Coverage for tests / unit / ai / conftest.py: 90%

1"""Shared fixtures for AI tests.""" 

2 

3from __future__ import annotations 

4 

5import threading 

6from dataclasses import dataclass 

7from typing import Any 

8 

9import pytest 

10 

11from lintro.ai.config import AIConfig 

12from lintro.ai.models import AIFixSuggestion 

13from lintro.ai.providers.base import AIResponse, BaseAIProvider 

14from lintro.parsers.base_issue import BaseIssue 

15 

16 

17class MockAIProvider(BaseAIProvider): 

18 """Thread-safe mock AI provider for testing.""" 

19 

20 def __init__( 

21 self, 

22 responses: list[AIResponse] | None = None, 

23 *, 

24 available: bool = True, 

25 ) -> None: 

26 """Initialize the mock AI provider. 

27 

28 Args: 

29 responses: List of responses to return from complete() calls. 

30 available: Whether the provider reports as available. 

31 """ 

32 super().__init__( 

33 provider_name="mock", 

34 has_sdk=True, 

35 sdk_package="mock", 

36 default_model="mock-model", 

37 default_api_key_env="MOCK_API_KEY", 

38 ) 

39 self.responses: list[AIResponse] = responses or [] 

40 self.calls: list[dict[str, Any]] = [] 

41 self._available = available 

42 self._call_index = 0 

43 self._lock = threading.Lock() 

44 

45 def _create_client(self, *, api_key: str) -> Any: 

46 """Return a mock client.""" 

47 return None 

48 

49 def complete( 

50 self, 

51 prompt: str, 

52 *, 

53 system: str | None = None, 

54 max_tokens: int = 1024, 

55 timeout: float = 60.0, 

56 ) -> AIResponse: 

57 """Return the next queued response or a default.""" 

58 with self._lock: 

59 self.calls.append( 

60 { 

61 "prompt": prompt, 

62 "system": system, 

63 "max_tokens": max_tokens, 

64 "timeout": timeout, 

65 }, 

66 ) 

67 if self._call_index < len(self.responses): 

68 response = self.responses[self._call_index] 

69 self._call_index += 1 

70 return response 

71 return AIResponse( 

72 content="{}", 

73 model="mock-model", 

74 input_tokens=10, 

75 output_tokens=5, 

76 cost_estimate=0.001, 

77 provider="mock", 

78 ) 

79 

80 def is_available(self) -> bool: 

81 """Check if the mock AI provider is available.""" 

82 return self._available 

83 

84 

85@dataclass 

86class MockIssue(BaseIssue): 

87 """Mock issue with code and severity for testing.""" 

88 

89 code: str = "" 

90 severity: str = "" 

91 fixable: bool = False 

92 

93 

94@pytest.fixture 

95def mock_provider() -> MockAIProvider: 

96 """Create a mock AI provider.""" 

97 return MockAIProvider() 

98 

99 

100@pytest.fixture 

101def ai_config() -> AIConfig: 

102 """Create a default AI config for testing.""" 

103 return AIConfig(enabled=True, provider="anthropic") # type: ignore[arg-type] # Pydantic coerces str 

104 

105 

106@pytest.fixture 

107def ai_config_disabled() -> AIConfig: 

108 """Create a disabled AI config for testing.""" 

109 return AIConfig(enabled=False) 

110 

111 

112@pytest.fixture 

113def sample_issues() -> list[MockIssue]: 

114 """Create sample issues for testing.""" 

115 return [ 

116 MockIssue( 

117 file="src/main.py", 

118 line=10, 

119 column=1, 

120 message="Use of assert detected", 

121 code="B101", 

122 severity="low", 

123 ), 

124 MockIssue( 

125 file="src/utils.py", 

126 line=25, 

127 column=5, 

128 message="Use of assert detected", 

129 code="B101", 

130 severity="low", 

131 ), 

132 MockIssue( 

133 file="src/main.py", 

134 line=42, 

135 column=1, 

136 message="Line too long", 

137 code="E501", 

138 severity="warning", 

139 ), 

140 ] 

141 

142 

143@pytest.fixture 

144def sample_fix_suggestions() -> list[AIFixSuggestion]: 

145 """Create sample fix suggestions for testing.""" 

146 return [ 

147 AIFixSuggestion( 

148 file="src/main.py", 

149 line=10, 

150 code="B101", 

151 tool_name="bandit", 

152 original_code="assert x > 0", 

153 suggested_code="if not x > 0:\n raise ValueError", 

154 diff="--- a/src/main.py\n+++ b/src/main.py\n" 

155 "-assert x > 0\n" 

156 "+if not x > 0:\n" 

157 "+ raise ValueError", 

158 explanation="Replace assert with if/raise", 

159 confidence="high", 

160 input_tokens=150, 

161 output_tokens=80, 

162 cost_estimate=0.002, 

163 ), 

164 ]