


"""simple docstring"""






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase     =  {
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

_lowerCamelCase     =  {'allegro/herbert-base-cased': 514}
_lowerCamelCase     =  {}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Union[str, Any]      							=					VOCAB_FILES_NAMES
				_lowerCAmelCase      :      Dict      							=					PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase      :      Optional[int]      							=					PRETRAINED_INIT_CONFIGURATION
				_lowerCAmelCase      :      Tuple      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase      :      Any      							=					HerbertTokenizer



				def __init__(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__="<s>"       ,		UpperCAmelCase__="<unk>"       ,		UpperCAmelCase__="<pad>"       ,		UpperCAmelCase__="<mask>"       ,		UpperCAmelCase__="</s>"       ,		**UpperCAmelCase__       ,		):
								super().__init__(
								    UpperCAmelCase__       ,		UpperCAmelCase__       ,		tokenizer_file=UpperCAmelCase__       ,		cls_token=UpperCAmelCase__       ,		unk_token=UpperCAmelCase__       ,		pad_token=UpperCAmelCase__       ,		mask_token=UpperCAmelCase__       ,		sep_token=UpperCAmelCase__       ,		**UpperCAmelCase__       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				[self.cls_token_id]
								SCREAMING_SNAKE_CASE__     							=				[self.sep_token_id]
								if token_ids_a is None:
												return cls + token_ids_a + sep

								return cls + token_ids_a + sep + token_ids_a + sep



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = False     ):
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCAmelCase__       ,		token_ids_a=UpperCAmelCase__       ,		already_has_special_tokens=UpperCAmelCase__     )

								if token_ids_a is None:
												return [1] + ([0] * len(UpperCAmelCase__     )) + [1]
								return [1] + ([0] * len(UpperCAmelCase__     )) + [1] + ([0] * len(UpperCAmelCase__     )) + [1]



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				[self.sep_token_id]
								SCREAMING_SNAKE_CASE__     							=				[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     ) * [0] + len(token_ids_a + sep     ) * [1]






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				self._tokenizer.model.save(UpperCAmelCase__       ,		name=UpperCAmelCase__     )
								return tuple(UpperCAmelCase__     )




"""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 			lowerCamelCase_		(			datasets.Metric   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								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       ,		UpperCAmelCase__     ):
								# 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')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				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
								SCREAMING_SNAKE_CASE__     							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
								SCREAMING_SNAKE_CASE__     							=				score.BleurtScorer(os.path.join(UpperCAmelCase__       ,		UpperCAmelCase__     )     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
