added deepseekv2

Bigcode-Evaluation-Harness-240327/makefile

+# There are two dockerfiles: for all benchmarks, and for MultiPL-E
+DOCKERFILE=Dockerfile
+
+ifeq ($(DOCKERFILE), Dockerfile)
+	IMAGE_NAME=evaluation-harness
+else
+	IMAGE_NAME=evaluation-harness-multiple
+endif
+
+build:
+	docker build -f $(DOCKERFILE) -t $(IMAGE_NAME) .
+
+test:
+	docker run -v $(CURDIR)/tests/docker_test/test_generations.json:/app/test_generations.json:ro \
+	-it $(IMAGE_NAME) python3 main.py --model dummy_model --tasks humaneval --limit 4 \
+	--load_generations_path /app/test_generations.json --allow_code_execution 
+
+	@echo "If pass@1 is 0.25 then your configuration for standard benchmarks is correct"
+
+all: build test
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/requirements.txt

+transformers>=4.25.1
+accelerate>=0.13.2
+datasets>=2.6.1
+evaluate>=0.3.0
+pyext==0.7
+mosestokenizer==1.0.0
+huggingface_hub>=0.11.1
+fsspec<2023.10.0

Bigcode-Evaluation-Harness-240327/setup.py

+from setuptools import setup, find_packages
+
+with open("README.md") as readme_file:
+    readme = readme_file.read()
+
+with open("requirements.txt") as reqs_file:
+    requirements = reqs_file.read().split("\n")
+
+ds1000_requirements = [
+    "DateTime==4.7",
+    "gensim==4.2.0",
+    "matplotlib==3.5.2",
+    "numpy==1.21.6",
+    "openai==0.23.0",
+    "pandas==1.3.5",
+    "pandas-datareader==0.10.0",
+    "pathlib==1.0.1",
+    "scikit-learn==1.0.2",
+    "scipy==1.7.3",
+    "seaborn==0.11.2",
+    "statsmodels==0.13.2",
+    "tensorflow==2.10.0",
+    "tokenizers==0.12.1",
+    "torchvision==0.13.1",
+    "tqdm==4.64.1",
+    "xgboost==1.6.2",
+    "Pillow==9.2.0",
+]
+
+setup(
+    description="A framework for the evaluation of autoregressive code generation language models.",
+    long_description=readme,
+    license="Apache 2.0",
+    packages=find_packages() ,
+    install_requires=requirements,
+    extras_require={"ds1000": ds1000_requirements},
+)

Bigcode-Evaluation-Harness-240327/templates/new_task.py

+# This template file is adapted from: https://github.com/EleutherAI/lm-evaluation-harness/blob/master/templates/new_task.py
+
+# TODO: Remove all TODO comments once the implementation is complete.
+"""
+TODO: Add the Paper Title on this line.
+TODO: Add the paper's PDF URL (preferably from arXiv) on this line.
+TODO: Write a Short Description of the task.
+Homepage: TODO: Add the URL to the task's Homepage here.
+"""
+from bigcode_eval.base import Task
+
+# TODO: Add the BibTeX citation for the task.
+_CITATION = """
+"""
+
+
+# TODO: Replace `NewTask` with the name of your Task.
+class NewTask(Task):
+    # TODO: Add the `DATASET_PATH` string. This will be the name of the `Task`
+    # dataset as denoted in HuggingFace `datasets`.
+    DATASET_PATH = ""
+    # TODO: Add the `DATASET_NAME` string. This is the name of a subset within
+    # `DATASET_PATH`. If there aren't specific subsets you need, leave this as `None`.
+    DATASET_NAME = None
+
+    def __init__(self):
+        super().__init__(
+            # TODO: Specify the list of stop words in `stop_words` for the code generation task \
+            # and if the evaluation requires executing the generated code in `requires_execution`.
+            stop_words=[],
+            requires_execution=False,
+        )
+
+    def get_dataset(self):
+        # TODO: retrieve the evaluation subset from the loaded dataset (e.g. `self.dataset["test"]`)
+        """Returns dataset for the task or an iterable of any object, that get_prompt can handle"""
+        return []
+
+    def fewshot_examples(self):
+        # TODO: load few-shot examples (from bigcode_eval/tasks/fewshot_examples) if they exist
+        """Loads and returns the few-shot examples for the task if they exist."""
+        pass
+
+    def get_prompt(self, doc):
+        # TODO: build the prompt for the language model from a sample `doc` from the dataset
+        """
+        Builds the prompt for the LM to generate from.
+        :param doc: dict[str: str]
+            sample from the test dataset
+        :return: str
+        """
+        return ""
+
+    def get_reference(self, doc):
+        # TODO: get the reference solution from a sample `doc` from the dataset
+        """
+        Builds the reference solution for the doc (sample from the test dataset).
+        :param doc: dict[str: str]
+            sample from the test dataset
+        :return: str
+        """
+        return ""
+
+    def postprocess_generation(self, generation, idx):
+        # TODO: define the postprocessing for the LM generation
+        """
+        Defines the postprocessing for a LM generation.
+        :param generation: str
+            code generation from LM
+        :param idx: int (if needed)
+            index of doc in the dataset to which the generation belongs
+        :return: str
+        """
+        return ""
+
+    def process_results(self, generations, references):
+        # TODO: define how the evaluation score is computed from list of \
+        # generations and reference solutions
+        """
+        Takes the list of LM generations and evaluates them against ground truth references,
+        returning the metric for the generations as in {"metric_name": result}.
+        We encourage to directly load the metric from `evaluate` library to keep the code concise.
+        :param generations: list(list(str))
+            list of lists containing generations
+        :param references: list(str)
+            list of str containing refrences
+        :return: dict[str: float]
+        """
+        return {}

