Merge remote-tracking branch 'origin/big-refactor' into nqopen_baber

# Conflicts:
#	lm_eval/api/task.py

lm_eval/tasks/wmt2016/README.md

+# WMT16
+
+### Paper
+
+Title: `Findings of the 2016 Conference on Machine Translation`
+Abstract: http://www.aclweb.org/anthology/W/W16/W16-2301
+
+
+
+Homepage: https://huggingface.co/datasets/wmt16
+
+
+### Citation
+
+```
+@InProceedings{bojar-EtAl:2016:WMT1,
+  author    = {Bojar, Ond
+{r}ej  and  Chatterjee, Rajen  and  Federmann, Christian  and  Graham, Yvette  and  Haddow, Barry  and  Huck, Matthias  and  Jimeno Yepes, Antonio  and  Koehn, Philipp  and  Logacheva, Varvara  and  Monz, Christof  and  Negri, Matteo  and  Neveol, Aurelie  and  Neves, Mariana  and  Popel, Martin  and  Post, Matt  and  Rubino, Raphael  and  Scarton, Carolina  and  Specia, Lucia  and  Turchi, Marco  and  Verspoor, Karin  and  Zampieri, Marcos},
+  title     = {Findings of the 2016 Conference on Machine Translation},
+  booktitle = {Proceedings of the First Conference on Machine Translation},
+  month     = {August},
+  year      = {2016},
+  address   = {Berlin, Germany},
+  publisher = {Association for Computational Linguistics},
+  pages     = {131--198},
+  url       = {http://www.aclweb.org/anthology/W/W16/W16-2301}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `wmt-t5-prompt`: Group for all wmt tasks with prompt templates used for T5 (`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`)
+
+#### Tasks
+
+With specific prompt styles
+* `wmt-ro-en-t5-prompt`: WMT16 with the prompt template used for T5
+
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/wmt2016/metrics.py

+import evaluate
+
+
+def bleu(predictions, references):
+    return (predictions[0], references[0])
+
+
+def agg_bleu(items):
+    bleu_fn = evaluate.load("bleu")
+    predictions, references = zip(*items)
+    return bleu_fn.compute(predictions=predictions, references=references)["bleu"]

lm_eval/tasks/wmt2016/ro_en-t5_prompt.yaml

+group:
+  - wmt-t5-prompt
+task: wmt-ro-en-t5-prompt
+dataset_path: wmt16
+dataset_name: ro-en
+training_split: train
+validation_split: validation
+output_type: greedy_until
+doc_to_text: "translate English to Romanian: {{translation.en}}"
+doc_to_target: "{{translation.ro}}"
+metric_list:
+  - metric: wer
+    aggregation: mean
+    higher_is_better: false
+  - metric: !function metrics.bleu
+    aggregation: !function metrics.agg_bleu
+    higher_is_better: true

lm_eval/tasks/wsc273/README.md

+# WSC273
+
+### Paper
+
+Title: `The Winograd Schema Challenge`
+
+Abstract: http://commonsensereasoning.org/2011/papers/Levesque.pdf
+
+A Winograd schema is a pair of sentences that differ in only one or two words
+and that contain an ambiguity that is resolved in opposite ways in the two
+sentences and requires the use of world knowledge and reasoning for its resolution.
+The Winograd Schema Challenge 273 is a collection of 273 such Winograd schemas.
+
+NOTE: This evaluation of Winograd Schema Challenge is based on `partial evaluation`
+as described by Trinh & Le in Simple Method for Commonsense Reasoning (2018).
+See: https://arxiv.org/abs/1806.0
+
+Homepage: https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html
+
+
+### Citation
+
+```
+@inproceedings{ea01b9c0db064caca6986b925d75f2bb,
+    title = "The winograd schema challenge",
+    abstract = "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Wino-grad schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.",
+    author = "Levesque, {Hector J.} and Ernest Davis and Leora Morgenstern",
+    year = "2012",
+    language = "English (US)",
+    isbn = "9781577355601",
+    series = "Proceedings of the International Conference on Knowledge Representation and Reasoning",
+    publisher = "Institute of Electrical and Electronics Engineers Inc.",
+    pages = "552--561",
+    booktitle = "13th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2012",
+    note = "13th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2012 ; Conference date: 10-06-2012 Through 14-06-2012",
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* Not part of any group yet.
+
+#### Tasks
+
+* `wsc273`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/wsc273/default.yaml

