




'''simple docstring'''



import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCAmelCase_				:     Any     			=						'''stas/tiny-wmt19-en-ru'''





class 			lowerCAmelCase      (       __lowerCAmelCase ,      unittest.TestCase):
       __lowercase     :					Dict       				=	FSMTTokenizer
       __lowercase     :					Optional[Any]       				=	False


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



            '''simple docstring'''




            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case			    =     [
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''w</w>''',
                '''r</w>''',
                '''t</w>''',
                '''lo''',
                '''low''',
                '''er</w>''',
                '''low</w>''',
                '''lowest</w>''',
                '''newer</w>''',
                '''wider</w>''',
                '''<unk>''',
            ]
            __snake_case			    =     dict(zip(__SCREAMING_SNAKE_CASE			,     range(len(__SCREAMING_SNAKE_CASE     )     )     )     )
            __snake_case			    =     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

            __snake_case			    =     ['''en''', '''ru''']
            __snake_case			    =     {
                '''langs''': self.langs,
                '''src_vocab_size''': 10,
                '''tgt_vocab_size''': 20,
            }

            __snake_case			    =     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''src_vocab_file''']     )
            __snake_case			    =     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
            __snake_case			    =     os.path.join(self.tmpdirname			,     '''tokenizer_config.json'''     )
            __snake_case			    =     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''merges_file''']     )
            with open(self.src_vocab_file			,     '''w'''     ) as fp:
                 fp.write(json.dumps(__SCREAMING_SNAKE_CASE     )     )
            with open(self.tgt_vocab_file			,     '''w'''     ) as fp:
                 fp.write(json.dumps(__SCREAMING_SNAKE_CASE     )     )
            with open(self.merges_file			,     '''w'''     ) as fp:
                 fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE     )     )
            with open(__SCREAMING_SNAKE_CASE			,     '''w'''     ) as fp:
                 fp.write(json.dumps(__SCREAMING_SNAKE_CASE     )     )


       @cached_property
       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     )


       @cached_property
       def 					lowerCAmelCase					(      self     )					->     List[Any]:



            '''simple docstring'''




            return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''     )


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            __snake_case			    =     FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     )
            self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]			,     ['''en''', '''ru''']     )
            self.assertEqual(tokenizer.src_vocab_size			,     21     )
            self.assertEqual(tokenizer.tgt_vocab_size			,     21     )


       def 					lowerCAmelCase					(      self     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     FSMTTokenizer(self.langs			,     self.src_vocab_file			,     self.tgt_vocab_file			,     self.merges_file     )

            __snake_case			    =     '''lower'''
            __snake_case			    =     ['''low''', '''er</w>''']
            __snake_case			    =     tokenizer.tokenize(__SCREAMING_SNAKE_CASE     )
            self.assertListEqual(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            __snake_case			    =     tokens + ['''<unk>''']
            __snake_case			    =     [14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )			,     __SCREAMING_SNAKE_CASE     )


       @slow
       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     self.tokenizer_ru_en

            __snake_case			    =     tokenizer.encode('''sequence builders'''			,     add_special_tokens=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     tokenizer.encode('''multi-sequence build'''			,     add_special_tokens=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            assert encoded_sentence == text + [2]
            assert encoded_pair == text + [2] + text_a + [2]


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



            '''simple docstring'''




            __snake_case			    =     self.tokenizer_en_ru
            __snake_case			    =     self.tokenizer_ru_en

            __snake_case			    =     [
                [
                    '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
                    [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
                ],
                ['''This is it. No more. I\'m done!''', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
            ]

            # if data needs to be recreated or added, run:
            # import torch
            # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
            # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

            for src_text, tgt_input_ids in targets:
                 __snake_case			    =     tokenizer_enc.encode(__SCREAMING_SNAKE_CASE			,     return_tensors=__SCREAMING_SNAKE_CASE     )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

                 # and decode backward, using the reversed languages model
                 __snake_case			    =     tokenizer_dec.decode(__SCREAMING_SNAKE_CASE			,     skip_special_tokens=__SCREAMING_SNAKE_CASE     )
                 self.assertEqual(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )


       @slow
       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''			,     do_lower_case=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     tokenizer.tokenize('''USA is United States of America'''     )
            __snake_case			    =     ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
            self.assertListEqual(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )


       @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            pass




       @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
       def 					lowerCAmelCase					(      self     )					->     Optional[int]:



            '''simple docstring'''




            pass












'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase_				:     List[Any]     			=						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase_				:     Union[str, Any]     			=						'''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''





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


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                        '''references''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                    }
                    if self.config_name == '''multilabel'''
                    else {
                        '''predictions''': datasets.Value('''int32'''     ),
                        '''references''': datasets.Value('''int32'''     ),
                    }     )			,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']			,     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






