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85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import functools
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from generator.consts import BUILTIN_TYPES, RETURN_PATTERNS, READ_MORE_PATTERN, SYMBOLS_MAP
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def normalize_description(text: str) -> str:
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for bad, good in SYMBOLS_MAP.items():
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text = text.replace(bad, good)
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text = READ_MORE_PATTERN.sub("", text)
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text.strip()
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return text
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def normalize_annotation(item: dict):
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for key in list(item.keys()):
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item[key.lower()] = item.pop(key)
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item["description"] = normalize_description(item["description"])
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def normalize_method_annotation(item: dict):
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normalize_annotation(item)
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item["required"] = {"Optional": False, "Yes": True}[item["required"]]
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item["name"] = item.pop("parameter")
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def normalize_type_annotation(item: dict):
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normalize_annotation(item)
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item["name"] = item.pop("field")
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if item["description"].startswith("Optional"):
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item["required"] = False
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item["description"] = item["description"][10:]
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else:
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item["required"] = True
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@functools.lru_cache()
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def normalize_type(string, required=True):
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if not string:
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return "typing.Any"
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union = "typing.Union" if required else "typing.Optional"
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lower = string.lower()
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split = lower.split()
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if split[0] == "array":
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new_string = string[lower.index("of") + 2 :].strip()
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return f"typing.List[{normalize_type(new_string)}]"
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if "or" in split:
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split_types = string.split(" or ")
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norm_str = ", ".join(map(normalize_type, map(str.strip, split_types)))
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return f"{union}[{norm_str}]"
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if "number" in lower:
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return normalize_type(string.replace("number", "").strip())
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if lower in ["true", "false"]:
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return "bool"
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if string not in BUILTIN_TYPES and string[0].isupper():
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return f"types.{string}"
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elif string in BUILTIN_TYPES:
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return BUILTIN_TYPES[string]
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return "typing.Any"
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@functools.lru_cache()
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def get_returning(description):
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parts = list(filter(lambda item: "return" in item.lower(), description.split(".")))
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if not parts:
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return "typing.Any", ""
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sentence = ". ".join(map(str.strip, parts))
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return_type = None
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for pattern in RETURN_PATTERNS:
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temp = pattern.search(sentence)
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if temp:
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return_type = temp.group("type")
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if "other" in temp.groupdict():
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otherwise = temp.group("other")
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return_type += f" or {otherwise}"
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if return_type:
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break
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return return_type, sentence + "."
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