+task: wsc273
+dataset_path: winograd_wsc
+dataset_name: wsc273
+output_type: multiple_choice
+test_split: test
+doc_to_text: label
+process_docs: !function utils.process_doc
+doc_to_target: "{% set index = pronoun_loc + pronoun | length %}{{text[index:]}}"
+doc_to_choice: "{% set template = text[:pronoun_loc] %}{{[template+options[0], template+options[1]]}}"
+should_decontaminate: true
+doc_to_decontamination_query: text
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true

lm_eval/tasks/wsc273/utils.py

+upper_pronouns = [
+    "A",
+    "An",
+    "The",
+    "She",
+    "He",
+    "It",
+    "They",
+    "My",
+    "His",
+    "Her",
+    "Their",
+]
+
+
+def process_doc(dataset):
+    def process_fn(doc):
+        # The HF implementation of `wsc273` is not `partial evaluation` friendly.
+        doc["text"] = doc["text"].replace("  ", " ")
+        doc["options"][0] = __normalize_option(doc, doc["options"][0])
+        doc["options"][1] = __normalize_option(doc, doc["options"][1])
+        return doc
+
+    return dataset.map(process_fn)
+
+
+def __normalize_option(doc, option):
+    # Append `'s` to possessive determiner based options.
+    if doc["pronoun"].lower() in ["my", "his", "her", "our", "their"]:
+        option += "'s"
+    # Appropriately lowercase the pronoun in the option.
+    pronoun = option.split()[0]
+    start_of_sentence = doc["text"][doc["pronoun_loc"] - 2] == "."
+    if not start_of_sentence and pronoun in upper_pronouns:
+        return option.replace(pronoun, pronoun.lower())
+    return option

lm_eval/utils.py

@@ -10,7 +10,7 @@ import collections
 import importlib.util
 import fnmatch
 
-from typing import List, Literal, Union
+from typing import Iterator, List, Literal, Union
 
 import gc
 import torch
@@ -65,7 +65,7 @@ def join_iters(iters):
         yield from iter
 
 
-def chunks(iter, n=0, fn=None):
+def chunks(iter, n: int = 0, fn=None):
     arr = []
     for i, x in enumerate(iter):
         arr.append(x)
@@ -87,11 +87,11 @@ def group(arr, fn):
 
 
 class MultiChoice:
-    def __init__(self, choices):
+    def __init__(self, choices) -> None:
         self.choices = choices
 
     # Simple wildcard support (linux filename patterns)
-    def __contains__(self, values):
+    def __contains__(self, values) -> bool:
         for value in values.split(","):
             if len(fnmatch.filter(self.choices, value)) == 0:
                 eval_logger.info(f"Available tasks to choose:")
@@ -100,7 +100,7 @@ class MultiChoice:
                 raise ValueError("'{}' is not in task list".format(value))
         return True
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator:
         for choice in self.choices:
             yield choice
 
@@ -108,7 +108,6 @@ class MultiChoice:
 # Returns a list containing all values of the source_list that
 # match at least one of the patterns
 def pattern_match(patterns, source_list):
-
     if type(patterns) == str:
         patterns = [patterns]
 
@@ -177,7 +176,7 @@ def make_disjoint_window(pair):
 
 
 class Reorderer:
-    def __init__(self, arr, fn):
+    def __init__(self, arr, fn) -> None:
         self.size = len(arr)
         arr = list(enumerate(arr))
         arr = group(arr, lambda x: fn(x[1]))
@@ -212,7 +211,7 @@ class Grouper:
     objects in `arr` satisfying `key == fn(ob)`.
     """
 
-    def __init__(self, arr, fn):
+    def __init__(self, arr, fn) -> None:
         # self.orig_arr = arr
         self.size = len(arr)
         arr = list(enumerate(arr))
@@ -263,14 +262,14 @@ class Grouper:
         return res
 
 
-def make_table(result_dict, column="results"):
+def make_table(result_dict, column: str = "results"):
     """Generate table of results."""
     from pytablewriter import MarkdownTableWriter, LatexTableWriter
 
     if column == "results":
-        column_name = "Task"
-    elif column == "aggregate":
-        column_name = "Benchmark"
+        column_name = "Tasks"
+    elif column == "groups":
+        column_name = "Groups"
 
     md_writer = MarkdownTableWriter()
     latex_writer = LatexTableWriter()
@@ -393,7 +392,6 @@ def get_git_commit_hash():
 
 
 def import_function(loader, node):
-
     function_name = loader.construct_scalar(node)
     yaml_path = os.path.dirname(loader.name)
 
