



import os

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

import datasets


a_						:     Any	     =		datasets.logging.get_logger(__name__)


a_						:     Union[str, Any]	     =		'\\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'

a_						:     int	     =		'\\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'

a_						:     Optional[Any]	     =		'\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'

a_						:     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    lowerCamelCase__  (						datasets.Metric):
     """simple docstring"""



     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				,			__a    ):






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

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

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







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



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




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_						:     int	     =		logging.get_logger(__name__)

a_						:     List[str]	     =		{'vocab_file': 'spiece.model'}

a_						:     Optional[Any]	     =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=False				,			__a=True				,			__a=False				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<sep>"				,			__a="<pad>"				,			__a="<cls>"				,			__a="<mask>"				,			__a=["<eop>", "<eod>"]				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else mask_token

           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__a				,			remove_space=__a				,			keep_accents=__a				,			bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			sep_token=__a				,			pad_token=__a				,			cls_token=__a				,			mask_token=__a				,			additional_special_tokens=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		3

           lowerCamelCase								=		do_lower_case
           lowerCamelCase								=		remove_space
           lowerCamelCase								=		keep_accents
           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )

           try:
                 import jieba
           except ModuleNotFoundError as error:
                 raise error.__class__(
                     "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                     "See https://pypi.org/project/jieba/ for installation."    )
           lowerCamelCase								=		jieba
           lowerCamelCase								=		str.maketrans(" \n"				,			"\u2582\u2583"    )







     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def 						_a      (self    ):






           '''simple docstring'''



           return len(self.sp_model    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if self.remove_space:
                 lowerCamelCase								=		" ".join(inputs.strip().split()    )
           else:
                 lowerCamelCase								=		inputs
           lowerCamelCase								=		outputs.replace("``"				,			"\""    ).replace("''"				,			"\""    )

           if not self.keep_accents:
                 lowerCamelCase								=		unicodedata.normalize("NFKD"				,			__a    )
                 lowerCamelCase								=		"".join([c for c in outputs if not unicodedata.combining(__a    )]    )
           if self.do_lower_case:
                 lowerCamelCase								=		outputs.lower()

           return outputs







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.preprocess_text(__a    )
           lowerCamelCase								=		self.sp_model.encode(__a				,			out_type=__a    )
           lowerCamelCase								=		[]
           for piece in pieces:
                 if len(__a    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
                       lowerCamelCase								=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__a				,			""    )    )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]    ) == 1:
                                   lowerCamelCase								=		cur_pieces[1:]
                             else:
                                   lowerCamelCase								=		cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]    )
                       new_pieces.extend(__a    )
                 else:
                       new_pieces.append(__a    )

           return new_pieces







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.PieceToId(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.IdToPiece(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"".join(__a    ).replace(__a				,			" "    ).strip()
           return out_string







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







     def 						_a      (self				,			__a				,			__a = None				,			__a = False    ):






           '''simple docstring'''



           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__a				,			token_ids_a=__a				,			already_has_special_tokens=__a    )

           if token_ids_a is not None:
                 return ([0] * len(__a    )) + [1] + ([0] * len(__a    )) + [1, 1]
           return ([0] * len(__a    )) + [1, 1]







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[2]

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







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(__a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)







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






           '''simple docstring'''



           lowerCamelCase								=		super()._decode(*__a				,			**__a    )
           lowerCamelCase								=		text.replace(" "				,			""    ).replace("\u2582"				,			" "    ).replace("\u2583"				,			"\n"    )
           return text