


"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase     =  logging.get_logger(__name__)


_lowerCamelCase     =  '▁'

_lowerCamelCase     =  {'vocab_file': 'spiece.model'}

_lowerCamelCase     =  {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

_lowerCamelCase     =  {
    'google/reformer-crime-and-punishment': 524288,
}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					VOCAB_FILES_NAMES
				_lowerCAmelCase      :      Tuple      							=					PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase      :      str      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase      :      str      							=					["input_ids", "attention_mask"]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__="</s>"       ,		UpperCAmelCase__="<unk>"       ,		UpperCAmelCase__=[]       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=UpperCAmelCase__       ,		unk_token=UpperCAmelCase__       ,		additional_special_tokens=UpperCAmelCase__       ,		sp_model_kwargs=self.sp_model_kwargs       ,		**UpperCAmelCase__       ,		)

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



				@property
				def   lowerCAmelCase__		(						self     ):
								return self.sp_model.get_piece_size()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				{self.convert_ids_to_tokens(UpperCAmelCase__     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



				def __getstate__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.__dict__.copy()
								SCREAMING_SNAKE_CASE__     							=				None
								return state



				def __setstate__(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				d

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

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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return self.sp_model.encode(UpperCAmelCase__       ,		out_type=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return self.sp_model.piece_to_id(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if index < self.sp_model.get_piece_size():
												SCREAMING_SNAKE_CASE__     							=				self.sp_model.IdToPiece(UpperCAmelCase__     )
								return token



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				[]
								SCREAMING_SNAKE_CASE__     							=				""
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																out_string += self.sp_model.decode(UpperCAmelCase__     ) + token
																SCREAMING_SNAKE_CASE__     							=				[]
												else:
																current_sub_tokens.append(UpperCAmelCase__     )
								out_string += self.sp_model.decode(UpperCAmelCase__     )
								return out_string.strip()






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								if not os.path.isdir(UpperCAmelCase__     ):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
												return
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    UpperCAmelCase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

								return (out_vocab_file,)




"""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}