Bigcode-Evaluation-Harness-240327/tests/data/humaneval_eval_gens.json

+[["from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "This is some random text"], ["This is some random text", "This is some random text"]]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/humaneval_gen_gens.json

+[["from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\"jectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectjectject through through through through through through through through through through through through through through through through through through through through through through through through through through"], ["from typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n    separate those group into separate strings and return the list of those.\n    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n    Ignore any spaces in the input string.\n    >>> separate_paren_groups('( ) (( )) (( )( ))')\n    ['()', '(())', '(()())']\n    \"\"\" at at at at at at at at at at at at at at at at at at at at at at at fe fe fe fe fe fe fe fe fe fe fe fe"]]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/humaneval_gen_refs.json

+["\n\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n\ncheck(has_close_elements)", "\n\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate('(()()) ((())) () ((())()())') == [\n        '(()())', '((()))', '()', '((())()())'\n    ]\n    assert candidate('() (()) ((())) (((())))') == [\n        '()', '(())', '((()))', '(((())))'\n    ]\n    assert candidate('(()(())((())))') == [\n        '(()(())((())))'\n    ]\n    assert candidate('( ) (( )) (( )( ))') == ['()', '(())', '(()())']\n\ncheck(separate_paren_groups)"]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/mbpp_eval_gens.json

+[["def remove_Occ(s,ch): \r\n    for i in range(len(s)): \r\n        if (s[i] == ch): \r\n            s = s[0 : i] + s[i + 1:] \r\n            break\r\n    for i in range(len(s) - 1,-1,-1):  \r\n        if (s[i] == ch): \r\n            s = s[0 : i] + s[i + 1:] \r\n            break\r\n    return s ", "This is some random text"], ["This is some random text", "This is some random text"]]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/mbpp_gen_gens.json

+[["\"\"\"\nWrite a python function to remove first and last occurrence of a given character from the string.\nassert remove_Occ(\"hello\",\"l\") == \"heo\"\n\"\"\"\normormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormorm only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only only"], ["\"\"\"\nWrite a function to sort a given matrix in ascending order according to the sum of its rows.\nassert sort_matrix([[1, 2, 3], [2, 4, 5], [1, 1, 1]])==[[1, 1, 1], [1, 2, 3], [2, 4, 5]]\n\"\"\"\n at at at at at at feiririririririririririririrherherherherher who who who who who who who who who who who who who who who who fe fe fe fe fe fe fe fejjjjjjjjjjjjjjjjjjjjjififififififififififififififiriririririririrGGGGGGGedededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededededed"]]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/mbpp_gen_refs.json

+["assert remove_Occ(\"hello\",\"l\") == \"heo\"\nassert remove_Occ(\"abcda\",\"a\") == \"bcd\"\nassert remove_Occ(\"PHP\",\"P\") == \"H\"", "assert sort_matrix([[1, 2, 3], [2, 4, 5], [1, 1, 1]])==[[1, 1, 1], [1, 2, 3], [2, 4, 5]]\nassert sort_matrix([[1, 2, 3], [-2, 4, -5], [1, -1, 1]])==[[-2, 4, -5], [1, -1, 1], [1, 2, 3]]\nassert sort_matrix([[5,8,9],[6,4,3],[2,1,4]])==[[2, 1, 4], [6, 4, 3], [5, 8, 9]]"]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/pal-gsm8k-greedy_eval_gens.json

