chore: import upstream snapshot with attribution

rdagent/components/coder/data_science/workflow/eval_tests/submission_format_test.txt

@@ -0,0 +1,77 @@
+from pathlib import Path
+import pandas as pd
+import hashlib
+
+def calculate_md5(file_path):
+    with open(file_path, "rb") as f:
+        file_hash = hashlib.md5(f.read()).hexdigest()
+    return file_hash
+
+file_md5 = calculate_md5("scores.csv")
+
+"""
+find . | grep -i sample | grep -i submission | grep -v sample_submission.csv | grep -v zip_files  | grep -v 'sample/'
+./denoising-dirty-documents/sampleSubmission.csv
+./the-icml-2013-whale-challenge-right-whale-redux/sampleSubmission.csv
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv.zip
+./text-normalization-challenge-russian-language/ru_sample_submission_2.csv
+./random-acts-of-pizza/sampleSubmission.csv
+./text-normalization-challenge-english-language/en_sample_submission_2.csv.zip
+./text-normalization-challenge-english-language/en_sample_submission_2.csv
+./detecting-insults-in-social-commentary/sample_submission_null.csv
+"""
+
+# Find sample submission file dynamically
+input_dir = Path("{% include "scenarios.data_science.share:scen.input_path" %}")
+# Look for common variations of sample submission filenames
+sample_submission_files = list(input_dir.glob("*sample_submission*.csv")) + \
+                         list(input_dir.glob("*sampleSubmission*.csv"))
+
+assert sample_submission_files, "Error: No sample submission file found in {% include "scenarios.data_science.share:scen.input_path" %}"
+
+# Use first matching file
+sample_submission_name = sample_submission_files[0].name
+SAMPLE_SUBMISSION_PATH = str(sample_submission_files[0])
+print(f"Using sample submission file: {sample_submission_name}")
+
+# Check if the sample submission file exists
+assert Path(SAMPLE_SUBMISSION_PATH).exists(), f"Error: {sample_submission_name} not found at {SAMPLE_SUBMISSION_PATH}"
+
+# Check if our submission file exists
+assert Path('submission.csv').exists(), "Error: submission.csv not found"
+
+sample_submission = pd.read_csv(SAMPLE_SUBMISSION_PATH)
+our_submission = pd.read_csv('submission.csv')
+
+success = True
+# Print the columns of the sample submission file
+print(f"Columns in {sample_submission_name}:", sample_submission.columns)
+print("Columns in our_submission.csv:", our_submission.columns)
+
+for col in sample_submission.columns:
+    if col not in our_submission.columns:
+        success = False
+        print(f'Column {col} not found in submission.csv')
+
+if success:
+    print(f'submission.csv\'s columns aligns with {sample_submission_name} .')
+
+
+# Print the first 5 rows of the two submission files, with columns separated by commas.
+def print_first_rows(file_path, file_name, num_rows=5):
+    print(f"\nFirst {num_rows} rows of {file_name}:")
+    try:
+        with open(file_path, 'r') as file:
+            for i, line in enumerate(file):
+                if i < num_rows:
+                    print(line.strip())
+                else:
+                    break
+    except FileNotFoundError:
+        print(f"Error: {file_name} not found.")
+
+print_first_rows(SAMPLE_SUBMISSION_PATH, sample_submission_name)
+print_first_rows('submission.csv', 'submission.csv')
+
+assert calculate_md5("scores.csv") == file_md5, "scores.csv should not be rewritten"
+print(f"\nPlease Checked the content of the submission file(submission.csv should align with {sample_submission_name}). ")