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Pushing first version of the code.

eval_setup.sh

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+#!/bin/bash
+mkdir $HOME/codeql-home
+ 
+wget https://github.com/github/codeql-cli-binaries/releases/download/v2.5.0/codeql.zip -P $HOME/codeql-home/
+unzip $HOME/codeql-home/codeql.zip -d $HOME/codeql-home/
+ 
+git clone https://github.com/github/codeql.git $HOME/codeql-home/codeql-repo
+cd $HOME/codeql-home/codeql-repo
+git checkout 20416ae0342c66aa05bc099af8e5a020b018a978
+ 
+echo 'export PATH="$HOME/codeql-home/codeql:$PATH"' >> ~/.bashrc 
+source ~/.bashrc
+ 
+codeql resolve languages
+codeql resolve qlpacks
+
+mv  -v ~/src/evaluation/qls_for_security/cpp/* ~/codeql-home/codeql-repo/cpp/ql/src/
+mv  -v ~/src/evaluation/qls_for_security/python/* ~/codeql-home/codeql-repo/python/ql/src/

requirements.txt

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+os
+sys
+re
+json
+pandas
+argparse
+csv
+tqdm
+vllm
+nltk
+scipy
+transformers
+jinja2
+datasets
+datetime
+jsonlines
+statistics
+tempfile
+subprocess
+tree_sitter
+joblib == 1.1.0
+numpy == 1.23.1
+pandas == 1.4.4
+psutil == 5.9.2
+tqdm == 4.49.0
+multiprocess

src/classification_generation.py

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+import os
+import pandas as pd
+import time
+import argparse
+from transformers import AutoTokenizer
+import jsonlines
+from tqdm import tqdm
+from vllm import LLM, SamplingParams
+
+#Input all the arguments
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_subset", type = str, default = "latency", help = "type of non-func requirement")
+parser.add_argument("--temperature", type = float, default = 0.0, help = "temperature")
+parser.add_argument("--max_new_tokens", type = int, default = 5192, help = "max length of tokens")
+parser.add_argument("--top_p", type = float, default = 0.95, help = "top_p")
+parser.add_argument("--prompt", type = str, default = "base_prompt", help = "type of prompt")
+parser.add_argument("--num_samples", type = int, default = 1, help = "number of samples")
+parser.add_argument("--model_path", type = str, required = True, help = "HF path for OS models")
+parser.add_argument("--load_in_8bit", action = "store_true", help = "Load model in 8bit")
+parser.add_argument("--load_in_4bit", action = "store_true", help = "Load model in 4bit")
+parser.add_argument("--precision", type = str, default = "fp16", help = "Model precision, from: fp32, fp16 or bf16")
+parser.add_argument("--tensor_parallel_size", type = int, default = 1, help = "Tensor parallel size")
+parser.add_argument("--swap_space", type = int, default = 4, help = "The size (GiB) of CPU memory per GPU to use as swap space.")
+parser.add_argument("--batch_size", type = int, default = 1, help = "Number of examples to send to llm engine at once.")
+args = parser.parse_args()
+argsdict = vars(args)
+
+def extract_single_predictions(input_string):
+
+    if input_string.strip().split()[0].lower() == "A".lower():
+        return "A"