@@ -428,7 +426,6 @@ def load_yaml_config(yaml_path):
             include_path.reverse()
             final_yaml_config = {}
             for path in include_path:
-
                 # Assumes that path is a full path.
                 # If not found, assume the included yaml
                 # is in the same dir as the original yaml
@@ -447,7 +444,7 @@ def load_yaml_config(yaml_path):
         return yaml_config
 
 
-def regex_replace(string, pattern, repl, count=0):
+def regex_replace(string, pattern, repl, count: int = 0):
     """Implements the `re.sub` function as a custom Jinja filter."""
     return re.sub(pattern, repl, string, count=count)
 
@@ -521,7 +518,7 @@ def pad_and_concat(
     return torch.cat(tensors, dim=0)
 
 
-def clear_torch_cache():
+def clear_torch_cache() -> None:
     gc.collect()
     torch.cuda.empty_cache()
 
@@ -546,7 +543,7 @@ class MultiTokenEOSCriteria(transformers.StoppingCriteria):
         tokenizer: transformers.PreTrainedTokenizer,
         initial_decoder_input_length: int,
         batch_size: int,
-    ):
+    ) -> None:
         self.initial_decoder_input_length = initial_decoder_input_length
         self.done_tracker = [False] * batch_size
         self.sequence = sequence

main.py

@@ -9,23 +9,26 @@ from pathlib import Path
 
 from lm_eval import evaluator, utils
 from lm_eval.api.registry import ALL_TASKS
-from lm_eval.logger import eval_logger
+from lm_eval.logger import eval_logger, SPACING
 from lm_eval.tasks import include_task_folder
+from lm_eval.benchmarks import include_benchmarks
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
     parser.add_argument("--model", required=True, help="Name of model e.g. `hf`")
+    parser.add_argument(
+        "--tasks",
+        default=None,
+        help="Available Tasks:\n - {}".format("\n - ".join(sorted(ALL_TASKS))),
+    )
     parser.add_argument(
         "--model_args",
         default="",
         help="String arguments for model, e.g. `pretrained=EleutherAI/pythia-160m,dtype=float32`",
     )
-    parser.add_argument(
-        "--tasks", default=None  # , choices=utils.MultiChoice(sorted(ALL_TASKS))
-    )
     parser.add_argument(
         "--num_fewshot",
         type=int,
@@ -98,7 +101,7 @@ def parse_args():
     return parser.parse_args()
 
 
-def main():
+def main() -> None:
     args = parse_args()
 
     if args.limit:
@@ -125,10 +128,21 @@ def main():
         else:
             tasks_list = args.tasks.split(",")
             task_names = utils.pattern_match(tasks_list, ALL_TASKS)
+            task_missing = []
             for task in [task for task in tasks_list if task not in task_names]:
                 if os.path.isfile(task):
                     config = utils.load_yaml_config(task)
                     task_names.append(config)
+                else:
+                    task_missing.append(task)
+
+        if task_missing != []:
+            missing = ", ".join(task_missing)
+            eval_logger.error(
+                f"Tasks were not found: {missing}\n"
+                f"{SPACING}Try `lm-eval -h` for list of available tasks",
+            )
+            raise ValueError(f"Tasks {missing} were not found.")
 
     if args.output_path:
         path = Path(args.output_path)
@@ -195,8 +209,8 @@ def main():
             f"batch_size: {args.batch_size}{f' ({batch_sizes})' if batch_sizes else ''}"
         )
         print(evaluator.make_table(results))
-        if "aggregate" in results:
-            print(evaluator.make_table(results, "aggregate"))
+        if "groups" in results:
+            print(evaluator.make_table(results, "groups"))
 
 
 if __name__ == "__main__":

mypy.ini

+[mypy]
+python_version = 3.9
+show_traceback = True
+check_untyped_defs = True
+no_implicit_reexport = True
+warn_unreachable = True
+warn_unused_configs = True
+warn_unused_ignores = True
+warn_redundant_casts = True
+
+# We ignore errors everywhere to gradually add type annotations
+
+[mypy-lm_eval.*]
+ignore_errors = True
+
+[mypy-lm_eval.api.*]
+ignore_errors = True
+
+[mypy-lm_eval.prompts.*]
+ignore_errors = True
+
+[mypy-lm_eval.models.*]
+ignore_errors = True
+
+[mypy-scripts.*]
+ignore_errors = True
+
+[mypy-main]
+ignore_errors = True

