""	,				self.hparams.eval_batch_size	,				shuffle=lowerCamelCase_				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							return os.path.join(
							    self.hparams.data_dir	,				"""cached_{}_{}_{}""".format(
							        lowerCamelCase_	,				list(filter(lowerCamelCase_	,				self.hparams.model_name_or_path.split("""/"""				)				)				).pop()	,				str(self.hparams.max_seq_length				)	,				)	,				)





				@pl.utilities.rank_zero_only
				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.output_dir.joinpath("""best_tfmr"""				)
							lowerCamelCase       =      self.step_count
							self.model.save_pretrained(lowerCamelCase_				)
							self.tokenizer.save_pretrained(lowerCamelCase_				)





				@staticmethod
				def      _lowerCAmelCase       ( _a	,				_a				):





							"""simple docstring"""
							parser.add_argument(
							    """--model_name_or_path"""	,				default=lowerCamelCase_	,				type=lowerCamelCase_	,				required=lowerCamelCase_	,				help="""Path to pretrained model or model identifier from huggingface.co/models"""	,				)
							parser.add_argument(
							    """--config_name"""	,				default=""""""	,				type=lowerCamelCase_	,				help="""Pretrained config name or path if not the same as model_name"""				)
							parser.add_argument(
							    """--tokenizer_name"""	,				default=lowerCamelCase_	,				type=lowerCamelCase_	,				help="""Pretrained tokenizer name or path if not the same as model_name"""	,				)
							parser.add_argument(
							    """--cache_dir"""	,				default=str(Path(lowerCamelCase_				).parent / """test_run""" / """cache"""				)	,				type=lowerCamelCase_	,				help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""	,				)
							parser.add_argument(
							    """--encoder_layerdrop"""	,				type=lowerCamelCase_	,				help="""Encoder layer dropout probability (Optional). Goes into model.config"""	,				)
							parser.add_argument(
							    """--decoder_layerdrop"""	,				type=lowerCamelCase_	,				help="""Decoder layer dropout probability (Optional). Goes into model.config"""	,				)
							parser.add_argument(
							    """--dropout"""	,				type=lowerCamelCase_	,				help="""Dropout probability (Optional). Goes into model.config"""	,				)
							parser.add_argument(
							    """--attention_dropout"""	,				type=lowerCamelCase_	,				help="""Attention dropout probability (Optional). Goes into model.config"""	,				)
							parser.add_argument("""--learning_rate"""	,				default=5e-5	,				type=lowerCamelCase_	,				help="""The initial learning rate for Adam."""				)
							parser.add_argument(
							    """--lr_scheduler"""	,				default="""linear"""	,				choices=lowerCamelCase_	,				metavar=lowerCamelCase_	,				type=lowerCamelCase_	,				help="""Learning rate scheduler"""	,				)
							parser.add_argument("""--weight_decay"""	,				default=0.0	,				type=lowerCamelCase_	,				help="""Weight decay if we apply some."""				)
							parser.add_argument("""--adam_epsilon"""	,				default=1e-8	,				type=lowerCamelCase_	,				help="""Epsilon for Adam optimizer."""				)
							parser.add_argument("""--warmup_steps"""	,				default=0	,				type=lowerCamelCase_	,				help="""Linear warmup over warmup_steps."""				)
							parser.add_argument("""--num_workers"""	,				default=4	,				type=lowerCamelCase_	,				help="""kwarg passed to DataLoader"""				)
							parser.add_argument("""--num_train_epochs"""	,				dest="""max_epochs"""	,				default=3	,				type=lowerCamelCase_				)
							parser.add_argument("""--train_batch_size"""	,				default=32	,				type=lowerCamelCase_				)
							parser.add_argument("""--eval_batch_size"""	,				default=32	,				type=lowerCamelCase_				)
							parser.add_argument("""--adafactor"""	,				action="""store_true"""				)






class 					__magic_name__							( pl.Callback       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""



							if (
							    trainer.is_global_zero and trainer.global_rank == 0
							):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
										pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 					__magic_name__							( pl.Callback       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""



							for name, param in pl_module.model.rag.named_parameters():
										if param.grad is None:
													print(lowerCamelCase_				)






class 					__magic_name__							( pl.Callback       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      trainer.lr_schedulers[0]["""scheduler"""]
							lowerCamelCase       =      {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
							pl_module.logger.log_metrics(lowerCamelCase_				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""
							rank_zero_info("""***** Validation results *****"""				)
							lowerCamelCase       =      trainer.callback_metrics
							# Log results
							for key in sorted(lowerCamelCase_				):
										if key not in ["log", "progress_bar"]:
													rank_zero_info("""{} = {}\n""".format(lowerCamelCase_	,				str(metrics[key]				)				)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""
							rank_zero_info("""***** Test results *****"""				)
							lowerCamelCase       =      trainer.callback_metrics
							# Log and save results to file
							lowerCamelCase       =      os.path.join(pl_module.hparams.output_dir	,				"""test_results.txt"""				)
							with open(lowerCamelCase_	,				"""w"""				) as writer:
										for key in sorted(lowerCamelCase_				):
													if key not in ["log", "progress_bar"]:
																rank_zero_info("""{} = {}\n""".format(lowerCamelCase_	,				str(metrics[key]				)				)				)
																writer.write("""{} = {}\n""".format(lowerCamelCase_	,				str(metrics[key]				)				)				)