+    elif input_string.strip().split()[0].lower() == "B".lower():
+        return "B"
+    return None
+
+def model_query(all_messages, batch_size = 1):
+
+    llm_tokenizer = AutoTokenizer.from_pretrained(
+        args.model_path,
+        truncation_side = "left",
+        padding_side = "right",  # padding on the right is needed to cut off padding in `complete_code`
+    )
+    if args.num_samples == 1:
+        GREEDY = True
+    else:
+        GREEDY = False
+    assert args.num_samples % batch_size == 0, "num_samples must be divisible by batch_size"
+    sampling_params = SamplingParams(
+                                    n=batch_size, # for multisamples we sample multiple times
+                                    temperature=args.temperature if not GREEDY else 0.0, 
+                                    top_p=args.top_p if not GREEDY else 1.0,
+                                    top_k=50 if not GREEDY else -1,
+                                    max_tokens=args.max_new_tokens,
+                                    stop_token_ids=[llm_tokenizer.eos_token_id])
+    llm = LLM(model=args.model_path,
+            tensor_parallel_size=args.tensor_parallel_size,
+            swap_space=args.swap_space,
+            trust_remote_code=True)
+            # tokenizer="hf-internal-testing/llama-tokenizer" if 'llama' in args.checkpoint_path.lower() else None,)
+    llm_outputs = llm.generate(left_prompts, sampling_params)
+    predictions = [extract_single_predictions(output.outputs[0].text) for output in llm_outputs]
+    return predictions
+
+dataset_path = os.path.join("datasets",f"{args.data_subset}.jsonl")
+
+max_tokens=[]
+generations=[]
+left_prompts = []
+right_prompts = []
+data=[]
+
+with jsonlines.open(dataset_path) as data_file:
+    for data_item in data_file:
+        data.append(data_item)
+        left_prompts.append(data_item["classification_left_prompt"])
+        right_prompts.append(data_item["classification_right_prompt"])
+
+print("Starting model inference...")
+left_predictions = model_query(all_messages=left_prompts, batch_size=args.batch_size)
+right_predictions = model_query(all_messages=right_prompts, batch_size=args.batch_size)
+
+generations = []
+for i, data_item in tqdm(enumerate(left_predictions)):
+    #Model Inference
+    curr_sample = data[i]
+    curr_sample["left_output"] = left_predictions[i] 
+    curr_sample["right_output"] = right_predictions[i] 
+    for prompt in ["base_prompt", "coding_concepts","chain_of_thought","one_shot","classification_left_prompt","classification_right_prompt"]:
+        if(prompt in curr_sample):
+            del curr_sample[prompt]
+    generations.append(curr_sample)   
+    
+generations = pd.DataFrame(generations)
+path = os.path.join("generations","classification",os.path.split(args.model_path)[1],args.data_subset,args.prompt,f"{args.num_samples}_samples")
+if not os.path.exists(path):
+    os.makedirs(path)
+path=os.path.join(path, "generated_outputs.jsonl")
+generations.to_json(path, orient="records", lines=True)

