




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
_lowerCamelCase	:		Tuple							       =   pytest.mark.integration


_lowerCamelCase	:		Union[str, Any]							       =   {"""comet"""}
_lowerCamelCase	:		Tuple							       =   importlib.util.find_spec("""fairseq""") is not None

_lowerCamelCase	:		Union[str, Any]							       =   {"""code_eval"""}
_lowerCamelCase	:		List[str]							       =   os.name == """nt"""

_lowerCamelCase	:		Union[str, Any]							       =   {"""bertscore""", """frugalscore""", """perplexity"""}
_lowerCamelCase	:		Union[str, Any]							       =   importlib.util.find_spec("""transformers""") is not None







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       @wraps(lowercase_						)
       def wrapper(self ,				lowercase_						):
              if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                     self.skipTest('''"test requires Fairseq"'''						)
              else:
                     test_case(self ,				lowercase_						)

       return wrapper







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       @wraps(lowercase_						)
       def wrapper(self ,				lowercase_						):
              if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                     self.skipTest('''"test requires transformers"'''						)
              else:
                     test_case(self ,				lowercase_						)

       return wrapper







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       @wraps(lowercase_						)
       def wrapper(self ,				lowercase_						):
              if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                     self.skipTest('''"test not supported on Windows"'''						)
              else:
                     test_case(self ,				lowercase_						)

       return wrapper







def SCREAMING_SNAKE_CASE				(       )   ->       Optional[Any]:



       """simple docstring"""

       A__       =    [metric_dir.split(os.sep						)[-2] for metric_dir in glob.glob('''./metrics/*/'''						)]
       return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names()     )
@for_all_test_methods(
    UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__     )
@local
class 				UpperCamelCase_    ( parameterized.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      {}
    UpperCAmelCase__							  =      None


    @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''')
    @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''')
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Tuple)						->Any:





           '''simple docstring'''

           A__       =    '''[...]'''
           A__       =    importlib.import_module(
               datasets.load.metric_module_factory(os.path.join('''metrics'''					, UpperCAmelCase__)).module_path)
           A__       =    datasets.load.import_main_class(metric_module.__name__					, dataset=UpperCAmelCase__)
           # check parameters
           A__       =    inspect.signature(metric._compute).parameters
           self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
           # run doctest
           with self.patch_intensive_calls(UpperCAmelCase__					, metric_module.__name__):
                  with self.use_local_metrics():
                         try:
                                A__       =    doctest.testmod(UpperCAmelCase__					, verbose=UpperCAmelCase__					, raise_on_error=UpperCAmelCase__)
                         except doctest.UnexpectedException as e:
                                raise e.exc_info[1]  # raise the exception that doctest caught
           self.assertEqual(results.failed					, 0)
           self.assertGreater(results.attempted					, 1)


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	List[str])						->List[Any]:





           '''simple docstring'''

           A__       =    '''[...]'''
           A__       =    importlib.import_module(
               datasets.load.metric_module_factory(os.path.join('''metrics'''					, UpperCAmelCase__)).module_path)
           # run doctest
           with self.use_local_metrics():
                  A__       =    doctest.testmod(UpperCAmelCase__					, verbose=UpperCAmelCase__					, raise_on_error=UpperCAmelCase__)
           self.assertEqual(results.failed					, 0)
           self.assertGreater(results.attempted					, 1)


    @contextmanager
    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[Any])						->int:





           '''simple docstring'''

           if metric_name in self.INTENSIVE_CALLS_PATCHER:
                  with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCAmelCase__):
                         yield
           else:
                  yield


    @contextmanager
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->int:





           '''simple docstring'''

           def load_local_metric(UpperCAmelCase__      :	Dict					, *UpperCAmelCase__      :	Optional[int]					, **UpperCAmelCase__      :	int):
                  return load_metric(os.path.join('''metrics'''					, UpperCAmelCase__)					, *UpperCAmelCase__					, **UpperCAmelCase__)

           with patch('''datasets.load_metric''') as mock_load_metric:
                  A__       =    load_local_metric
                  yield





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           def wrapper(UpperCAmelCase__      :	Dict):
                  A__       =    contextmanager(UpperCAmelCase__)
                  A__       =    patcher
                  return patcher

           return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''						)
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

       import tensorflow.compat.va as tf
       from bleurt.score import Predictor

       tf.flags.DEFINE_string('''sv''' ,				'''''' ,				''''''						)  # handle pytest cli flags


       class 				UpperCamelCase_    ( UpperCAmelCase__     ):

           '''simple docstring'''






           def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	str)						->Any:





                  '''simple docstring'''

                  assert len(input_dict['''input_ids''']) == 2
                  return np.array([1.03, 1.04])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
       with patch('''bleurt.score._create_predictor'''						) as mock_create_predictor:
              A__       =    MockedPredictor()
              yield







@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''						)
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       import torch

       def bert_cos_score_idf(lowercase_ ,				lowercase_ ,				*lowercase_ ,				**lowercase_						):
              return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_						)						)

       # mock get_model which is supposed to do download a bert model
       # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
       with patch('''bert_score.scorer.get_model'''						), patch(
           '''bert_score.scorer.bert_cos_score_idf'''						) as mock_bert_cos_score_idf:
              A__       =    bert_cos_score_idf
              yield







@LocalMetricTest.register_intensive_calls_patcher('''comet'''						)
def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       def load_from_checkpoint(lowercase_						):


              class 				UpperCamelCase_    :

                  '''simple docstring'''






                  def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	str					, *UpperCAmelCase__      :	Any					, **UpperCAmelCase__      :	int)						->Tuple:





                         '''simple docstring'''

                         assert len(UpperCAmelCase__) == 2
                         A__       =    [0.19, 0.92]
                         return scores, sum(UpperCAmelCase__) / len(UpperCAmelCase__)
              return Model()

       # mock load_from_checkpoint which is supposed to do download a bert model
       # mock load_from_checkpoint which is supposed to do download a bert model
       with patch('''comet.download_model'''						) as mock_download_model:
              A__       =    None
              with patch('''comet.load_from_checkpoint'''						) as mock_load_from_checkpoint:
                     A__       =    load_from_checkpoint
                     yield







def SCREAMING_SNAKE_CASE				(       )   ->       Optional[int]:



       """simple docstring"""

       A__       =    load_metric(os.path.join('''metrics''' ,				'''seqeval'''						)						)
       A__       =    '''ERROR'''
       A__       =    f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
       with pytest.raises(lowercase_ ,				match=re.escape(lowercase_						)						):
              metric.compute(predictions=[] ,				references=[] ,				scheme=lowercase_						)








import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """title""": (
          """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
          """capacitance, volumetric capacitance, and energy density"""
      ),
      """journal""": """Chem. Mater.""",
      """volume""": 30,
      """pages""": """3979-3990""",
      """year""": 2018,
      """hl""": """en""",
  }
  print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