+[
+    [
+        "def solution():\n    \"\"\"Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\"\"\"\n    eggs_per_day = 16\n    eggs_eaten = 3\n    eggs_baked = 4\n    eggs_sold = eggs_per_day - eggs_eaten - eggs_baked\n    price_per_egg = 2\n    money_made = eggs_sold * price_per_egg\n    result = money_made\n    return result\nprint(solution())"
+    ],
+    [
+        "def solution():\n    \"\"\"A robe takes 2 bolts of blue fiber and half that much white fiber.  How many bolts in total does it take?\"\"\"\n    blue_fiber = 2\n    white_fiber = blue_fiber / 2\n    total_fiber = blue_fiber + white_fiber\n    result = total_fiber\n    return result\nprint(solution())"
+    ]
+]
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/data/pal-gsm8k-greedy_prompt.json

+{
+    "prompt":"Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\"\"\"\n    money_initial = 23\n    bagels = 5\n    bagel_cost = 3\n    money_spent = bagels * bagel_cost\n    money_left = money_initial - money_spent\n    result = money_left\n    return result\n\n\n\n\n\nQ: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\"\"\"\n    golf_balls_initial = 58\n    golf_balls_lost_tuesday = 23\n    golf_balls_lost_wednesday = 2\n    golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday\n    result = golf_balls_left\n    return result\n\n\n\n\n\nQ: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\"\"\"\n    computers_initial = 9\n    computers_per_day = 5\n    num_days = 4  # 4 days between monday and thursday\n    computers_added = computers_per_day * num_days\n    computers_total = computers_initial + computers_added\n    result = computers_total\n    return result\n\n\n\n\n\nQ: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\"\"\"\n    toys_initial = 5\n    mom_toys = 2\n    dad_toys = 2\n    total_received = mom_toys + dad_toys\n    total_toys = toys_initial + total_received\n    result = total_toys\n    return result\n\n\n\n\n\nQ: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\"\"\"\n    jason_lollipops_initial = 20\n    jason_lollipops_after = 12\n    denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n    result = denny_lollipops\n    return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\"\"\"\n    leah_chocolates = 32\n    sister_chocolates = 42\n    total_chocolates = leah_chocolates + sister_chocolates\n    chocolates_eaten = 35\n    chocolates_left = total_chocolates - chocolates_eaten\n    result = chocolates_left\n    return result\n\n\n\n\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\"\"\"\n    cars_initial = 3\n    cars_arrived = 2\n    total_cars = cars_initial + cars_arrived\n    result = total_cars\n    return result\n\n\n\n\n\nQ: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\n\n# solution in Python:\n\n\ndef solution():\n    \"\"\"There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\"\"\"\n    trees_initial = 15\n    trees_after = 21\n    trees_added = trees_after - trees_initial\n    result = trees_added\n    return result\n\n\n\n\n\nQ: test\n\n# solution in Python:\n\n\n"
+}
\ No newline at end of file

