


'''simple docstring'''
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
UpperCAmelCase_			:  Dict										=   pytest.mark.integration


UpperCAmelCase_			:  Optional[Any]										=   {'comet'}
UpperCAmelCase_			:  Any										=   importlib.util.find_spec('fairseq') is not None

UpperCAmelCase_			:  Tuple										=   {'code_eval'}
UpperCAmelCase_			:  int										=   os.name == 'nt'

UpperCAmelCase_			:  Optional[Any]										=   {'bertscore', 'frugalscore', 'perplexity'}
UpperCAmelCase_			:  List[str]										=   importlib.util.find_spec('transformers') is not None


def       _lowercase							(					UpperCamelCase__							:       List[Any]      ):


 @wraps(_snake_case      )
 def wrapper(self							:       List[str],					UpperCamelCase__							:       str      ):
  if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
   self.skipTest('"test requires Fairseq"'      )
  else:
   test_case(self,					_snake_case      )

 return wrapper


def       _lowercase							(					UpperCamelCase__							:       Any      ):


 @wraps(_snake_case      )
 def wrapper(self							:       Union[str, Any],					UpperCamelCase__							:       Optional[int]      ):
  if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
   self.skipTest('"test requires transformers"'      )
  else:
   test_case(self,					_snake_case      )

 return wrapper


def       _lowercase							(					UpperCamelCase__							:       Union[str, Any]      ):


 @wraps(_snake_case      )
 def wrapper(self							:       Union[str, Any],					UpperCamelCase__							:       List[str]      ):
  if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
   self.skipTest('"test not supported on Windows"'      )
  else:
   test_case(self,					_snake_case      )

 return wrapper


def       _lowercase							(					):
 __A	:		Any	  =      [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(
    __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase					)
@local
class     _lowerCamelCase       (     parameterized.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    {}
 __lowercase					:		Optional[Any]     				=    None


 @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'    )
 @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'    )
 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[Any]	  =      '[...]'
  __A	:		Union[str, Any]	  =      importlib.import_module(
      datasets.load.metric_module_factory(os.path.join('metrics'   ,   _UpperCAmelCase    )    ).module_path    )
  __A	:		Dict	  =      datasets.load.import_main_class(metric_module.__name__   ,   dataset=_UpperCAmelCase    )
  # check parameters
  __A	:		Tuple	  =      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	:		Union[str, Any]	  =      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 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      '[...]'
  __A	:		Optional[int]	  =      importlib.import_module(
      datasets.load.metric_module_factory(os.path.join('metrics'   ,   _UpperCAmelCase    )    ).module_path    )
  # run doctest
  with self.use_local_metrics():
   __A	:		List[str]	  =      doctest.testmod(_UpperCAmelCase   ,   verbose=_UpperCAmelCase   ,   raise_on_error=_UpperCAmelCase    )
  self.assertEqual(results.failed   ,   0    )
  self.assertGreater(results.attempted   ,   1    )


 @contextmanager
 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







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


 @contextmanager
 def 			snake_case__ (   self    ):

  """simple docstring"""







  def load_local_metric(__lowercase   ,   *__lowercase   ,   **__lowercase    ):
   return load_metric(os.path.join('metrics'   ,   _UpperCAmelCase    )   ,   *_UpperCAmelCase   ,   **_UpperCAmelCase    )

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







 @classmethod
 def 			snake_case__ (   cls   ,   __lowercase    ):

  """simple docstring"""







  def wrapper(__lowercase    ):
   __A	:		Tuple	  =      contextmanager(_UpperCAmelCase    )
   __A	:		Dict	  =      patcher
   return patcher

  return wrapper


@LocalMetricTest.register_intensive_calls_patcher('bleurt'      )
def       _lowercase							(					UpperCamelCase__							:       int      ):
 import tensorflow.compat.va as tf
 from bleurt.score import Predictor

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

 class     _lowerCamelCase       (     __lowerCAmelCase					):




  '''simple docstring'''


  def 			snake_case__ (   self   ,   __lowercase    ):

   """simple docstring"""







   assert len(input_dict['input_ids']    ) == 2
   return np.array([1.0_3, 1.0_4]    )

    # 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	:		Tuple	  =      MockedPredictor()
  yield


@LocalMetricTest.register_intensive_calls_patcher('bertscore'      )
def       _lowercase							(					UpperCamelCase__							:       Tuple      ):
 import torch

 def bert_cos_score_idf(UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Optional[Any],					*UpperCamelCase__							:       Union[str, Any],					**UpperCamelCase__							:       Optional[Any]      ):
  return torch.tensor([[1.0, 1.0, 1.0]] * len(_snake_case      )      )

 # 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	:		Optional[int]	  =      bert_cos_score_idf
  yield


@LocalMetricTest.register_intensive_calls_patcher('comet'      )
def       _lowercase							(					UpperCamelCase__							:       List[str]      ):


 def load_from_checkpoint(UpperCamelCase__							:       str      ):

  class     _lowerCamelCase       :




   '''simple docstring'''


   def 			snake_case__ (   self   ,   __lowercase   ,   *__lowercase   ,   **__lowercase    ):

    """simple docstring"""







    assert len(_UpperCAmelCase    ) == 2
    __A	:		Optional[int]	  =      [0.1_9, 0.9_2]
    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	:		List[str]	  =      None
  with patch('comet.load_from_checkpoint'      ) as mock_load_from_checkpoint:
   __A	:		Optional[int]	  =      load_from_checkpoint
   yield


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












"""simple docstring"""

from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

