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chore: import upstream snapshot with attribution
2026-07-13 13:24:08 +08:00

64 lines
2.0 KiB
Python

from typing import Any, Dict, Optional
import pandas as pd
def generate_simple_tests(config: Optional[Dict[str, Any]] = None):
"""Generate a simple set of test cases with optional configuration."""
# Default values
languages = ["Spanish", "French"]
phrases = ["Hello", "Thank you"]
# Override with config if provided
if config:
languages = config.get("languages", languages)
phrases = config.get("phrases", phrases)
test_cases = []
for phrase in phrases:
for lang in languages:
test_case = {
"vars": {"text": phrase, "target_language": lang},
"assert": [
{"type": "regex", "value": ".+"}
], # Just check for non-empty output
"description": f"Translate '{phrase}' to {lang}",
}
test_cases.append(test_case)
return test_cases
def generate_from_csv(config: Optional[Dict[str, Any]] = None):
"""Generate test cases from CSV data with optional row limit."""
# Default test data
data = {
"source_text": ["Good morning", "Thank you", "Goodbye"],
"target_language": ["French", "Spanish", "German"],
"expected_translation": ["Bonjour", "Gracias", "Auf Wiedersehen"],
}
# Use custom data if provided in config
if config and "data" in config:
data = config["data"]
df = pd.DataFrame(data)
# Limit rows if specified in config
if config and "max_rows" in config:
df = df.head(config["max_rows"])
test_cases = []
for _, row in df.iterrows():
test_case = {
"vars": {
"text": row["source_text"],
"target_language": row["target_language"],
},
"assert": [{"type": "contains", "value": row["expected_translation"]}],
"description": f"Translate '{row['source_text']}' to {row['target_language']}",
}
test_cases.append(test_case)
return test_cases