Repository: pydantic/pydantic

Issue:
TypeError during generation schema for model field (type Callable) with provided default value.
### Checks

* [x] I added a descriptive title to this issue
* [x] I have searched (google, github) for similar issues and couldn't find anything
* [x] I have read and followed [the docs](https://pydantic-docs.helpmanual.io/) and still think this is a bug

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# Bug

Output of `python -c "import pydantic.utils; print(pydantic.utils.version_info())"`:
```
             pydantic version: 1.7.1
            pydantic compiled: True
                 install path: /home/***/.local/share/virtualenvs/lerdem-oY9IIzMI/lib/python3.6/site-packages/pydantic
               python version: 3.6.9 (default, Oct  8 2020, 12:12:24)  [GCC 8.4.0]
                     platform: Linux-5.0.0-32-generic-x86_64-with-LinuxMint-19.3-tricia
     optional deps. installed: []
```
<!-- or if you're using pydantic prior to v1.3, manually include: OS, python version and pydantic version -->

<!-- Please read the [docs](https://pydantic-docs.helpmanual.io/) and search through issues to
confirm your bug hasn't already been reported. -->

<!-- Where possible please include a self-contained code snippet describing your bug: -->

```py
>>> from pydantic import BaseModel, Field
>>> from typing import Callable
>>> class Foo(BaseModel):
...     # without default value, getting user warning
...     callback: Callable[[], dict]
... 
>>> Foo.schema()
__main__:1: UserWarning: Callable callback was excluded from schema since JSON schema has no equivalent type.
{'title': 'Foo', 'type': 'object', 'properties': {}}

>>> class FooDefault(BaseModel):
...     callback: Callable[[], dict] = Field(default=lambda: {'some': 'data'})
>>> FooDefault.schema()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pydantic/main.py", line 637, in pydantic.main.BaseModel.schema
  File "pydantic/schema.py", line 153, in pydantic.schema.model_schema
  File "pydantic/schema.py", line 523, in pydantic.schema.model_process_schema
  File "pydantic/schema.py", line 564, in pydantic.schema.model_type_schema
  File "pydantic/schema.py", line 215, in pydantic.schema.field_schema
  File "pydantic/schema.py", line 183, in pydantic.schema.get_field_info_schema
  File "pydantic/schema.py", line 846, in pydantic.schema.encode_default
  File "pydantic/json.py", line 65, in pydantic.json.pydantic_encoder
TypeError: Object of type 'function' is not JSON serializable
```

One possible solution according for docs is to use custom [`Confing.json_encoders`](https://pydantic-docs.helpmanual.io/usage/exporting_models/#json_encoders). So i have tried:
```py
>>> def hack():
...     pass
>>> class FooDefault(BaseModel):
...     callback: Callable[[], dict] = Field(default=lambda: {'some': 'data'})
...     class Config:
...         json_encoders = {
...             # i don't know how to make it right way
...             type(hack): str,
...             Callable: str
...         }
>>> FooDefault.schema()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pydantic/main.py", line 637, in pydantic.main.BaseModel.schema
  File "pydantic/schema.py", line 153, in pydantic.schema.model_schema
  File "pydantic/schema.py", line 523, in pydantic.schema.model_process_schema
  File "pydantic/schema.py", line 564, in pydantic.schema.model_type_schema
  File "pydantic/schema.py", line 215, in pydantic.schema.field_schema
  File "pydantic/schema.py", line 183, in pydantic.schema.get_field_info_schema
  File "pydantic/schema.py", line 846, in pydantic.schema.encode_default
  File "pydantic/json.py", line 65, in pydantic.json.pydantic_encoder
TypeError: Object of type 'function' is not JSON serializable
```
I think, that preferable behaviour is to show warnings in situation like that. And that should work without any custom `json_encoders`.


Comments:

Patch:
diff --git a/pydantic/main.py b/pydantic/main.py
--- a/pydantic/main.py
+++ b/pydantic/main.py
@@ -198,7 +198,11 @@ def validate_custom_root_type(fields: Dict[str, ModelField]) -> None:
         raise ValueError('__root__ cannot be mixed with other fields')
 
 
-UNTOUCHED_TYPES = FunctionType, property, type, classmethod, staticmethod
+# Annotated fields can have many types like `str`, `int`, `List[str]`, `Callable`...
+# If a field is of type `Callable`, its default value should be a function and cannot to ignored.
+ANNOTATED_FIELD_UNTOUCHED_TYPES: Tuple[Any, ...] = (property, type, classmethod, staticmethod)
+# When creating a `BaseModel` instance, we bypass all the methods, properties... added to the model
+UNTOUCHED_TYPES: Tuple[Any, ...] = (FunctionType,) + ANNOTATED_FIELD_UNTOUCHED_TYPES
 
 # Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra
 # (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's
@@ -245,7 +249,6 @@ def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901
         class_vars = set()
         if (namespace.get('__module__'), namespace.get('__qualname__')) != ('pydantic.main', 'BaseModel'):
             annotations = resolve_annotations(namespace.get('__annotations__', {}), namespace.get('__module__', None))
-            untouched_types = UNTOUCHED_TYPES + config.keep_untouched
             # annotation only fields need to come first in fields
             for ann_name, ann_type in annotations.items():
                 if is_classvar(ann_type):
@@ -255,14 +258,14 @@ def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901
                     value = namespace.get(ann_name, Undefined)
                     allowed_types = get_args(ann_type) if get_origin(ann_type) is Union else (ann_type,)
                     if (
-                        isinstance(value, untouched_types)
+                        isinstance(value, ANNOTATED_FIELD_UNTOUCHED_TYPES)
                         and ann_type != PyObject
                         and not any(
                             lenient_issubclass(get_origin(allowed_type), Type) for allowed_type in allowed_types
                         )
                     ):
                         continue
-                    fields[ann_name] = inferred = ModelField.infer(
+                    fields[ann_name] = ModelField.infer(
                         name=ann_name,
                         value=value,
                         annotation=ann_type,
@@ -272,6 +275,7 @@ def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901
                 elif ann_name not in namespace and config.underscore_attrs_are_private:
                     private_attributes[ann_name] = PrivateAttr()
 
+            untouched_types = UNTOUCHED_TYPES + config.keep_untouched
             for var_name, value in namespace.items():
                 can_be_changed = var_name not in class_vars and not isinstance(value, untouched_types)
                 if isinstance(value, ModelPrivateAttr):
diff --git a/pydantic/schema.py b/pydantic/schema.py
--- a/pydantic/schema.py
+++ b/pydantic/schema.py
@@ -188,7 +188,12 @@ def get_field_info_schema(field: ModelField) -> Tuple[Dict[str, Any], bool]:
         schema['description'] = field.field_info.description
         schema_overrides = True
 
-    if not field.required and not field.field_info.const and field.default is not None:
+    if (
+        not field.required
+        and not field.field_info.const
+        and field.default is not None
+        and not is_callable_type(field.outer_type_)
+    ):
         schema['default'] = encode_default(field.default)
         schema_overrides = True
 


Test patch:
diff --git a/tests/test_dataclasses.py b/tests/test_dataclasses.py
--- a/tests/test_dataclasses.py
+++ b/tests/test_dataclasses.py
@@ -3,7 +3,7 @@
 from collections.abc import Hashable
 from datetime import datetime
 from pathlib import Path
-from typing import ClassVar, Dict, FrozenSet, List, Optional
+from typing import Callable, ClassVar, Dict, FrozenSet, List, Optional
 
 import pytest
 
@@ -801,6 +801,60 @@ class Config:
         e.item.name = 'pika2'
 
 
+def test_pydantic_callable_field():
+    """pydantic callable fields behaviour should be the same as stdlib dataclass"""
+
+    def foo(arg1, arg2):
+        return arg1, arg2
+
+    def bar(x: int, y: float, z: str) -> bool:
+        return str(x + y) == z
+
+    class PydanticModel(BaseModel):
+        required_callable: Callable
+        required_callable_2: Callable[[int, float, str], bool]
+
+        default_callable: Callable = foo
+        default_callable_2: Callable[[int, float, str], bool] = bar
+
+    @pydantic.dataclasses.dataclass
+    class PydanticDataclass:
+        required_callable: Callable
+        required_callable_2: Callable[[int, float, str], bool]
+
+        default_callable: Callable = foo
+        default_callable_2: Callable[[int, float, str], bool] = bar
+
+    @dataclasses.dataclass
+    class StdlibDataclass:
+        required_callable: Callable
+        required_callable_2: Callable[[int, float, str], bool]
+
+        default_callable: Callable = foo
+        default_callable_2: Callable[[int, float, str], bool] = bar
+
+    pyd_m = PydanticModel(required_callable=foo, required_callable_2=bar)
+    pyd_dc = PydanticDataclass(required_callable=foo, required_callable_2=bar)
+    std_dc = StdlibDataclass(required_callable=foo, required_callable_2=bar)
+
+    assert (
+        pyd_m.required_callable
+        is pyd_m.default_callable
+        is pyd_dc.required_callable
+        is pyd_dc.default_callable
+        is std_dc.required_callable
+        is std_dc.default_callable
+    )
+    assert (
+        pyd_m.required_callable_2
+        is pyd_m.default_callable_2
+        is pyd_dc.required_callable_2
+        is pyd_dc.default_callable_2
+        is std_dc.required_callable_2
+        is std_dc.default_callable_2
+    )
+
+
 def test_pickle_overriden_builtin_dataclass(create_module):
     module = create_module(
         # language=Python
diff --git a/tests/test_schema.py b/tests/test_schema.py
--- a/tests/test_schema.py
+++ b/tests/test_schema.py
@@ -1006,10 +1006,18 @@ class Model(BaseModel):
     }
 
 
-@pytest.mark.parametrize('annotation', [Callable, Callable[[int], int]])
-def test_callable_type(annotation):
+@pytest.mark.parametrize(
+    'type_,default_value',
+    (
+        (Callable, ...),
+        (Callable, lambda x: x),
+        (Callable[[int], int], ...),
+        (Callable[[int], int], lambda x: x),
+    ),
+)
+def test_callable_type(type_, default_value):
     class Model(BaseModel):
-        callback: annotation
+        callback: type_ = default_value
         foo: int
 
     with pytest.warns(UserWarning):