





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
_snake_case  :    List[Any]						    =  pytest.mark.integration


_snake_case  :    Dict						    =  {'comet'}
_snake_case  :    Union[str, Any]						    =  importlib.util.find_spec('fairseq') is not None

_snake_case  :    Optional[int]						    =  {'code_eval'}
_snake_case  :    int						    =  os.name == 'nt'

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

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   @wraps(lowerCAmelCase_	)
   def wrapper(self      :			List[Any],	lowerCAmelCase_      :			Union[str, Any]	):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest('"test requires Fairseq"'	)
      else:
         test_case(self,	lowerCAmelCase_	)

   return wrapper

def   a_    ( lowerCAmelCase_      :			Optional[Any]	):
   @wraps(lowerCAmelCase_	)
   def wrapper(self      :			List[str],	lowerCAmelCase_      :			Tuple	):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest('"test requires transformers"'	)
      else:
         test_case(self,	lowerCAmelCase_	)

   return wrapper

def   a_    ( lowerCAmelCase_      :			Dict	):
   @wraps(lowerCAmelCase_	)
   def wrapper(self      :			int,	lowerCAmelCase_      :			Optional[int]	):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest('"test not supported on Windows"'	)
      else:
         test_case(self,	lowerCAmelCase_	)

   return wrapper

def   a_    ( ):
   __lowerCAmelCase								=	[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"""


  a_        = {}
  a_        = None






  @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'     )
  @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'     )
  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		str     )							->						Optional[int]:
     __lowerCAmelCase								=	'[...]'
     __lowerCAmelCase								=	importlib.import_module(
         datasets.load.metric_module_factory(os.path.join('metrics'					,  lowerCAmelCase_     )     ).module_path     )
     __lowerCAmelCase								=	datasets.load.import_main_class(metric_module.__name__					,  dataset=lowerCAmelCase_     )
     # check parameters
     __lowerCAmelCase								=	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:
              __lowerCAmelCase								=	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 :		Any					,  lowerCAmelCase_ :		Tuple     )							->						List[str]:
     __lowerCAmelCase								=	'[...]'
     __lowerCAmelCase								=	importlib.import_module(
         datasets.load.metric_module_factory(os.path.join('metrics'					,  lowerCAmelCase_     )     ).module_path     )
     # run doctest
     with self.use_local_metrics():
        __lowerCAmelCase								=	doctest.testmod(lowerCAmelCase_					,  verbose=lowerCAmelCase_					,  raise_on_error=lowerCAmelCase_     )
     self.assertEqual(results.failed					,  0     )
     self.assertGreater(results.attempted					,  1     )






  @contextmanager
  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		List[str]     )							->						int:
     if metric_name in self.INTENSIVE_CALLS_PATCHER:
        with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase_     ):
           yield
     else:
        yield






  @contextmanager
  def 				lowercase		(				self :		Union[str, Any]     )							->						List[str]:
     def load_local_metric(lowerCAmelCase_ :		Any					,  *lowerCAmelCase_ :		str					,  **lowerCAmelCase_ :		List[str]     ):
        return load_metric(os.path.join('metrics'					,  lowerCAmelCase_     )					,  *lowerCAmelCase_					,  **lowerCAmelCase_     )

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






  @classmethod
  def 				lowercase		(				cls :		List[str]					,  lowerCAmelCase_ :		List[Any]     )							->						List[str]:
     def wrapper(lowerCAmelCase_ :		str     ):
        __lowerCAmelCase								=	contextmanager(lowerCAmelCase_     )
        __lowerCAmelCase								=	patcher
        return patcher

     return wrapper





@LocalMetricTest.register_intensive_calls_patcher('bleurt'	)
def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   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 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Optional[Any]     )							->						Dict:
        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								=	MockedPredictor()
      yield

@LocalMetricTest.register_intensive_calls_patcher('bertscore'	)
def   a_    ( lowerCAmelCase_      :			Dict	):
   import torch

   def bert_cos_score_idf(lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			List[Any],	*lowerCAmelCase_      :			List[str],	**lowerCAmelCase_      :			Union[str, Any]	):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCAmelCase_	)	)

   # 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								=	bert_cos_score_idf
      yield

@LocalMetricTest.register_intensive_calls_patcher('comet'	)
def   a_    ( lowerCAmelCase_      :			Dict	):
   def load_from_checkpoint(lowerCAmelCase_      :			Dict	):



      class 				_UpperCAmelCase    :

        """simple docstring"""


        def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Any					,  *lowerCAmelCase_ :		Any					,  **lowerCAmelCase_ :		List[Any]     )							->						Optional[Any]:
           assert len(lowerCAmelCase_     ) == 2
           __lowerCAmelCase								=	[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:
      __lowerCAmelCase								=	None
      with patch('comet.load_from_checkpoint'	) as mock_load_from_checkpoint:
         __lowerCAmelCase								=	load_from_checkpoint
         yield




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








from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   if depth < 0:
      raise ValueError('Depth cannot be less than 0'	)

   if len(lowerCAmelCase_	) == 0:
      raise ValueError('Scores cannot be empty'	)

   if depth == height:
      return scores[node_index]

   if is_max:
      return max(
          minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

   return min(
       minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

