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import pytest
import torch
from types import SimpleNamespace
from cognitive_mapping_probe.llm_iface import LLM
@pytest.fixture(scope="session")
def mock_llm_config():
"""Stellt eine minimale, Schein-Konfiguration für das LLM bereit."""
return SimpleNamespace(
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=4
)
@pytest.fixture
def mock_llm(mocker, mock_llm_config):
"""
Erstellt einen schnellen "Mock-LLM" für Unit-Tests.
ERWEITERT: Patcht nun auch die `concepts`-Abhängigkeit.
"""
mock_tokenizer = mocker.MagicMock()
mock_tokenizer.eos_token_id = 1
def mock_model_forward(*args, **kwargs):
batch_size = 1
if 'input_ids' in kwargs: seq_len = kwargs['input_ids'].shape[1]
elif 'past_key_values' in kwargs: seq_len = kwargs['past_key_values'][0][0].shape[-2] + 1
else: seq_len = 1
mock_outputs = {
"hidden_states": tuple([torch.randn(batch_size, seq_len, mock_llm_config.hidden_size) for _ in range(mock_llm_config.num_hidden_layers + 1)]),
"past_key_values": tuple([(torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16), torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16)) for _ in range(mock_llm_config.num_hidden_layers)]),
"logits": torch.randn(batch_size, seq_len, 32000)
}
return SimpleNamespace(**mock_outputs)
llm_instance = LLM.__new__(LLM)
llm_instance.model = mock_model_forward
llm_instance.model.config = mock_llm_config
llm_instance.model.device = 'cpu'
llm_instance.model.dtype = torch.float32
mock_lm_head = mocker.MagicMock(return_value=torch.randn(1, 32000))
llm_instance.model.lm_head = mock_lm_head
llm_instance.tokenizer = mock_tokenizer
llm_instance.config = mock_llm_config
llm_instance.seed = 42
llm_instance.set_all_seeds = mocker.MagicMock()
mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_or_load_model', return_value=llm_instance)
# Patch für die wiederhergestellte `concepts`-Funktion
mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_concept_vector', return_value=torch.randn(mock_llm_config.hidden_size))
return llm_instance
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