wehub-resource-sync
b4fbd6fe9f
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105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
"""Tests for _normalize_chat_content in the API server adapter."""
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from gateway.platforms import api_server
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from gateway.platforms.api_server import _normalize_chat_content
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class TestNormalizeChatContent:
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"""Content normalization converts array-based content parts to plain text."""
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def test_none_returns_empty_string(self):
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assert _normalize_chat_content(None) == ""
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def test_plain_string_returned_as_is(self):
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assert _normalize_chat_content("hello world") == "hello world"
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def test_empty_string_returned_as_is(self):
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assert _normalize_chat_content("") == ""
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def test_text_content_part(self):
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content = [{"type": "text", "text": "hello"}]
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assert _normalize_chat_content(content) == "hello"
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def test_input_text_content_part(self):
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content = [{"type": "input_text", "text": "user input"}]
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assert _normalize_chat_content(content) == "user input"
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def test_output_text_content_part(self):
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content = [{"type": "output_text", "text": "assistant output"}]
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assert _normalize_chat_content(content) == "assistant output"
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def test_multiple_text_parts_joined_with_newline(self):
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content = [
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{"type": "text", "text": "first"},
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{"type": "text", "text": "second"},
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]
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assert _normalize_chat_content(content) == "first\nsecond"
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def test_mixed_string_and_dict_parts(self):
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content = ["plain string", {"type": "text", "text": "dict part"}]
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assert _normalize_chat_content(content) == "plain string\ndict part"
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def test_image_url_parts_silently_skipped(self):
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content = [
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{"type": "text", "text": "check this:"},
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{"type": "image_url", "image_url": {"url": "https://example.com/img.png"}},
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]
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assert _normalize_chat_content(content) == "check this:"
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def test_integer_content_converted(self):
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assert _normalize_chat_content(42) == "42"
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def test_boolean_content_converted(self):
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assert _normalize_chat_content(True) == "True"
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def test_deeply_nested_list_respects_depth_limit(self):
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"""Nesting beyond max_depth returns empty string."""
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content = [[[[[[[[[[[["deep"]]]]]]]]]]]]
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result = _normalize_chat_content(content)
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# The deep nesting should be truncated, not crash
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assert isinstance(result, str)
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def test_large_list_capped(self):
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"""Lists beyond MAX_CONTENT_LIST_SIZE are truncated."""
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content = [{"type": "text", "text": f"item{i}"} for i in range(2000)]
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result = _normalize_chat_content(content)
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# Should not contain all 2000 items
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assert result.count("item") <= 1000
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def test_oversized_string_truncated(self):
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"""Strings beyond 64KB are truncated."""
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huge = "x" * 100_000
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result = _normalize_chat_content(huge)
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assert len(result) == 65_536
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def test_empty_text_parts_filtered(self):
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content = [
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{"type": "text", "text": ""},
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{"type": "text", "text": "actual"},
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{"type": "text", "text": ""},
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]
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assert _normalize_chat_content(content) == "actual"
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def test_dict_without_type_skipped(self):
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content = [{"foo": "bar"}, {"type": "text", "text": "real"}]
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assert _normalize_chat_content(content) == "real"
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def test_empty_list_returns_empty(self):
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assert _normalize_chat_content([]) == ""
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def test_many_small_parts_normalize_without_quadratic_rescan(self, monkeypatch):
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"""Large content arrays should normalize in linear time."""
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content = [{"type": "text", "text": "x"} for _ in range(1000)]
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sum_calls = 0
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def counting_sum(values):
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nonlocal sum_calls
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sum_calls += 1
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return sum(values)
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monkeypatch.setattr(api_server, "sum", counting_sum, raising=False)
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result = _normalize_chat_content(content)
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assert result.count("x") == 1000
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assert sum_calls == 0
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