Toloka 2: https://hackmd.io/@P_oDzqNMQBqjwVAD_Ao-Dw/H1gdSYWcR
Toloka 3: https://hackmd.io/@P_oDzqNMQBqjwVAD_Ao-Dw/Sy1bIKbqR
The instruction is clear, with no ambiguities or context issues, and the required changes are directly related to the functionality of the function.
The instruction does not contain ambiguities or lack of context issues, and the changes required are directly related to the functionality of the function.
```python=
"""Classes for creating configuration groups and namespaces."""
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
from . import compat
# Renaming option to opt to allow option as a variable name.
from . import option as opt
class Namespace(object):
"""A collection of configuration options."""
def __init__(self, description=None, **options):
"""Initialize the Namespace with options
Args:
description (str, optional): A human readable description of what
the Namespace contains.
**options: Each keyword should be an Option object which will be
added to the Namespace.
Raises:
TypeError: If an entry is not an Option object.
"""
self.__doc__ = description
self._options = {}
for name, option in compat.iteritems(options):
self.register(name, option)
super(Namespace, self).__init__()
@property
def description(self):
"""Get the description of what the namespace contains."""
return self.__doc__
def get(self, name, default=None):
"""Fetch an option from the dictionary.
Args:
name (str): The name of the option.
default: The value to return if the name is missing.
Returns:
any: The value stored by the option.
This method resolves the option to its value rather than returning
the option object itself. Use the 'options()' method or this object's
iter to get the raw options.
"""
option = self._options.get(name, None)
if option is None:
return default
return option.__get__(self)
def set(self, name, value):
"""Set an option value.
Args:
name (str): The name of the option.
value: The value to set the option to.
Raises:
AttributeError: If the name is not registered.
TypeError: If the value is not a string or appropriate native type.
ValueError: If the value is a string but cannot be coerced.
"""
if name not in self._options:
raise AttributeError("Option {0} does not exist.".format(name))
return self._options[name].__set__(self, value)
def options(self):
"""Get an iterable of two-tuples containing name and option.
The name in this case is the name given at registration time which is
used to identify an option and look it up on the object. The
option is the actual Option object.
"""
return compat.iteritems(self._options)
def __iter__(self):
"""Proxy iter attempts to the 'options' method."""
return iter(self.options())
def __setattr__(self, name, value):
"""Proxy attribute sets to the 'register' method if needed.
If the value is an option object this call gets proxied to 'register'.
If the value is anything else this method will follow the standard
setattr behaviour unless the target is an option in which case the
method is proxied to 'set'.
"""
if isinstance(value, opt.Option):
return self.register(name, value)
if not hasattr(self, name):
return object.__setattr__(self, name, value)
# The options dictionary may not be set yet if this is getting called
# from the init method. Check for the attribute before accessing it to
# avoid infinite recursion.
if hasattr(self, "_options") and name in self._options:
return self.set(name, value)
return object.__setattr__(self, name, value)
def __getattr__(self, name):
"""Lookup missing attributes in the options dictionary."""
# PY3 'hasattr' behaviour changed to utilize the 'getattr' which causes
# infinite recursion if it is used before the options dictionary is
# created. Lookup up the attribute directly in the instance dictionary
# here to avoid that scenario.
if "_options" not in self.__dict__ or name not in self._options:
raise AttributeError("Option {0} does not exist.".format(name))
return self.get(name)
class AutoNamespace(Namespace):
"""Namespace which automatically defined options of a given type."""
def __init__(self, description=None, type=None, **options):
"""Initialize the Namespace with a type generator.
Args:
description (str, optional): A human readable description of what
the Namespace contains.
type (Option): The Option class to use when generating dynamic
options.
**options: Each keyword should be an Option object which will be
added to the Namespace.
Raises:
ValueError: If type is not given or is not an Option class.
TypeError: If an entry is not an Option object.
"""
super(AutoNamespace, self).__init__(description=description, **options)
self._generator = type
def set(self, name, value):
"""Set an option value.
Args:
name (str): The name of the option.
value: The value to set the option to.
Raises:
TypeError: If the value is not a string or appropriate native type.
ValueError: If the value is a string but cannot be coerced.
If the name is not registered a new option will be created using the
option generator.
"""
if name not in self._options:
self.register(name, self._generator())
return self._options[name].__set__(self, value)
def __setattr__(self, name, value):
"""Proxy attribute sets to the 'register' method if needed.
