





from sklearn.metrics import mean_absolute_error

import datasets


_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}
}
"""

_UpperCamelCase   :							int     =	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_UpperCamelCase   :							List[str]     =	"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""







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

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION  ,    citation=_CITATION  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(self._get_feature_types()     )  ,    reference_urls=[
             '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
         ]  ,    )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     if self.config_name == "multilist":
        return {
            "predictions": datasets.Sequence(datasets.Value('''float'''     )     ),
            "references": datasets.Sequence(datasets.Value('''float'''     )     ),
        }
     else:
        return {
            "predictions": datasets.Value('''float'''     ),
            "references": datasets.Value('''float'''     ),
        }





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase=None  ,    UpperCAmelCase="uniform_average"     )-> Optional[int]:
     __A				 =       mean_absolute_error(UpperCAmelCase  ,    UpperCAmelCase  ,    sample_weight=UpperCAmelCase  ,    multioutput=UpperCAmelCase     )

     return {"mae": mae_score}






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   :							str     =	"""stas/tiny-wmt19-en-ru"""







class 						_lowerCAmelCase(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   FSMTTokenizer
  lowerCamelCase__      					=   False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A				 =       [
         '''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>''',
     ]
     __A				 =       dict(zip(UpperCAmelCase  ,    range(len(UpperCAmelCase     )     )     )     )
     __A				 =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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

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

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FSMTTokenizer.from_pretrained(UpperCAmelCase     )
     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 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       FSMTTokenizer(self.langs  ,    self.src_vocab_file  ,    self.tgt_vocab_file  ,    self.merges_file     )

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

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

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       self.tokenizer_ru_en

     __A				 =       tokenizer.encode('''sequence builders'''  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=UpperCAmelCase     )

     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase     )
     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase  ,    UpperCAmelCase     )

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

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.tokenizer_en_ru
     __A				 =       self.tokenizer_ru_en

     __A				 =       [
         [
             '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
             [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
         ],
         ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 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:
        __A				 =       tokenizer_enc.encode(UpperCAmelCase  ,    return_tensors=UpperCAmelCase     )
        self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

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

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

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





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

