




'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class     _a						(			tf.keras.layers.Layer				):
							def __init__(			self		:			Dict     ,       lowercase		:			Dict[str, int]     ,       lowercase		:			List[str]     ,       lowercase		:			int = None     ,       lowercase		:			int = None       ):



											'''simple docstring'''

											super().__init__()
											UpperCAmelCase     =	pad_token_id
											UpperCAmelCase     =	max_length
											UpperCAmelCase     =	vocab
											UpperCAmelCase     =	merges
											UpperCAmelCase     =	BytePairTokenizer(lowercase     ,       lowercase     ,       sequence_length=lowercase       )
							@classmethod
							def 					A   (			cls		:			Dict     ,       lowercase		:			GPTaTokenizer     ,       *lowercase		:			Optional[Any]     ,       **lowercase		:			Optional[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	[''' '''.join(lowercase       ) for m in tokenizer.bpe_ranks.keys()]
											UpperCAmelCase     =	tokenizer.get_vocab()
											return cls(lowercase     ,       lowercase     ,       *lowercase     ,       **lowercase       )
							@classmethod
							def 					A   (			cls		:			Any     ,       lowercase		:			Union[str, os.PathLike]     ,       *lowercase		:			List[str]     ,       **lowercase		:			Optional[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	GPTaTokenizer.from_pretrained(lowercase     ,       *lowercase     ,       **lowercase       )
											return cls.from_tokenizer(lowercase     ,       *lowercase     ,       **lowercase       )
							@classmethod
							def 					A   (			cls		:			str     ,       lowercase		:			Union[str, Any]       ):



											'''simple docstring'''

											return cls(**lowercase       )
							def 					A   (			self		:			Optional[int]       ):



											'''simple docstring'''

											return {
											    "vocab": self.vocab,
											    "merges": self.merges,
											    "max_length": self.max_length,
											    "pad_token_id": self.pad_token_id,
											}







							def 					A   (			self		:			str     ,       lowercase		:			Tuple     ,       lowercase		:			int = None       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.tf_tokenizer(lowercase       )
											UpperCAmelCase     =	tf.ones_like(lowercase       )

											if self.pad_token_id is not None:
															# pad the tokens up to max length
															UpperCAmelCase     =	max_length if max_length is not None else self.max_length

															if max_length is not None:
																			UpperCAmelCase ,  UpperCAmelCase     =	pad_model_inputs(
																			    lowercase     ,       max_seq_length=lowercase     ,       pad_value=self.pad_token_id       )

											return {"attention_mask": attention_mask, "input_ids": input_ids}










'''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}