If the value is an option object this call gets proxied to 'register'.
If the value is anything else this method will follow the standard
setattr behaviour unless the target is an option in which case the
method is proxied to 'set'.
"""
if isinstance(value, opt.Option):
return self.register(name, value)
if not hasattr(self, name):
return object.__setattr__(self, name, value)
# The options dictionary may not be set yet if this is getting called
# from the init method. Check for the attribute before accessing it to
# avoid infinite recursion.
if hasattr(self, "_options") and hasattr(self, "_generator"):
return self.set(name, value)
return object.__setattr__(self, name, value)
def __getattr__(self, name):
"""Lookup missing attributes in the options dictionary."""
# PY3 'hasattr' behaviour changed to utilize the 'getattr' which causes
# infinite recursion if it is used before the options dictionary is
# created. Lookup up the attribute directly in the instance dictionary
# here to avoid that scenario.
if (
"_options" not in self.__dict__
or "_generator" not in self.__dict__
):
raise AttributeError("Attribute {0} does not exist.".format(name))
if name not in self._options:
self._options[name] = self._generator()
return self.get(name)
```
##### User's instruction
I want a function named register that can add new options to the namespace. I want these options to be stored under a specific name provided by me. The function should raise a TypeError if the option isn't of type Option, and a ValueError if the name is already registered. Suppose this function belongs to the class Namespace. Can you provide the function definition for it?
#### Instruction evaluation criteria
Evaluate the user's instruction
✅ Good: none of the below options match
⛔️ Ambiguous: there are multiple interpretations of what the user wants
ℹ️ The instruction may have some ambiguity that could be resolved by common sense like assuming specific constant values
⛔️ Lack of context: ask to use something that is not presented in original code
ℹ️ Adding import of some common library is allowed, it's not lack of context
⛔️ Non-solvable
- The task doesn’t make sense
- The task is clear but it’s impossible to solve it
- What’s asked is already implemented in the source file
### Plan and Code diff
Plan
- Define a function named 'register' that takes two arguments: 'self' and 'name'.
- Inside the function, check if the name is in the '_options' dictionary of the object.
- If the name is in the dictionary, raise a ValueError with a message indicating that the name is already registered.
- If the name is not in the dictionary, add the option to the dictionary with the name as the key.
- Return the object.
ℹ️ A correct plan is one that is both complete and not excessive in relation to the instruction, it has all required steps and doesn’t have redundant ones.
Also:
Should be relevant to the instruction
Should be complete, mentioning all steps needed to solve the problem.
Shouldn’t mention unnecessary steps, i.e. ones without which the working plan still works
Should contain the main steps of the assumed solution
Plan steps should mostly imply code changes. The only allowed exceptions are:
Steps like “ensure that variable X is used consistently…”
Evaluate the plan
✅ Good: meets the requirements
⛔️ Bad: does not meet the requirements
## Model's Response
def addComponent(self, glyphName, transformation):
self.otherPen.addComponent(glyphName, transformation)
### Solution's requirements
Solution requirements
- Solution should be relevant to the instruction and plan in such priority.
- Solution should not include any changes that are not implied by the task such as changing the code style, removing the import, adding the comments in the code locations that are not mentioned in the task.
- Solutions should not be unnecessarily complicated or introduce any redundancy. For example, if the instruction asks for changing some constant like “add 10 more random strings to list X” it should generate a new code with a new definition of the list instead of e.g. generating a new function that does so.
- Solution needs to be executable in a sense that an expert after an eye-check doesn’t see reasons why it may fail to run
Is correct? (Solves the task described in the instruction and doesn’t have unnecessary steps and code looks executable)
✅ Yes
⛔️ No
Clarification of why the solution is incorrect (or the opposite) when the decision is not obvious
# ---------------- Justification:
## ------------ User's Instruction:
The instruction is bad as it doesn't explicitly state all the parameters the function should have. It mentions a name and an option, but doesn't clearly define them. It implies that the instruction left room for interpretation and assumptions.
## ------------ Plan:
The plan is good, it is relevant, complete, and outlines the main solution steps.
## ------------ Solution:
The solution is good because it is concise and meets all of the user's instructions without any unncessary steps.
The code doesn't have any issues.
# Draft:
The instruction is good because it is clear, provides sufficient context, and is feasible to implement based on the provided code.