Bigcode-Evaluation-Harness-240327/tests/test_generation_evaluation.py

+import json
+import os
+import tempfile
+
+from accelerate import Accelerator
+from accelerate.utils import write_basic_config
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+from bigcode_eval.arguments import EvalArguments
+from bigcode_eval.evaluator import Evaluator
+
+# TODO add more tasks
+
+# Tasks for generation test
+GEN_TASKS = ["humaneval", "mbpp"]
+# Tasks for evaluator tests
+EVAL_TASKS = ["humaneval", "mbpp", "pal-gsm8k-greedy"]
+TMPDIR = tempfile.mkdtemp()
+TEST_MODEL = "hf-internal-testing/tiny-random-gpt2"
+REF_EVAL_SCORES = {
+    "humaneval": {"pass@1": 0.25},
+    "mbpp": {"pass@1": 0.25},
+    "pal-gsm8k-greedy": {"accuracy": 1.0, "num_failed_execution": 0},
+}
+
+
+def update_args(args):
+    args.model = "hf-internal-testing/tiny-random-gpt2"
+    # the executed code for the tests is safe (see tests/data/*_eval_gens.json)
+    args.allow_code_execution = True
+    args.save_generations = False
+    args.save_generations_path = ""
+    args.save_references = False
+    args.save_references_path = ""
+    args.metric_output_path = TMPDIR
+    args.load_generations_path = None
+    args.generation_only = False
+    args.check_references = False
+    # postprocessing for HumanEval and MBPP makes generations
+    # with dummy model not distinctive
+    args.postprocess = False
+    args.instruction_tokens = None
+
+    args.limit = 2
+    args.limit_start = 0
+    args.batch_size = 1
+    args.max_length_generation = 300
+    args.left_padding = False
+    args.do_sample = False
+    args.top_p = 0
+    args.n_samples = 1
+    args.seed = 0
+    args.prompt = None
+    args.precision = None
+    args.modeltype = None
+    args.max_memory_per_gpu = None
+    return args
+
+
+def setup():
+    model = AutoModelForCausalLM.from_pretrained(TEST_MODEL)
+    tokenizer = AutoTokenizer.from_pretrained(TEST_MODEL)
+    tokenizer.pad_token = tokenizer.eos_token
+    configPath = os.path.join(TMPDIR, "default_config.yml")
+    write_basic_config(save_location=configPath)
+    accelerator = Accelerator()
+    return model, tokenizer, accelerator
+
+
+def load_generation_examples(task):
+    # generations for testing the generation feature of dummy test model
+    with open(f"tests/data/{task}_gen_gens.json") as fp:
+        gens = json.load(fp)
+    with open(f"tests/data/{task}_gen_refs.json") as fp:
+        refs = json.load(fp)
+    return gens, refs
+
+
+args = update_args(EvalArguments())
+set_seed(args.seed)
+model, tokenizer, accelerator = setup()
+
+
+def test_generation():
+    args.generation_only = True
+    args.save_every_k_tasks = -1
+    evaluator = Evaluator(accelerator, model, tokenizer, args)
+    for task in GEN_TASKS:
+        print(f"testing task {task}")
+        generations, references = evaluator.generate_text(task)
+        true_gens, true_refs = load_generation_examples(task)
+        assert generations == true_gens
+        assert references == true_refs
+    print("passed gen")
+
+
+def test_evaluation():
+    # TODO add scores for each task
+    args.n_samples = 2
+    args.save_every_k_tasks = -1
+    for task in EVAL_TASKS:
+        print(f"testing task {task}")
+        # path to generation examples to evaluate
+        args.load_generations_path = f"tests/data/{task}_eval_gens.json"
+        evaluator = Evaluator(accelerator, None, None, args)
+        results = evaluator.evaluate(task)
+        assert results == REF_EVAL_SCORES[task]
+    print("passed eval")

Bigcode-Evaluation-Harness-240327/tests/test_prompts.py

+import json
+
+from bigcode_eval import tasks
+
+TASKS = ["pal-gsm8k-greedy"]
+
+sample_doc = {"pal-gsm8k-greedy": {"question": "test"}}
+
+
+def load_reference_prompt(task_name):
+    with open(f"tests/data/{task_name}_prompt.json") as fp:
+        prompts = json.load(fp)
+    return prompts["prompt"]
+
+
+def test_gsm_prompt():
+    for task_name in TASKS:
+        task = tasks.get_task(task_name)
+        task_prompt = task.get_prompt(sample_doc[task_name])
+        ref_prompt = load_reference_prompt(task_name)
+        assert task_prompt == ref_prompt

Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+COPY requirements.txt requirements.txt
+RUN source /opt/dtk-24.04/env.sh
+RUN cp /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' >/etc/timezone 
+ENV LANG C.UTF-8
+RUN pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

LICENSE

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
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LM-Evaluation-Harness-240310/.coveragerc

+[run]
+
+# tasks that aren't wired up.
+omit =
+    lm_eval/tasks/quac.py
+    lm_eval/tasks/storycloze.py
+    lm_eval/tasks/cbt.py
+    lm_eval/tasks/sat.py
+    lm_eval/tasks/triviaqa.py
+    lm_eval/tasks/naturalqs.py
+    lm_eval/models/dummy.py
+
+[report]
+exclude_lines =
+    # Skip any pass lines such as may be used for @abstractmethod
+    pass
+
+    # Have to re-enable the standard pragma
+    pragma: no cover
+
+    # Don't complain about missing debug-only code:
+    def __repr__
+    if self\.debug
+
+    # Don't complain if tests don't hit defensive assertion code:
+    raise AssertionError
+    raise NotImplementedError
+    return NotImplemented

LM-Evaluation-Harness-240310/.flake8

+[flake8]
+ignore = E203, E266, E501, W503, F403, F401, C901
+max-line-length = 127
+max-complexity = 10
+select = B,C,E,F,W,T4,B9

