





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										=      pytest.mark.integration


_lowerCamelCase										=      {'comet'}
_lowerCamelCase										=      importlib.util.find_spec('fairseq') is not None

_lowerCamelCase										=      {'code_eval'}
_lowerCamelCase										=      os.name == 'nt'

_lowerCamelCase										=      {'bertscore', 'frugalscore', 'perplexity'}
_lowerCamelCase										=      importlib.util.find_spec('transformers') is not None




def    __UpperCAmelCase(      lowercase_      ):
       @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    __UpperCAmelCase(      lowercase_      ):
       @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    __UpperCAmelCase(      lowercase_      ):
       @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    __UpperCAmelCase(      ):
       _lowerCamelCase							:     List[str]	=		[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 			__A       (      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 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		'''[...]'''
             _lowerCamelCase							:     int	=		importlib.import_module(
                 datasets.load.metric_module_factory(os.path.join('''metrics'''      ,							a__)).module_path)
             _lowerCamelCase							:     Tuple	=		datasets.load.import_main_class(metric_module.__name__      ,							dataset=a__)
             # check parameters
             _lowerCamelCase							:     List[Any]	=		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(a__      ,							metric_module.__name__):
                    with self.use_local_metrics():
                           try:
                                  _lowerCamelCase							:     Any	=		doctest.testmod(a__      ,							verbose=a__      ,							raise_on_error=a__)
                           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      ,							a__):



             """simple docstring"""





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


      @contextmanager
      def 				__snake_case			(		self      ,							a__      ,							a__):



             """simple docstring"""





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


      @contextmanager
      def 				__snake_case			(		self):



             """simple docstring"""





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

             with patch('''datasets.load_metric''') as mock_load_metric:
                    _lowerCamelCase							:     Optional[Any]	=		load_local_metric
                    yield



      @classmethod
      def 				__snake_case			(		cls      ,							a__):



             """simple docstring"""





             def wrapper(a__):
                    _lowerCamelCase							:     Dict	=		contextmanager(a__)
                    _lowerCamelCase							:     Optional[int]	=		patcher
                    return patcher

             return wrapper




@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''      )
def    __UpperCAmelCase(      lowercase_      ):
       import tensorflow.compat.va as tf
       from bleurt.score import Predictor

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







       class 			__A       (      lowerCamelCase__  ):




             """simple docstring"""







             def 				__snake_case			(		self      ,							a__):



                    """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:
              _lowerCamelCase							:     Union[str, Any]	=		MockedPredictor()
              yield




@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''      )
def    __UpperCAmelCase(      lowercase_      ):
       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:
              _lowerCamelCase							:     List[Any]	=		bert_cos_score_idf
              yield




@LocalMetricTest.register_intensive_calls_patcher('''comet'''      )
def    __UpperCAmelCase(      lowercase_      ):
       def load_from_checkpoint(lowercase_      ):







              class 			__A       :




                    """simple docstring"""







                    def 				__snake_case			(		self      ,							a__      ,							*a__      ,							**a__):



                           """simple docstring"""





                           assert len(a__) == 2
                           _lowerCamelCase							:     int	=		[0.19, 0.92]
                           return scores, sum(a__) / len(a__)
              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:
              _lowerCamelCase							:     List[Any]	=		None
              with patch('''comet.load_from_checkpoint'''      ) as mock_load_from_checkpoint:
                     _lowerCamelCase							:     Tuple	=		load_from_checkpoint
                     yield




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     int	=		load_metric(os.path.join('''metrics''' ,    '''seqeval'''      )      )
       _lowerCamelCase							:     Optional[int]	=		'''ERROR'''
       _lowerCamelCase							:     Union[str, Any]	=		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_      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase			       =      {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaPreLayerNormForCausalLM',
           'RobertaPreLayerNormForMaskedLM',
           'RobertaPreLayerNormForMultipleChoice',
           'RobertaPreLayerNormForQuestionAnswering',
           'RobertaPreLayerNormForSequenceClassification',
           'RobertaPreLayerNormForTokenClassification',
           'RobertaPreLayerNormModel',
           'RobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaPreLayerNormForCausalLM',
           'TFRobertaPreLayerNormForMaskedLM',
           'TFRobertaPreLayerNormForMultipleChoice',
           'TFRobertaPreLayerNormForQuestionAnswering',
           'TFRobertaPreLayerNormForSequenceClassification',
           'TFRobertaPreLayerNormForTokenClassification',
           'TFRobertaPreLayerNormMainLayer',
           'TFRobertaPreLayerNormModel',
           'TFRobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxRobertaPreLayerNormForCausalLM',
           'FlaxRobertaPreLayerNormForMaskedLM',
           'FlaxRobertaPreLayerNormForMultipleChoice',
           'FlaxRobertaPreLayerNormForQuestionAnswering',
           'FlaxRobertaPreLayerNormForSequenceClassification',
           'FlaxRobertaPreLayerNormForTokenClassification',
           'FlaxRobertaPreLayerNormModel',
           'FlaxRobertaPreLayerNormPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

