






"""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       __A		(   datasets.Metric			):

					def   lowerCamelCase__   (			self  :      Any					)			->							Dict:
											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  :      List[Any]     ,		__snake_case  :      Optional[int]					)			->							Tuple:
											# 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')."""					)
																	__magic_name__:	List[Any]							=       """bleurt-base-128"""

											if self.config_name.lower() in CHECKPOINT_URLS:
																	__magic_name__:	str							=       self.config_name.lower()

											elif self.config_name.upper() in CHECKPOINT_URLS:
																	__magic_name__:	List[Any]							=       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
											__magic_name__:	Union[str, Any]							=       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]					)
											__magic_name__:	Any							=       score.BleurtScorer(os.path.join(__snake_case     ,		__snake_case					)					)






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Union[str, Any]							=       self.scorer.score(references=__snake_case     ,		candidates=__snake_case					)
											return {"scores": scores}









'''simple docstring'''



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
            tokenizer.save_pretrained(self.tmpdirname	)
            tokenizer.save_pretrained(self.tmpdirname     ,	legacy_format=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]     ,	'''<s>'''	)
            self.assertEqual(vocab_keys[1]     ,	'''<pad>'''	)
            self.assertEqual(vocab_keys[-1]     ,	'''<mask>'''	)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            self.assertEqual((2, 6)     ,	batch.input_ids.shape	)
            self.assertEqual((2, 6)     ,	batch.attention_mask.shape	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

            __snake_case            =	'''I was born in 92000, and this is falsé.'''

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            __snake_case            =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