LM-Evaluation-Harness-240310/.github/workflows/new_tasks.yml

+name: Tasks Modified
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    branches:
+      - 'main'
+  workflow_dispatch:
+# comment/edit out the above to stop/change the triggers
+jobs:
+  changed_files:
+    runs-on: ubuntu-latest  # windows-latest || macos-latest
+    timeout-minutes: 120
+    name: Scan for changed tasks
+    steps:
+      - name: checkout
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2  # OR "2" -> To retrieve the preceding commit.
+
+      # Uses the tj-actions/changed-files@v37 action to check for changes.
+      # Outputs provided here: https://github.com/tj-actions/changed-files#outputs
+      # The `files_yaml` input optionally takes a yaml string to specify filters,
+      # and prepends the filter name to the standard output names.
+      - name: Check task folders
+        id: changed-tasks
+        uses: tj-actions/changed-files@v37.1.2
+        with:
+          # tasks checks the tasks folder and api checks the api folder for changes
+          files_yaml: |
+            tasks:
+              - lm_eval/tasks/**
+            api:
+              - lm_eval/api/**
+          write_output_files: true
+
+    # The next step is optional; the files are written to the workspace by default (above).
+    # so it's just for debugging
+      - name: Run Tests
+        if: steps.changed-tasks.outputs.tasks_any_modified == 'true' || steps.changed-tasks.outputs.api_any_modified == 'true'
+        run: |
+          echo .github/outputs/tasks_all_changed_and_modified_files.txt >> 'GITHUB_ENV'
+          echo "One or more test file(s) has changed."
+          echo "List of all the files that have changed: ${{ steps.changed-tasks.outputs.tasks_all_modified_files }}"
+
+      - name: Set up Python 3.9
+        if: steps.changed-tasks.outputs.tasks_any_modified == 'true' || steps.changed-tasks.outputs.api_any_modified == 'true'
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.9
+          cache: 'pip'
+          cache-dependency-path: setup.py
+      - name: Install dependencies
+        if: steps.changed-tasks.outputs.tasks_any_modified == 'true' || steps.changed-tasks.outputs.api_any_modified == 'true'
+        run: |
+            python -m pip install --upgrade pip
+            pip install -e '.[dev]' --extra-index-url https://download.pytorch.org/whl/cpu
+    #   Install optional git dependencies
+    #       pip install bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt
+    #       if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      - name: Test with pytest
+        # if new tasks are added, run tests on them
+        if: steps.changed-tasks.outputs.tasks_any_modified == 'true'
+        run: python -m pytest tests/test_tasks.py -s -vv
+        # if api is modified, run tests on it
+      - name: Test more tasks with pytest
+        env:
+          API: true
+        if: steps.changed-tasks.outputs.api_any_modified == 'true'
+        run: python -m pytest tests/test_tasks.py -s -vv

LM-Evaluation-Harness-240310/.github/workflows/publish.yml

+name: Publish Python distribution to PyPI
+
+on:
+  push:
+    tags:
+      - '*'
+
+jobs:
+  build:
+    name: Build distribution
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.x"
+
+    - name: Install pypa/build
+      run: >-
+        python3 -m
+        pip install
+        build
+        --user
+    - name: Build a binary wheel and a source tarball
+      run: python3 -m build
+    - name: Store the distribution packages
+      uses: actions/upload-artifact@v3
+      with:
+        name: python-package-distributions
+        path: dist/
+
+  publish-to-pypi:
+    name: >-
+      Publish Python distribution to PyPI
+    if: startsWith(github.ref, 'refs/tags/')  # only publish to PyPI on tag pushes
+    needs:
+    - build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/lm_eval
+    permissions:
+      id-token: write  # IMPORTANT: mandatory for trusted publishing
+
+    steps:
+    - name: Download all the dists
+      uses: actions/download-artifact@v3
+      with:
+        name: python-package-distributions
+        path: dist/
+    - name: Publish distribution to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+
+  publish-to-testpypi:
+    name: Publish Python distribution to TestPyPI
+    needs:
+    - build
+    runs-on: ubuntu-latest
+
+    environment:
+      name: testpypi
+      url: https://test.pypi.org/p/lm_eval
+
+    permissions:
+      id-token: write  # IMPORTANT: mandatory for trusted publishing
+
+    steps:
+    - name: Download all the dists
+      uses: actions/download-artifact@v3
+      with:
+        name: python-package-distributions
+        path: dist/
+    - name: Publish distribution to TestPyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        repository-url: https://test.pypi.org/legacy/