





"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


A	     =					get_tests_dir('''fixtures/test_sentencepiece.model''')

A	     =					{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
A	     =					'''>>zh<<'''
A	     =					'''Helsinki-NLP/'''

if is_torch_available():
		A	     =					'''pt'''
elif is_tf_available():
		A	     =					'''tf'''
else:
		A	     =					'''jax'''




@require_sentencepiece
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					MarianTokenizer
					__lowerCAmelCase					=					False
					__lowerCAmelCase					=					True







					def 	_lowerCamelCase						(  self       ):
									super().setUp()
									__a	:  int         =					['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
									__a	:  Optional[Any]         =					dict(zip(_UpperCAmelCase		,     range(len(_UpperCAmelCase       )       )       )       )
									__a	:  Optional[int]         =					Path(self.tmpdirname       )
									save_json(_UpperCAmelCase		,     save_dir / VOCAB_FILES_NAMES['''vocab''']       )
									save_json(_UpperCAmelCase		,     save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']       )
									if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
													copyfile(_UpperCAmelCase		,     save_dir / VOCAB_FILES_NAMES['''source_spm''']       )
													copyfile(_UpperCAmelCase		,     save_dir / VOCAB_FILES_NAMES['''target_spm''']       )

									__a	:  Dict         =					MarianTokenizer.from_pretrained(self.tmpdirname       )
									tokenizer.save_pretrained(self.tmpdirname       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									return MarianTokenizer.from_pretrained(self.tmpdirname		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return (
									    "This is a test",
									    "This is a test",
									)







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					'''</s>'''
									__a	:  int         =					0

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					list(self.get_tokenizer().get_vocab().keys()       )

									self.assertEqual(vocab_keys[0]		,     '''</s>'''       )
									self.assertEqual(vocab_keys[1]		,     '''<unk>'''       )
									self.assertEqual(vocab_keys[-1]		,     '''<pad>'''       )
									self.assertEqual(len(_UpperCAmelCase       )		,     9       )







					def 	_lowerCamelCase						(  self       ):
									self.assertEqual(self.get_tokenizer().vocab_size		,     9       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""       )
									__a	:  Dict         =					en_de_tokenizer(['''I am a small frog''']		,     return_tensors=_UpperCAmelCase       )
									self.assertIsInstance(_UpperCAmelCase		,     _UpperCAmelCase       )
									__a	:  str         =					[38, 121, 14, 697, 38848, 0]
									self.assertListEqual(_UpperCAmelCase		,     batch.input_ids[0]       )

									__a	:  Optional[int]         =					tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(_UpperCAmelCase       )
									__a	:  Dict         =					[x.name for x in Path(_UpperCAmelCase       ).glob('''*'''       )]
									self.assertIn('''source.spm'''		,     _UpperCAmelCase       )
									MarianTokenizer.from_pretrained(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					self.get_tokenizer()

									__a	:  Optional[int]         =					tok(
									    ['''I am a small frog''' * 1000, '''I am a small frog''']		,     padding=_UpperCAmelCase		,     truncation=_UpperCAmelCase		,     return_tensors=_UpperCAmelCase       )
									self.assertIsInstance(_UpperCAmelCase		,     _UpperCAmelCase       )
									self.assertEqual(batch.input_ids.shape		,     (2, 512)       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.get_tokenizer()
									__a	:  Union[str, Any]         =					tok(['''I am a tiny frog''', '''I am a small frog''']		,     padding=_UpperCAmelCase		,     return_tensors=_UpperCAmelCase       )
									self.assertIsInstance(_UpperCAmelCase		,     _UpperCAmelCase       )
									self.assertEqual(batch_smaller.input_ids.shape		,     (2, 10)       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									# fmt: off
									__a	:  Optional[Any]         =					{'''input_ids''': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '''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, 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], [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, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase		,     model_name='''Helsinki-NLP/opus-mt-en-de'''		,     revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''		,     decode_kwargs={'''use_source_tokenizer''': True}		,     )







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''       )

									__a	:  List[str]         =					'''Tämä on testi'''
									__a	:  str         =					'''This is a test'''

									__a	:  Tuple         =					[76, 7, 2047, 2]
									__a	:  Union[str, Any]         =					[69, 12, 11, 940, 2]

									__a	:  Union[str, Any]         =					tokenizer(_UpperCAmelCase       ).input_ids
									self.assertListEqual(_UpperCAmelCase		,     _UpperCAmelCase       )

									__a	:  List[str]         =					tokenizer(text_target=_UpperCAmelCase       ).input_ids
									self.assertListEqual(_UpperCAmelCase		,     _UpperCAmelCase       )

									__a	:  int         =					tokenizer.decode(_UpperCAmelCase		,     skip_special_tokens=_UpperCAmelCase       )
									self.assertEqual(_UpperCAmelCase		,     _UpperCAmelCase       )






"""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 			__lowercase     (     datasets.Metric							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self       ):
									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 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if self.config_name == "default":
													__a	:  List[str]         =					comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
									else:
													__a	:  List[str]         =					comet.load_from_checkpoint(comet.download_model(self.config_name       )       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase=None		,     _UpperCAmelCase=False       ):
									if gpus is None:
													__a	:  str         =					1 if torch.cuda.is_available() else 0
									__a	:  Optional[Any]         =					{'''src''': sources, '''mt''': predictions, '''ref''': references}
									__a	:  Dict         =					[dict(zip(_UpperCAmelCase		,     _UpperCAmelCase       )       ) for t in zip(*data.values()       )]
									__a    ,  __a	:  int         =					self.scorer.predict(_UpperCAmelCase		,     gpus=_UpperCAmelCase		,     progress_bar=_UpperCAmelCase       )
									return {"mean_score": mean_score, "scores": scores}