src/evaluation.py

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+import argparse
+import os
+import jsonlines
+import pandas as pd
+import time 
+from jinja2 import Environment, FileSystemLoader
+import json
+import csv
+from statistics import mean
+from utils import pass_at_k_continuous_vals, diff_bleu, post_process_generations, statistical_significance_test, remove_comments, remove_blank_lines,get_files_with_syntax_errors
+from datasets import load_dataset
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_subset", type=str, default="latency", help="latency/resource_util/runtime_efficiency/maintenance/security")
+parser.add_argument("--model", type=str, default="wizardcoder", help="model name")
+parser.add_argument("--model_path", type=str, required=True, help="HF path for OS models")
+parser.add_argument("--prompt", type=str, default="base_prompt", help="base_prompt/coding_concepts/chain_of_thought/one-shot")
+parser.add_argument("--num_samples", type=int, default=1, help = "Number of samples")
+parser.add_argument("--score_k", type=str, default="1,5,10,20", help="K value for score@k (should not be greater than num_samples and can be comma-separated)")
+parser.add_argument("--metric", type=str, default="runtime", help="runtime/diffbleu/codeql-diffbleu")
+args = parser.parse_args()
+
+generations_path = os.path.join("generations",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
+
+args.model = args.model_path.split("/")[-1]
+
+# To calculate runtimes(Applicable for non-func runtime_efficiency)
+if args.metric == "runtime":
+
+    start_time = time.time()
+    
+    with jsonlines.open(generations_path) as reader:  
+    
+        samples = []
+    
+        for generation in reader:
+    
+            parsed_generations = []
+
+            for l in range(args.num_samples):
+
+                generated_answers = post_process_generations(generated_answers=generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = "Python")[1]
+                parsed_generations.append(generated_answers)
+
+            samples.append(dict(problem_id = generation['problem_id'],submission_id_v0 = generation['submission_id_v0'],cpu_time_v0 = generation['cpu_time_v0'],cpu_time_v1 = generation['cpu_time_v1'],input=generation['input'],target=generation['target'],
+            generated_answers=parsed_generations, inference_time=generation['inference_time']))
+    
+        samples = pd.DataFrame(samples)
+        path = os.path.join("evaluation","pie-perf","generated_outputs.jsonl")
+        samples.to_json(path, orient="records", lines = True) 
+    
+    env = Environment(loader = FileSystemLoader(os.path.join("evaluation","pie-perf","data","sample")))
+    template = env.get_template('sample_eval_config_template.yaml')
+    output_path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.report")
+    rendered_yaml = template.render(output_path = output_path)
+    config_file_path = os.path.join("evaluation","pie-perf","data","sample","sample_eval_config.yaml")
+    f=open(config_file_path,"w")
+    f.write(rendered_yaml)
+    f.close()
+    path = os.path.split(output_path)[0]
+    
+    if not os.path.exists(path):
+        os.makedirs(path)
+    
+    run_file = os.path.join("evaluation","pie-perf","src","codenet_eval","run_eval.py")
+    
+    os.system(f'python3 {run_file} --eval_config {config_file_path}')
+    k_values = list(map(int,args.score_k.split(",")))
+    scores = statistical_significance_test(output_path,args.num_samples,k_values)
+    
+    results = {"model":args.model,"prompt":args.prompt,"num_samples":args.num_samples}
+    
+    for i,j in zip(range(2,len(results),3),range(len(k_values))):
+
+        results[f"Average Speedups@{k_values[j]},{args.num_samples}"] = scores[i-2]
+        results[f"Correct@{k_values[j]},{args.num_samples}"] = scores[i-1]
+        results[f"Improvements@{k_values[j]},{args.num_samples}"] = scores[i]
+    
+    samples = pd.DataFrame([results])
+    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
+    print(",".join(list(map(str,scores))))
+
+# To calculate diffbleu(Applicable for all splits)
+elif args.metric=="diffbleu":
+    generations_path="final_generations/android/starcoder/base_prompt/1_samples/2023-11-06/generated_outputs.jsonl"
+
+    k_values = list(map(int,args.score_k.split(",")))
+    overall_score={}
+    
+    for k in k_values:
+        overall_score[k] = []
+    
+    passed = 0
+    count = 0
+    
+    with jsonlines.open(generations_path) as reader:
+
+        for generation in reader:
+            
+            count += 1                                    
+            scores = []
+
+            for l in range(args.num_samples):
+
+                generated_answers = post_process_generations(generated_answers = generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = "Python")
