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
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		pytest.mark.integration


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

SCREAMING_SNAKE_CASE :	Any							     =		{"""code_eval"""}
SCREAMING_SNAKE_CASE :	Dict							     =		os.name == """nt"""

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



def   __A		(			_A					):



 """simple docstring"""


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

 return wrapper



def   __A		(			_A					):



 """simple docstring"""


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

 return wrapper



def   __A		(			_A					):



 """simple docstring"""


 @wraps(snake_case__					)
 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							,				snake_case__					)

 return wrapper



def   __A		(			):



 """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(
    __a			,       __a			,       __a )
@local
class    A_ (       parameterized.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			{}
  _SCREAMING_SNAKE_CASE    				=			None

  @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"       )
  @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"       )
  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  """[...]"""
   __a       =  importlib.import_module(
       datasets.load.metric_module_factory(os.path.join("metrics"     ,							a_       )       ).module_path       )
   __a       =  datasets.load.import_main_class(metric_module.__name__     ,							dataset=a_       )
   # 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(a_     ,							metric_module.__name__       ):
    with self.use_local_metrics():
     try:
      __a       =  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 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  """[...]"""
   __a       =  importlib.import_module(
       datasets.load.metric_module_factory(os.path.join("metrics"     ,							a_       )       ).module_path       )
   # run doctest
   with self.use_local_metrics():
    __a       =  doctest.testmod(a_     ,							verbose=a_     ,							raise_on_error=a_       )
   self.assertEqual(results.failed     ,							0       )
   self.assertGreater(results.attempted     ,							1       )

  @contextmanager
  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Dict       ):



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

  @contextmanager
  def 		_UpperCAmelCase   (		self :      str       ):



   def load_local_metric(__SCREAMING_SNAKE_CASE :      Any     ,							*__SCREAMING_SNAKE_CASE :      List[Any]     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
    return load_metric(os.path.join("metrics"     ,							a_       )     ,							*a_     ,							**a_       )

   with patch("datasets.load_metric"       ) as mock_load_metric:
    __a       =  load_local_metric
    yield

  @classmethod
  def 		_UpperCAmelCase   (		cls :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):



   def wrapper(__SCREAMING_SNAKE_CASE :      int       ):
    __a       =  contextmanager(a_       )
    __a       =  patcher
    return patcher

   return wrapper



@LocalMetricTest.register_intensive_calls_patcher("bleurt"					)
def   __A		(			_A					):



 """simple docstring"""


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

 tf.flags.DEFINE_string("sv"							,				""							,				""					)  # handle pytest cli flags
 class    A_ (       __a ):

   def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
    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   __A		(			_A					):



 """simple docstring"""


 import torch

 def bert_cos_score_idf(_A							,				_A							,				*_A							,				**_A					):
  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       =  bert_cos_score_idf
  yield



@LocalMetricTest.register_intensive_calls_patcher("comet"					)
def   __A		(			_A					):



 """simple docstring"""


 def load_from_checkpoint(_A					):
  class    A_ :

    def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							*__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
     assert len(a_       ) == 2
     __a       =  [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:
  __a       =  None
  with patch("comet.load_from_checkpoint"					) as mock_load_from_checkpoint:
   __a       =  load_from_checkpoint
   yield






def   __A		(			):



 """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(snake_case__							,				match=re.escape(snake_case__					)					):
  metric.compute(predictions=[]							,				references=[]							,				scheme=snake_case__					)







from __future__ import annotations
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result


