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https://github.com/NousResearch/hermes-agent.git
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66827f8947
Remove unused imports (F401) and duplicate/shadowed import redefinitions (F811) across the codebase using ruff's safe autofixes. No behavioral changes -- imports only. - ~1400 safe autofixes applied across 644 files (net -1072 lines) - __init__.py re-exports preserved (excluded from F401 removal so public re-export surfaces stay intact) - Re-exports that are imported or monkeypatched by tests but look unused in their defining module are kept with explicit # noqa: F401 (gateway/run.py load_dotenv; run_agent re-exports from agent.message_sanitization, agent.context_compressor, agent.retry_utils, agent.prompt_builder, agent.process_bootstrap, agent.codex_responses_adapter) - Unsafe F841 (unused-variable) fixes deliberately skipped -- those can change behavior when the RHS has side effects - ruff lints remain disabled in pyproject.toml (only PLW1514 is selected); this is a one-time cleanup, not a config change Verification: - python -m compileall: clean - pytest --collect-only: all 27161 tests collect (zero import errors) - core entry points import clean (run_agent, model_tools, cli, toolsets, hermes_state, batch_runner, gateway) - static scan: every name any test imports directly from an edited module still resolves
184 lines
7.8 KiB
Python
184 lines
7.8 KiB
Python
"""Tests for extract_schema.py."""
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from __future__ import annotations
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from extract_schema import (
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extract_schema,
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find_negative_prompt_node,
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find_positive_prompt_node,
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trace_to_node,
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)
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# =============================================================================
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# Connection tracing
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# =============================================================================
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class TestConnectionTracing:
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def test_direct_link(self):
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wf = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "x"}},
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"2": {"class_type": "KSampler",
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"inputs": {"positive": ["1", 0], "negative": ["1", 0]}},
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}
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assert trace_to_node(wf, ["1", 0]) == "1"
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def test_through_reroute(self):
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wf = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "x"}},
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"2": {"class_type": "Reroute", "inputs": {"input": ["1", 0]}},
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"3": {"class_type": "Reroute", "inputs": {"input": ["2", 0]}},
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}
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assert trace_to_node(wf, ["3", 0]) == "1"
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def test_circular_safe(self):
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wf = {
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"1": {"class_type": "Reroute", "inputs": {"input": ["2", 0]}},
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"2": {"class_type": "Reroute", "inputs": {"input": ["1", 0]}},
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}
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# Should hit max_hops without infinite loop
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result = trace_to_node(wf, ["1", 0], max_hops=5)
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assert result in {"1", "2"} # any node, just don't hang
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class TestPositiveNegativeDetection:
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def test_basic(self, sd15_workflow):
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# In sd15_workflow.json node 6 is positive, node 7 is negative
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assert find_positive_prompt_node(sd15_workflow) == "6"
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assert find_negative_prompt_node(sd15_workflow) == "7"
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def test_swapped_order(self):
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wf = {
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["7", 0], "negative": ["6", 0],
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"model": ["4", 0], "latent_image": ["5", 0],
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"seed": 1, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "ugly", "clip": ["4", 1]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "beautiful", "clip": ["4", 1]}},
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}
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# Now 7 is the positive (despite higher node ID)
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assert find_positive_prompt_node(wf) == "7"
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assert find_negative_prompt_node(wf) == "6"
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# =============================================================================
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# Schema extraction
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# =============================================================================
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class TestExtractSchema:
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def test_basic_sd15(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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params = schema["parameters"]
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assert "prompt" in params
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assert "negative_prompt" in params
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assert "seed" in params
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assert "steps" in params
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assert "cfg" in params
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assert "width" in params
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assert "height" in params
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def test_prompt_value_correct(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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# The positive prompt in the example is the landscape one
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assert "landscape" in schema["parameters"]["prompt"]["value"]
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assert "ugly" in schema["parameters"]["negative_prompt"]["value"]
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def test_model_dependencies(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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deps = schema["model_dependencies"]
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ckpts = [d["value"] for d in deps if d["folder"] == "checkpoints"]
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assert "v1-5-pruned-emaonly.safetensors" in ckpts
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def test_output_nodes(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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assert "9" in schema["output_nodes"]
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def test_summary(self, sd15_workflow):
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schema = extract_schema(sd15_workflow)
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s = schema["summary"]
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assert s["has_negative_prompt"] is True
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assert s["has_seed"] is True
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assert s["is_video_workflow"] is False
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assert s["parameter_count"] > 5
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def test_flux_workflow(self, flux_workflow):
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schema = extract_schema(flux_workflow)
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# Flux uses RandomNoise for seed
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assert schema["summary"]["has_seed"] is True
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# Flux has only positive prompt (no negative encoder)
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assert schema["summary"]["has_negative_prompt"] is False
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def test_video_detected(self, video_workflow):
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schema = extract_schema(video_workflow)
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assert schema["summary"]["is_video_workflow"] is True
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class TestEmbeddingDeps:
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def test_extract_from_prompt(self):
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wf = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage",
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"inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "a cat, embedding:goodvibes, embedding:art:1.2",
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"clip": ["1", 1]
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}},
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"7": {"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "ugly, embedding:badhands",
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"clip": ["1", 1]
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}},
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 1, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"9": {"class_type": "SaveImage", "inputs": {"filename_prefix": "x", "images": ["3", 0]}},
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}
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schema = extract_schema(wf)
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names = [d["embedding_name"] for d in schema["embedding_dependencies"]]
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assert sorted(names) == ["art", "badhands", "goodvibes"]
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class TestDuplicateDeduplication:
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def test_two_ksamplers_get_unique_names(self):
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wf = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "x"}},
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"5": {"class_type": "EmptyLatentImage",
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"inputs": {"width": 512, "height": 512, "batch_size": 1}},
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"6": {"class_type": "CLIPTextEncode", "inputs": {"text": "a", "clip": ["1", 1]}},
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"7": {"class_type": "CLIPTextEncode", "inputs": {"text": "b", "clip": ["1", 1]}},
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"3": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 42, "steps": 20, "cfg": 7.5,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 1.0,
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}},
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"4": {"class_type": "KSampler",
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"inputs": {
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"positive": ["6", 0], "negative": ["7", 0],
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"model": ["1", 0], "latent_image": ["5", 0],
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"seed": 99, "steps": 30, "cfg": 8.0,
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"sampler_name": "euler", "scheduler": "normal", "denoise": 0.6,
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}},
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"9": {"class_type": "SaveImage", "inputs": {"filename_prefix": "x", "images": ["3", 0]}},
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}
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schema = extract_schema(wf)
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params = schema["parameters"]
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# Both seeds present with disambiguated names
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seed_keys = [k for k in params if "seed" in k]
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# Symmetric: both renamed (no bare "seed")
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assert "seed" not in params
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assert "seed_3" in params and "seed_4" in params
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assert params["seed_3"]["value"] == 42
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assert params["seed_4"]["value"] == 99
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