

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__ :     Tuple				    =    pytest.mark.integration


UpperCAmelCase__ :     List[str]				    =    {"""comet"""}
UpperCAmelCase__ :     Dict				    =    importlib.util.find_spec("""fairseq""") is not None

UpperCAmelCase__ :     Dict				    =    {"""code_eval"""}
UpperCAmelCase__ :     Dict				    =    os.name == """nt"""

UpperCAmelCase__ :     Optional[Any]				    =    {"""bertscore""", """frugalscore""", """perplexity"""}
UpperCAmelCase__ :     int				    =    importlib.util.find_spec("""transformers""") is not None






def 					__lowercase      (     _A			)     ->						Tuple:
      @wraps(__A			)
      def wrapper(self						,    _A			):
            if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                  self.skipTest("""\"test requires Fairseq\""""			)
            else:
                  test_case(self						,    __A			)

      return wrapper






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      @wraps(__A			)
      def wrapper(self						,    _A			):
            if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                  self.skipTest("""\"test requires transformers\""""			)
            else:
                  test_case(self						,    __A			)

      return wrapper






def 					__lowercase      (     _A			)     ->						str:
      @wraps(__A			)
      def wrapper(self						,    _A			):
            if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                  self.skipTest("""\"test not supported on Windows\""""			)
            else:
                  test_case(self						,    __A			)

      return wrapper






def 					__lowercase      (     )     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      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(
    _snake_case	,			_snake_case	,			_snake_case       )
@local
class        a__       (   parameterized.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Dict			       ={}
  UpperCAmelCase__      : Optional[Any]			       =None


  @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""		)
  @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""		)
  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Tuple		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   """[...]"""
        SCREAMING_SNAKE_CASE :      Dict       =   importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("""metrics"""				,				lowerCAmelCase__		)		).module_path		)
        SCREAMING_SNAKE_CASE :      Any       =   datasets.load.import_main_class(metric_module.__name__				,				dataset=lowerCAmelCase__		)
        # check parameters
        SCREAMING_SNAKE_CASE :      List[str]       =   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(lowerCAmelCase__				,				metric_module.__name__		):
              with self.use_local_metrics():
                    try:
                          SCREAMING_SNAKE_CASE :      str       =   doctest.testmod(lowerCAmelCase__				,				verbose=lowerCAmelCase__				,				raise_on_error=lowerCAmelCase__		)
                    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  _lowercase      (			self    : str				,				UpperCAmelCase__    : Optional[int]		)					->str:
        """simple docstring"""







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


  @contextmanager
  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Optional[Any]		)					->Dict:
        """simple docstring"""







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


  @contextmanager
  def  _lowercase      (			self    : List[str]		)					->int:
        """simple docstring"""







        def load_local_metric(UpperCAmelCase__    : Tuple				,				*UpperCAmelCase__    : str				,				**UpperCAmelCase__    : Dict		):
              return load_metric(os.path.join("""metrics"""				,				lowerCAmelCase__		)				,				*lowerCAmelCase__				,				**lowerCAmelCase__		)

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




  @classmethod
  def  _lowercase      (			cls    : List[Any]				,				UpperCAmelCase__    : Any		)					->str:
        """simple docstring"""







        def wrapper(UpperCAmelCase__    : List[Any]		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   contextmanager(lowerCAmelCase__		)
              SCREAMING_SNAKE_CASE :      str       =   patcher
              return patcher

        return wrapper






@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""			)
def 					__lowercase      (     _A			)     ->						int:
      import tensorflow.compat.va as tf
      from bleurt.score import Predictor

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







      class        a__       (   _snake_case       ):







        """simple docstring"""




        def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : Any		)					->int:
              """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:
            SCREAMING_SNAKE_CASE :      Dict       =   MockedPredictor()
            yield






@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""			)
def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      import torch

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

      # 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:
            SCREAMING_SNAKE_CASE :      int       =   bert_cos_score_idf
            yield






@LocalMetricTest.register_intensive_calls_patcher("""comet"""			)
def 					__lowercase      (     _A			)     ->						Optional[Any]:
      def load_from_checkpoint(_A			):







            class        a__       :







              """simple docstring"""




              def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]				,				*UpperCAmelCase__    : Dict				,				**UpperCAmelCase__    : str		)					->Dict:
                    """simple docstring"""







                    assert len(lowerCAmelCase__		) == 2
                    SCREAMING_SNAKE_CASE :      List[str]       =   [0.19, 0.92]
                    return scores, sum(lowerCAmelCase__		) / len(lowerCAmelCase__		)


            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:
            SCREAMING_SNAKE_CASE :      str       =   None
            with patch("""comet.load_from_checkpoint"""			) as mock_load_from_checkpoint:
                  SCREAMING_SNAKE_CASE :      str       =   load_from_checkpoint
                  yield







def 					__lowercase      (     )     ->						int:
      SCREAMING_SNAKE_CASE :      str       =   load_metric(os.path.join("""metrics"""						,    """seqeval"""			)			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   """ERROR"""
      SCREAMING_SNAKE_CASE :      str       =   F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
      with pytest.raises(__A						,    match=re.escape(__A			)			):
            metric.compute(predictions=[]						,    references=[]						,    scheme=__A			)







from __future__ import annotations

import numpy as np






def 					__lowercase      (     _A			)     ->						tuple[np.ndarray, np.ndarray]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   np.shape(_A			)
      if rows != columns:
            SCREAMING_SNAKE_CASE :      int       =   (
                """'table' has to be of square shaped array but got a """
                F"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(_A			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




