"""simple docstring"""






import os

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

import datasets


__lowerCAmelCase					: int        =datasets.logging.get_logger(__name__)


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

__lowerCAmelCase					: Dict        ="""\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

__lowerCAmelCase					: Optional[int]        ="""
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

__lowerCAmelCase					: Optional[int]        ={
    """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        A__ (		self		):







            """simple docstring"""



            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        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            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')."""		)
                  lowercase														=			"""bleurt-base-128"""

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

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







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scorer.score(references=__lowerCAmelCase	, candidates=__lowerCAmelCase		)
            return {"scores": scores}




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)


__lowerCAmelCase					: str        ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCAmelCase					: Optional[int]        ={
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

__lowerCAmelCase					: Optional[int]        ={
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[int]													=			VOCAB_FILES_NAMES
      snake_case__    :     Tuple													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     Tuple													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     Optional[Any]													=			['input_ids', 'attention_mask']
      snake_case__    :     Dict													=			BartTokenizer

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase="replace"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="<unk>"	, __lowerCAmelCase="<pad>"	, __lowerCAmelCase="<mask>"	, __lowerCAmelCase=False	, __lowerCAmelCase=True	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(
                __lowerCAmelCase	, __lowerCAmelCase	, tokenizer_file=__lowerCAmelCase	, errors=__lowerCAmelCase	, bos_token=__lowerCAmelCase	, eos_token=__lowerCAmelCase	, sep_token=__lowerCAmelCase	, cls_token=__lowerCAmelCase	, unk_token=__lowerCAmelCase	, pad_token=__lowerCAmelCase	, mask_token=__lowerCAmelCase	, add_prefix_space=__lowerCAmelCase	, trim_offsets=__lowerCAmelCase	, **__lowerCAmelCase	, )

            lowercase														=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
            if pre_tok_state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                  lowercase														=			getattr(__lowerCAmelCase	, pre_tok_state.pop("""type"""		)		)
                  lowercase														=			add_prefix_space
                  lowercase														=			pre_tok_class(**__lowerCAmelCase		)

            lowercase														=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowercase														=			"""post_processor"""
            lowercase														=			getattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)
            if tokenizer_component_instance:
                  lowercase														=			json.loads(tokenizer_component_instance.__getstate__()		)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowercase														=			tuple(state["""sep"""]		)
                  if "cls" in state:
                        lowercase														=			tuple(state["""cls"""]		)

                  lowercase														=			False

                  if state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                        lowercase														=			add_prefix_space
                        lowercase														=			True

                  if state.get("""trim_offsets"""	, __lowerCAmelCase		) != trim_offsets:
                        lowercase														=			trim_offsets
                        lowercase														=			True

                  if changes_to_apply:
                        lowercase														=			getattr(__lowerCAmelCase	, state.pop("""type"""		)		)
                        lowercase														=			component_class(**__lowerCAmelCase		)
                        setattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""		)
                  return None
            return str(self._mask_token		)

      @mask_token.setter
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			AddedToken(__lowerCAmelCase	, lstrip=__lowerCAmelCase	, rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase	, __lowerCAmelCase		) else value
            lowercase														=			value

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""		)

            return super()._batch_encode_plus(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""		)

            return super()._encode_plus(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			self._tokenizer.model.save(__lowerCAmelCase	, name=__lowerCAmelCase		)
            return tuple(__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



            lowercase														=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            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]



