contest-helper 0.1

A flexible framework for generating randomized test cases with input/output validation, designed for competitive programming and algorithmic testing.

Easy Random Test Case Generator Framework

A flexible framework for generating randomized test cases with input/output validation, designed for competitive programming and algorithmic testing.

Features

• Type-safe generators for all basic data types and structures
• Automatic test validation with BadTestException handling
• Customizable I/O formatting for any problem format
• Sample test integration alongside generated cases
• Extensible architecture for custom generators

Installation

pip install contest-helper

Quick Start

from contest_helper import *


# 1. Define your solution with validation
def word_count(text: str) -> int:
    if len(text) > 1000:
        raise BadTestException("Input too long")
    return len(text.split())


# 2. Configure generator
generator = Generator(
    solution=word_count,
    tests_generator=RandomSentence(min_length=1, max_length=20),
    tests_count=10,
    input_parser=lambda lines: ' '.join(lines),
    input_printer=lambda text: [text],
    output_printer=lambda count: [str(count)]
)

# 3. Generate tests
generator.run()

Core Components

Value Generators

Generator	Description	Example
Value	Basic value wrapper	Value(5)
Lambda	Custom generator functions	Lambda(lambda: datetime.now())
RandomValue	Random value from sequence	RandomValue(['red', 'green', 'blue'])
RandomNumber	Numeric ranges	RandomNumber(1, 100)
RandomWord	Random strings	RandomWord()
RandomSentence	Natural language-like text	RandomSentence()
RandomList	Random sequences	RandomList(gen, 5)
RandomSet	Random sequences	RandomSet(gen, 5)
RandomDict	Key-value pairs	RandomDict(kgen, vgen, 3)

Test Validation

def solution(input_data):
    # Validate input before processing
    if is_invalid(input_data):
        raise BadTestException("Validation failed")
    
    # Normal processing...

I/O Configuration

Generator(
    input_parser=lambda lines: parse_custom_format(lines),
    input_printer=lambda obj: format_as_text(obj),
    output_printer=lambda result: [str(result)]
)

Advanced Usage

Dynamic Test Generation

dynamic_gen = RandomDict(
    key_generator=RandomWord(),
    value_generator=RandomList(
        RandomNumber(1, 100),
        length=RandomNumber(2, 5)
    ),
    length=RandomNumber(3, 10)
)

Custom Generators

class RandomGraph(Value[Dict]):
    def __init__(self, node_count: Value[int]):
        self.node_count = node_count
    
    def __call__(self) -> Dict:
        nodes = [f"node{i}" for i in range(self.node_count())]
        return {
            n: random.sample(nodes, k=random.randint(1, len(nodes)))
            for n in nodes
        }

Directory Structure

Generated tests follow this structure:

tests/
├── sample01    # Sample input
├── sample01.a  # Sample output
├── 01          # Generated test input
├── 01.a        # Generated test output
└── ...

Best Practices

1. Validation: Always validate inputs in your solution function
2. Descriptive Messages: Provide clear BadTestException messages
3. Generator Composition: Build complex types from simple generators
4. Test Diversity: Configure generators to produce edge cases
5. Performance: Avoid expensive operations in validation

License

MIT License - Free for commercial and personal use