




'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


A									=logging.get_logger(__name__)

A									={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A									={
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

A									={"""mobilebert-uncased""": 5_12}


A									={}


class     _a						(			_lowerCAmelCase				):
							__a       :  Optional[int]            =      VOCAB_FILES_NAMES
							__a       :  List[Any]            =      PRETRAINED_VOCAB_FILES_MAP
							__a       :  Tuple            =      PRETRAINED_INIT_CONFIGURATION
							__a       :  Union[str, Any]            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a       :  Optional[int]            =      MobileBertTokenizer
							def __init__(			self		:			List[Any]     ,       lowercase		:			Union[str, Any]=None     ,       lowercase		:			Any=None     ,       lowercase		:			int=True     ,       lowercase		:			Union[str, Any]="[UNK]"     ,       lowercase		:			List[str]="[SEP]"     ,       lowercase		:			Dict="[PAD]"     ,       lowercase		:			str="[CLS]"     ,       lowercase		:			List[str]="[MASK]"     ,       lowercase		:			str=True     ,       lowercase		:			Tuple=None     ,       **lowercase		:			List[Any]     ,       ):



											'''simple docstring'''

											super().__init__(
											    _lowerCAmelCase     ,       tokenizer_file=_lowerCAmelCase     ,       do_lower_case=_lowerCAmelCase     ,       unk_token=_lowerCAmelCase     ,       sep_token=_lowerCAmelCase     ,       pad_token=_lowerCAmelCase     ,       cls_token=_lowerCAmelCase     ,       mask_token=_lowerCAmelCase     ,       tokenize_chinese_chars=_lowerCAmelCase     ,       strip_accents=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )

											UpperCAmelCase     =	json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
											if (
											    normalizer_state.get('''lowercase'''     ,       _lowerCAmelCase       ) != do_lower_case
											    or normalizer_state.get('''strip_accents'''     ,       _lowerCAmelCase       ) != strip_accents
											    or normalizer_state.get('''handle_chinese_chars'''     ,       _lowerCAmelCase       ) != tokenize_chinese_chars
											):
															UpperCAmelCase     =	getattr(_lowerCAmelCase     ,       normalizer_state.pop('''type'''       )       )
															UpperCAmelCase     =	do_lower_case
															UpperCAmelCase     =	strip_accents
															UpperCAmelCase     =	tokenize_chinese_chars
															UpperCAmelCase     =	normalizer_class(**_lowerCAmelCase       )

											UpperCAmelCase     =	do_lower_case
							def 					A   (			self		:			Any     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[int]=None       ):



											'''simple docstring'''

											UpperCAmelCase     =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

											return output
							def 					A   (			self		:			Dict     ,       lowercase		:			List[int]     ,       lowercase		:			Optional[List[int]] = None       ):



											'''simple docstring'''

											UpperCAmelCase     =	[self.sep_token_id]
											UpperCAmelCase     =	[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 					A   (			self		:			Tuple     ,       lowercase		:			str     ,       lowercase		:			Optional[str] = None       ):



											'''simple docstring'''

											UpperCAmelCase     =	self._tokenizer.model.save(_lowerCAmelCase     ,       name=_lowerCAmelCase       )
											return tuple(_lowerCAmelCase       )










'''simple docstring'''







import os

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

import datasets


A									=datasets.logging.get_logger(__name__)


A									='\\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									='\\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									='\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									={
    '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		:			Dict       ):



           '''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		:			List[Any]     ,       lowercase		:			Union[str, Any]       ):



           '''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\').'''       )
               UpperCAmelCase     =	'''bleurt-base-128'''

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

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







       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

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