def 					a__				(       snake_case__ ,				snake_case__       )		->      int:
			parser.add_argument(
			    """--output_dir""" ,				default=str(Path(lowerCamelCase_       ).parent / """test_run""" / """model_checkpoints"""       ) ,				type=lowerCamelCase_ ,				help="""The output directory where the model predictions and checkpoints will be written.""" ,				)
			parser.add_argument(
			    """--fp16""" ,				action="""store_true""" ,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" ,				)

			parser.add_argument(
			    """--fp16_opt_level""" ,				type=lowerCamelCase_ ,				default="""O2""" ,				help=(
			        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
			        """See details at https://nvidia.github.io/apex/amp.html"""
			    ) ,				)
			parser.add_argument("""--n_tpu_cores""" ,				dest="""tpu_cores""" ,				type=lowerCamelCase_       )
			parser.add_argument("""--max_grad_norm""" ,				dest="""gradient_clip_val""" ,				default=1.0 ,				type=lowerCamelCase_ ,				help="""Max gradient norm"""       )
			parser.add_argument("""--do_train""" ,				action="""store_true""" ,				help="""Whether to run training."""       )
			parser.add_argument("""--do_predict""" ,				action="""store_true""" ,				help="""Whether to run predictions on the test set."""       )
			parser.add_argument(
			    """--gradient_accumulation_steps""" ,				dest="""accumulate_grad_batches""" ,				type=lowerCamelCase_ ,				default=1 ,				help="""Number of updates steps to accumulate before performing a backward/update pass.""" ,				)
			parser.add_argument("""--seed""" ,				type=lowerCamelCase_ ,				default=42 ,				help="""random seed for initialization"""       )
			parser.add_argument(
			    """--data_dir""" ,				default=str(Path(lowerCamelCase_       ).parent / """test_run""" / """dummy-train-data"""       ) ,				type=lowerCamelCase_ ,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" ,				)



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=True ,				snake_case__=[] ,				snake_case__=None ,				snake_case__=None ,				**snake_case__ ,				)		->      List[Any]:
			pl.seed_everything(args.seed       )

			# init model
			lowerCamelCase       =      Path(model.hparams.output_dir       )
			odir.mkdir(exist_ok=lowerCamelCase_       )

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCamelCase       =      pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir ,				prefix="""checkpoint""" ,				monitor="""val_loss""" ,				mode="""min""" ,				save_top_k=1       )
			if early_stopping_callback:
						extra_callbacks.append(lowerCamelCase_       )
			if logging_callback is None:
						lowerCamelCase       =      LoggingCallback()

			lowerCamelCase       =      {}

			if args.fpaa:
						lowerCamelCase       =      16

			if args.gpus > 1:
						lowerCamelCase       =      """auto"""
						lowerCamelCase       =      """ddp"""

			lowerCamelCase       =      args.accumulate_grad_batches
			lowerCamelCase       =      None
			lowerCamelCase       =      """auto"""

			lowerCamelCase       =      pl.Trainer.from_argparse_args(
			    lowerCamelCase_ ,				weights_summary=lowerCamelCase_ ,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,				logger=lowerCamelCase_ ,				val_check_interval=1 ,				num_sanity_val_steps=2 ,				**lowerCamelCase_ ,				)

			if args.do_train:
						trainer.fit(lowerCamelCase_       )

			else:
						print("""RAG modeling tests with new set functions successfuly executed!"""       )
			return trainer









"""simple docstring"""




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

import datasets


lowerCAmelCase							:    List[Any]  							=							"""\
@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							:    List[str]  							=							"""\
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							:    str  							=							"""
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 					__magic_name__							( datasets.Metric       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""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      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a = CHRF.CHAR_ORDER	,				_a = CHRF.WORD_ORDER	,				_a = CHRF.BETA	,				_a = False	,				_a = False	,				_a = False	,				):





							"""simple docstring"""
							lowerCamelCase       =      len(references[0]				)
							if any(len(_a				) != references_per_prediction for refs in references				):
										raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							lowerCamelCase       =      [[refs[i] for refs in references] for i in range(_a				)]

							lowerCamelCase       =      CHRF(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)
							lowerCamelCase       =      sb_chrf.corpus_score(_a	,				_a				)

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



