




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
lowercase__				:      Dict     = pytest.mark.integration


lowercase__				:      str     = {"comet"}
lowercase__				:      int     = importlib.util.find_spec("fairseq") is not None

lowercase__				:      Optional[Any]     = {"code_eval"}
lowercase__				:      List[str]     = os.name == "nt"

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
     @wraps(__UpperCamelCase)
     def wrapper(self		, __UpperCamelCase):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest("\"test requires Fairseq\"")
          else:
               test_case(self		, __UpperCamelCase)

     return wrapper

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Any:
     @wraps(__UpperCamelCase)
     def wrapper(self		, __UpperCamelCase):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest("\"test requires transformers\"")
          else:
               test_case(self		, __UpperCamelCase)

     return wrapper

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					List[str]:
     @wraps(__UpperCamelCase)
     def wrapper(self		, __UpperCamelCase):
          if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
               self.skipTest("\"test not supported on Windows\"")
          else:
               test_case(self		, __UpperCamelCase)

     return wrapper

def  SCREAMING_SNAKE_CASE							(       )      ->					str:
     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(
    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__				)
@local
class       a__	( parameterized.TestCase				):
     a		:      str		 =     {}
     a		:      str		 =     None







     @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
     @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"			)
     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          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       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          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       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



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







     @contextmanager
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''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:
               a     =				load_local_metric
               yield







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          def wrapper(A			):
               a     =				contextmanager(A			)
               a     =				patcher
               return patcher

          return wrapper







@LocalMetricTest.register_intensive_calls_patcher("bleurt")
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
     import tensorflow.compat.va as tf
     from bleurt.score import Predictor

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




     class       a__	( UpperCamelCase__				):







          def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



               '''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     =				MockedPredictor()
          yield

@LocalMetricTest.register_intensive_calls_patcher("bertscore")
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     import torch

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

     # 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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     def load_from_checkpoint(__UpperCamelCase):




          class       a__	:







               def       lowerCAmelCase_    (     self  ,		A  ,		*A  ,		**A			)  ->   Optional[int]:



                    '''simple docstring'''



                    assert len(A			) == 2
                    a     =				[0.1_9, 0.9_2]
                    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  SCREAMING_SNAKE_CASE							(       )      ->					Optional[Any]:
     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(__UpperCamelCase		, match=re.escape(__UpperCamelCase)):
          metric.compute(predictions=[]		, references=[]		, scheme=__UpperCamelCase)







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowercase__				:      str	   = TypeVar("T")




class       a__	( Generic[T]				):







     def __init__(     self  ,		A = True			)  ->   None:



          '''simple docstring'''



          a     =				{}  # dictionary of lists
          a     =				directed







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   GraphAdjacencyList[T]:



          '''simple docstring'''



          if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    self.adj_list[destination_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(A			)
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                    a     =				[destination_vertex]
                    a     =				[source_vertex]
          else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[]
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                    a     =				[destination_vertex]
                    a     =				[]

          return self







     def __repr__(     self			)  ->   str:



          '''simple docstring'''



          return pformat(self.adj_list			)

