






"""simple docstring"""





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__lowerCamelCase							=  datasets.logging.get_logger(__name__)


__lowerCamelCase							=  '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

__lowerCamelCase							=  '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

__lowerCamelCase							=  '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

__lowerCamelCase							=  {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}



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

					def   lowerCamelCase__   (			self  :      Any					)			->							Dict:
											return datasets.MetricInfo(
											    description=_DESCRIPTION     ,		citation=_CITATION     ,		homepage="""https://github.com/google-research/bleurt"""     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""     ,		id="""sequence"""					),
											            """references""": datasets.Value("""string"""     ,		id="""sequence"""					),
											        }					)     ,		codebase_urls=["""https://github.com/google-research/bleurt"""]     ,		reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]     ,		)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[int]					)			->							Tuple:
											# check that config name specifies a valid BLEURT model
											if self.config_name == "default":
																	logger.warning(
																	    """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
																	    """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""					)
																	__magic_name__:	List[Any]							=       """bleurt-base-128"""

											if self.config_name.lower() in CHECKPOINT_URLS:
																	__magic_name__:	str							=       self.config_name.lower()

											elif self.config_name.upper() in CHECKPOINT_URLS:
																	__magic_name__:	List[Any]							=       self.config_name.upper()

											else:
																	raise KeyError(
																	    F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'					)

											# download the model checkpoint specified by self.config_name and set up the scorer
											__magic_name__:	Union[str, Any]							=       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]					)
											__magic_name__:	Any							=       score.BleurtScorer(os.path.join(__snake_case     ,		__snake_case					)					)






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Union[str, Any]							=       self.scorer.score(references=__snake_case     ,		candidates=__snake_case					)
											return {"scores": scores}





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__a         =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep			) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''			)

					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE_			):
									copyfile(self.vocab_file			,				SCREAMING_SNAKE_CASE_			)

					return (out_vocab_file,)