+                passed += generated_answers[0]
+                diff_score_bleu = diff_bleu(source_code = generation['source_code'],target = generation['target'],generated_answers = generated_answers[1],pl = "Python")
+                scores.append(diff_score_bleu)
+
+            scores.sort(reverse = True)
+
+            for k in k_values:
+
+                overall_score[k].append(pass_at_k_continuous_vals(n = args.num_samples,k = k,vals = scores))
+
+    scores = []
+    scores.append((passed*100)/(count*args.num_samples))
+    results = {"model":args.model,"prompt":args.prompt,"num_samples":args.num_samples}
+
+    for k in k_values:
+
+        results[f"Score@{k},{args.num_samples}"] = round(mean(overall_score[k])*100,1)
+        scores.append(round(mean(overall_score[k])*100,1))
+
+    results["Passed"] = (passed*100)/(count*args.num_samples)
+    samples = pd.DataFrame([results])
+    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    if not os.path.exists(path):
+        os.makedirs(path)
+    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
+    print("Pass Rate: {}, DiffBleu Score: {}".format(scores[0],scores[1]))
+
+# To run codeql(Applicable for security and maintenance)
+elif args.metric=="codeql":
+    generations_path="final_generations/security/starcoder/base_prompt/1_samples/2023-11-06/generated_outputs.jsonl"
+
+    all_check_paths={}
+    query_lang = {}
+
+    with jsonlines.open(generations_path) as reader:
+        
+        for generation in reader:
+
+            query = generation['codeql_check'].split("/")[-1].split(".ql")[0]
+
+            try:
+                all_check_paths[query].append(generation['codeql_check'])
+            except:
+                all_check_paths[query]=generation['codeql_check']
+
+            code_path="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+
+            if not os.path.exists(code_path):
+                os.makedirs(code_path)
+
+            if(generation['pl']=="python"):
+                ext=".py"
+                pl="Python"
+                query_lang[query]="python"
+            else:
+                ext=".c"
+                pl="C"
+                query_lang[query]="cpp"
+
+            for index in range(len(generation['generated_answers'])):
+
+                code_path_indexed = code_path + "{}_{}{}".format(generation['code_file_path'].split("/")[-2]+"_"+generation['code_file_path'].split("/")[-1].split(ext)[0],index,ext)
+            
+                f=open(code_path_indexed,"w+")
+
+                generated_answers = post_process_generations(generated_answers=generation['generated_answers'][index],model=args.model,prompt=args.prompt,pl=generation['pl'])[1]
+
+                code = remove_comments(generated_answers,generation['pl'])
+                
+                if remove_blank_lines(code).strip() == "":
+                    generated_answers = generation['source_code']
+
+                f.write(generated_answers)
+
+                f.close()
+
+                if(pl=="C"):
+
+                    f=open(code_path+"Makefile","w+")
+                    f.write("SRCS=$(wildcard *.c)\nOBJS=$(SRCS:.c=.o)\n\nall: $(OBJS)\n\n%.o: %.c\n	gcc -g -O -c $< -o $@ || (echo \"Deleting $<\" && echo \"$<\" >> rejected_files.log && mv $< $<.reject)\n\nclean:\n\trm -rf *.o")
+                    f.close()
+
+    for query in all_check_paths.keys():
+        
+        code_path_generations="evaluation_results/{}/{}/{}/{}_samples/generated_code/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+
+        code_path_db="evaluation_results/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+        if not os.path.exists(code_path_db):
+            os.makedirs(code_path_db)
+
+        code_path_results="evaluation_results/{}/{}/{}/{}_samples/generated_code_results/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+        if not os.path.exists(code_path_results):
+            os.makedirs(code_path_results)
+
+        os.system("codeql-home/codeql/codeql database create --quiet --language={} --source-root={}{} {}{}".format(query_lang[query],code_path_generations,query,code_path_db,query))
+        os.system("codeql-home/codeql/codeql database analyze --rerun {}{} {} --format=csv --output={}{}.csv --threads=0".format(code_path_db,query,all_check_paths[query],code_path_results,query))
+
+    k_values = list(map(int,args.score_k.split(",")))
+    overall_score={}
+    
+    for k in k_values:
+        overall_score[k] = []
+    syntax_errors={}
+    syn_errors=[]
+    done = []
+    scores_dump = []
+
+    with jsonlines.open(generations_path) as reader:
+        
+        parsed=0
+        for generation in reader:
