





"""simple docstring"""



import comet  # From: unbabel-comet
import torch

import datasets


a_					=     datasets.logging.get_logger(__name__)

a_					=     """\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
   author = \"Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon\",
   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
   month = nov,
   year = \"2020\",
   address = \"Online\",
   publisher = \"Association for Computational Linguistics\",
   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
   pages = \"2685--2702\",
}
"""

a_					=     """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

a_					=     """
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION	)
class 						__snake_case   (						datasets.Metric	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return datasets.MetricInfo(
         description=_DESCRIPTION   ,		citation=_CITATION   ,		homepage='''https://unbabel.github.io/COMET/html/index.html'''   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
             {
                 '''sources''': datasets.Value('''string'''   ,		id='''sequence'''    ),
                 '''predictions''': datasets.Value('''string'''   ,		id='''sequence'''    ),
                 '''references''': datasets.Value('''string'''   ,		id='''sequence'''    ),
             }    )   ,		codebase_urls=['''https://github.com/Unbabel/COMET''']   ,		reference_urls=[
             '''https://github.com/Unbabel/COMET''',
             '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
             '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
         ]   ,		)

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     if self.config_name == "default":
         __A       :		str							=	comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''    )    )
     else:
         __A       :		Union[str, Any]							=	comet.load_from_checkpoint(comet.download_model(self.config_name    )    )







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if gpus is None:
         __A       :		Union[str, Any]							=	1 if torch.cuda.is_available() else 0
     __A       :		List[str]							=	{'''src''': sources, '''mt''': predictions, '''ref''': references}
     __A       :		int							=	[dict(zip(__lowerCamelCase   ,		__lowerCamelCase    )    ) for t in zip(*data.values()    )]
     __A					,			__A       :		Tuple							=	self.scorer.predict(__lowerCamelCase   ,		gpus=__lowerCamelCase   ,		progress_bar=__lowerCamelCase    )
     return {"mean_score": mean_score, "scores": scores}











"""simple docstring"""



import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_					=     get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	AlbertTokenizer
 _lowerCamelCase								=	AlbertTokenizerFast
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A       :		str							=	AlbertTokenizer(__lowerCamelCase    )
     tokenizer.save_pretrained(self.tmpdirname    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Tuple							=	'''this is a test'''
     __A       :		Union[str, Any]							=	'''this is a test'''
     return input_text, output_text

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	'''<pad>'''
     __A       :		Optional[int]							=	0

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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]   ,		'''<pad>'''    )
     self.assertEqual(vocab_keys[1]   ,		'''<unk>'''    )
     self.assertEqual(vocab_keys[-1]   ,		'''▁eloquent'''    )
     self.assertEqual(len(__lowerCamelCase    )   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A       :		Dict							=	self.get_tokenizer()
     __A       :		List[Any]							=	self.get_rust_tokenizer()

     __A       :		int							=	'''I was born in 92000, and this is falsé.'''

     __A       :		Any							=	tokenizer.tokenize(__lowerCamelCase    )
     __A       :		Dict							=	rust_tokenizer.tokenize(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     __A       :		List[str]							=	rust_tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	self.get_rust_tokenizer()
     __A       :		Union[str, Any]							=	tokenizer.encode(__lowerCamelCase    )
     __A       :		List[Any]							=	rust_tokenizer.encode(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	AlbertTokenizer(__lowerCamelCase   ,		keep_accents=__lowerCamelCase    )

     __A       :		Optional[int]							=	tokenizer.tokenize('''This is a test'''    )
     self.assertListEqual(__lowerCamelCase   ,		['''▁this''', '''▁is''', '''▁a''', '''▁test''']    )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase    )   ,		[48, 25, 21, 1289]    )

     __A       :		Tuple							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']    )
     __A       :		Optional[Any]							=	tokenizer.convert_tokens_to_ids(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

     __A       :		Optional[Any]							=	tokenizer.convert_ids_to_tokens(__lowerCamelCase    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']   ,		)

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	AlbertTokenizer(__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode('''sequence builders'''    )
     __A       :		str							=	tokenizer.encode('''multi-sequence build'''    )

     __A       :		Union[str, Any]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase    )
     __A       :		Optional[int]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   ,		__lowerCamelCase    )

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	{'''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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0]], '''input_ids''': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 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, 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, 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, 0, 0, 0, 0, 0]], '''token_type_ids''': [[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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase   ,		model_name='''albert-base-v2'''   ,		revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''   ,		)