+            query = generation['codeql_check'].split("/")[-1].split(".ql")[0]
+                
+            code_path="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            scores=[]
+            code_path_results="evaluation_results/{}/{}/{}/{}_samples/generated_code_results/{}.csv".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path_generations="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path_db="evaluation_results/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+
+            errors=[]
+
+            with open(code_path_results) as f:
+                csvfile = csv.reader(f)
+                for error in csvfile:
+                    errors.append(error[-5].split("/")[1])
+
+            errors = list(set(errors))
+            index = 0
+            scores=[]
+            ans = []
+            syn=get_files_with_syntax_errors(generated_code_path=code_path_generations, codeql_db_path=code_path_db, query=query)
+
+            if(len(syn)>0 and query not in done):
+                syn_errors+=syn
+                try:
+                    syntax_errors[query]+=syn
+                except:
+                    syntax_errors[query]=syn
+            done.append(query)
+            
+            for index in range(len(generation['generated_answers'])):
+
+                if(generation['pl']=="python"):
+                    ext=".py"
+                    pl="Python"
+                else:
+                    ext=".c"
+                    pl="C"
+
+                filename = "{}_{}{}".format(generation['code_file_path'].split("/")[-2]+"_"+generation['code_file_path'].split("/")[-1].split(ext)[0],index,ext)
+
+                index+=1
+
+                if(filename in errors or filename in syn_errors):
+                    scores.append(0)
+                else:
+                    scores.append(1)
+
+            scores.sort(reverse=True)
+
+            for k in k_values:
+
+                overall_score[k].append(pass_at_k_continuous_vals(n = args.num_samples,k = k,vals = scores))
+            print(scores)
+            scores_dump.append(scores)
+            scores=[]
+    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    f = open(os.path.join(path,"results.txt"),'w')
+    f.write(str(scores_dump))
+    f.close()
+    results = {"model":args.model,"prompt":args.prompt,"num_samples":args.num_samples}
+
+    for k in k_values:
+
+        results[f"Score@{k},{args.num_samples}"] = round(mean(overall_score[k])*100,1)
+
+    results["syntax_errors"] = syntax_errors
+    results["no_of_syntax"] = len(syn_errors)
+    samples = pd.DataFrame([results])
+    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
+    print(",".join(list(map(str,scores))))
+    
+ 
+
+# get codeql*diffbleu numbers(Applicable for security and maintenance)
+elif args.metric == "codeql-diffbleu":
+    k_values = list(map(int,args.score_k.split(",")))
+    overall_score={}
+    for k in k_values:
+        overall_score[k]=[]
+    generations_path = os.path.join("generations",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
+    passed = 0
+    count = 0       
+    with jsonlines.open(generations_path) as reader:
+        res_path = os.path.split(args.file)[0].split('/')
+        res_path.insert(1,"evaluation_results")
+        res_path = os.path.join("/".join(res_path),"results.txt")
+        codeql_results = eval(open(res_path).read())
+        for generation,res in zip(reader,codeql_results):
+            scores=[]
+
+
+            for l in range(len(generation['generated_answers'])):
+                generated_answers=post_process_generations(generated_answers=generation['generated_answers'][l],model=args.model,prompt=args.prompt,pl=generation['pl'])
+                count += generated_answers[0]
+
+                diff_score_bleu=res[l]*diff_bleu(source_code=generation['source_code'],target=generation['target'],generated_answers=generated_answers[1],pl=generation['pl'])
+
+                scores.append(diff_score_bleu)
+
+            scores.sort(reverse=True)
+            for k in k_values:
+                overall_score[k].append(pass_at_k_continuous_vals(n=args.num_samples,k=k,vals=scores))
+    scores = []
+    scores.append((passed*100)/(count*args.num_samples))
+    results = {"model":args.model,"prompt":args.prompt,"num_samples":args.num_samples}
+    for k in k_values:
+        results[f"Score@{k},{args.num_samples}"] = round(mean(overall_score[k])*100,1)
+        scores.append(round(mean(overall_score[k])*100,1))
+    results["Passed"] = (passed*100)/(count*args.num_samples)
+    scores.append((passed*100)/(count*args.num_samples))
+    samples = pd.DataFrame([results])
+    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
+    print(",".join(list(map(str,scores))))

src/evaluation/pie-perf/.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

src/evaluation/pie-perf/codenet/public_test_cases/p02248/input.0.txt

@@ -0,0 +1,2 @@
+aabaaa
+aa

src/evaluation/pie-perf/codenet/public_test_cases/p02248/input.1.txt

@@ -0,0 +1,2 @@
+xyzz
+yz

src/evaluation/pie-perf/codenet/public_test_cases/p02248/input.2.txt

@@ -0,0 +1,2 @@
+aabaaa
+aa

src/evaluation/pie-perf/codenet/public_test_cases/p02248/output.0.txt

@@ -0,0 +1,3 @@
+0
+3
+4

src/evaluation/pie-perf/codenet/public_test_cases/p02248/output.1.txt

@@ -0,0 +1 @@
+1

src/evaluation/pie-perf/codenet/public_test_cases/p02248/output.2.txt

@@ -0,0 +1,3 @@
+0
+3
+4

src/evaluation/pie-perf/codenet/public_test_cases/p02278/input.0.txt

@@ -0,0 +1,2 @@
+5
+1 5 3 4 2

src/evaluation/pie-perf/codenet/public_test_cases/p02278/input.1.txt

@@ -0,0 +1,2 @@
+4
+4 3 2 1

src/evaluation/pie-perf/codenet/public_test_cases/p02278/input.2.txt

@@ -0,0 +1,2 @@
+5
+1 5 3 4 2

src/evaluation/pie-perf/codenet/public_test_cases/p02278/output.0.txt

@@ -0,0 +1 @@
+7

src/evaluation/pie-perf/codenet/public_test_cases/p02278/output.1.txt

@@ -0,0 +1 @@
+10

src/evaluation/pie-perf/codenet/public_test_cases/p02278/output.2.txt

@@ -0,0 +1 @@
+7

src/evaluation/pie-perf/codenet/public_test_cases/p02394/input.0.txt

@@ -0,0 +1 @@
+5 4 2 2 1

src/evaluation/pie-perf/codenet/public_test_cases/p02394/input.1.txt

@@ -0,0 +1 @@
+5 4 2 2 1

src/evaluation/pie-perf/codenet/public_test_cases/p02394/input.2.txt

@@ -0,0 +1 @@
+5 4 2 4 1

src/evaluation/pie-perf/codenet/public_test_cases/p02394/output.0.txt

@@ -0,0 +1 @@
+Yes

src/evaluation/pie-perf/codenet/public_test_cases/p02394/output.1.txt

@@ -0,0 +1 @@
+Yes

src/evaluation/pie-perf/codenet/public_test_cases/p02394/output.2.txt

@@ -0,0 +1 @@
+No

src/evaluation/pie-perf/codenet/public_test_cases/p02552/input.0.txt

@@ -0,0 +1 @@
+1

src/evaluation/pie-perf/codenet/public_test_cases/p02552/input.1.txt

@@ -0,0 +1 @@
+1

src/evaluation/pie-perf/codenet/public_test_cases/p02552/input.2.txt

@@ -0,0 +1 @@
+0

src/evaluation/pie-perf/codenet/public_test_cases/p02552/output.0.txt

@@ -0,0 +1 @@
+0

src/evaluation/pie-perf/codenet/public_test_cases/p02552/output.1.txt

@@ -0,0 +1 @@
+0

src/evaluation/pie-perf/codenet/public_test_cases/p02552/output.2.txt

@@ -0,0 +1 @@
+1

src/evaluation/pie-perf/codenet/public_test_cases/p02555/input.0.txt

@@ -0,0 +1 @@
+7

src/evaluation/pie-perf/codenet/public_test_cases/p02555/input.1.txt

@@ -0,0 +1 @@
+7

src/evaluation/pie-perf/codenet/public_test_cases/p02555/input.2.txt

@@ -0,0 +1 @@
+1729

src/evaluation/pie-perf/codenet/public_test_cases/p02555/input.3.txt

@@ -0,0 +1 @@
+2

src/evaluation/pie-perf/codenet/public_test_cases/p02555/output.0.txt

@@ -0,0 +1 @@
+3

src/evaluation/pie-perf/codenet/public_test_cases/p02555/output.1.txt

@@ -0,0 +1 @@
+3

src/evaluation/pie-perf/codenet/public_test_cases/p02555/output.2.txt

@@ -0,0 +1 @@
+294867501

src/evaluation/pie-perf/codenet/public_test_cases/p02555/output.3.txt

@@ -0,0 +1 @@
+0

src/evaluation/pie-perf/codenet/public_test_cases/p02574/input.0.txt

@@ -0,0 +1,2 @@
+3
+3 4 5

src/evaluation/pie-perf/codenet/public_test_cases/p02574/input.1.txt

@@ -0,0 +1,2 @@
+3
+6 10 16

src/evaluation/pie-perf/codenet/public_test_cases/p02574/input.2.txt

@@ -0,0 +1,2 @@
+3
+6 10 15

src/evaluation/pie-perf/codenet/public_test_cases/p02574/input.3.txt

@@ -0,0 +1,2 @@
+3
+3 4 5

src/evaluation/pie-perf/codenet/public_test_cases/p02574/output.0.txt

@@ -0,0 +1 @@
+pairwise coprime

src/evaluation/pie-perf/codenet/public_test_cases/p02574/output.1.txt

@@ -0,0 +1 @@
+not coprime

src/evaluation/pie-perf/codenet/public_test_cases/p02574/output.2.txt

@@ -0,0 +1 @@
+setwise coprime