pyproject.toml

+[build-system]
+requires = ["setuptools>=40.8.0", "wheel"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -15,7 +15,7 @@ extras_require = {
     ],
     "testing": ["pytest", "pytest-cov", "pytest-xdist"],
     "multilingual": ["nagisa>=0.2.7", "jieba>=0.42.1"],
-    "sentencepiece": ["sentencepiece>=0.1.98", "protobuf>=4.22.1"],
+    "sentencepiece": ["sentencepiece>=0.1.98", "protobuf>=4.22.1", "pycountry"],
     "promptsource": [
         "promptsource @ git+https://github.com/bigscience-workshop/promptsource.git#egg=promptsource"
     ],
@@ -53,7 +53,7 @@ setuptools.setup(
     ],
     python_requires=">=3.9",
     install_requires=[
-        "accelerate>=0.18.0",
+        "accelerate>=0.21.0",
         "evaluate",
         "datasets>=2.0.0",
         "evaluate>=0.4.0",
@@ -62,10 +62,9 @@ setuptools.setup(
         "omegaconf>=2.2",
         "peft>=0.2.0",
         "pybind11>=2.6.2",
-        "pycountry",
         "pytablewriter",
         "rouge-score>=0.0.4",
-        "sacrebleu==1.5.0",
+        "sacrebleu>=1.5.0",
         "scikit-learn>=0.24.1",
         "sqlitedict",
         "torch>=1.8",

tests/extra/test_new_tasks.py

-import pytest
-from itertools import islice
-import lm_eval.tasks as tasks
-from .utilities_testing import load_changed_files, parser
-from typing import List
-from lm_eval.api.task import ConfigurableTask
-import os
-
-
-# GitHub CI
-def new_tasks() -> List[str]:
-    FILENAME = ".github/outputs/tasks_all_changed_and_modified_files.txt"
-    if os.path.exists(FILENAME):
-        # If tasks folder has changed then we get the list of files from FILENAME
-        # and parse the yaml files to get the task names.
-        return parser(load_changed_files(FILENAME))
-    elif os.getenv("API") is not None:
-        # Or if API has changed then we set the ENV variable API to True
-        # and run  given tasks.
-        return ["arc_easy", "hellaswag", "piqa", "wikitext"]
-    # if both not true just do arc_easy
-    else:
-        return ["arc_easy"]
-
-
-def get_task_class() -> List[ConfigurableTask]:
-    task_name = new_tasks()
-    x = [cls for name, cls in tasks.TASK_REGISTRY.items() if name in task_name]
-    return x
-
-
-@pytest.fixture()
-def limit() -> int:
-    return 10
-
-
-# Tests
-@pytest.mark.parametrize("task_class", get_task_class())
-class TestNewTasks:
-    def test_download(self, task_class: ConfigurableTask):
-        task_class().download()
-        assert task_class().dataset is not None
-
-    def test_has_training_docs(self, task_class: ConfigurableTask):
-        assert task_class().has_training_docs() in [True, False]
-
-    def test_check_training_docs(self, task_class: ConfigurableTask):
-        task = task_class()
-        if task.has_training_docs():
-            assert task._config["training_split"] is not None
-
-    def test_has_validation_docs(self, task_class):
-        assert task_class().has_validation_docs() in [True, False]
-
-    def test_check_validation_docs(self, task_class):
-        task = task_class()
-        if task.has_validation_docs():
-            assert task._config["validation_split"] is not None
-
-    def test_has_test_docs(self, task_class):
-        assert task_class().has_test_docs() in [True, False]
-
-    def test_check_test_docs(self, task_class):
-        task = task_class()
-        if task.has_test_docs():
-            assert task._config["test_split"] is not None
-
-    def test_should_decontaminate(self, task_class):
-        task = task_class()
-        assert task.should_decontaminate() in [True, False]
-        if task.should_decontaminate():
-            assert task._config["doc_to_decontamination_query"] is not None
-
-    def test_doc_to_text(self, task_class, limit):
-        task = task_class()
-        arr = (
-            list(islice(task.test_docs(), limit))
-            if task.has_test_docs()
-            else list(islice(task.validation_docs(), limit))
-        )
-        _array = [task.doc_to_text(doc) for doc in arr]
-        # space convention; allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
-        assert all(
-            isinstance(x, str) and (x[-1] != " " if len(x) != 0 else True)
-            for x in _array
-        )
-
-    def test_create_choices(self, task_class, limit):
-        task = task_class()
-        arr = (
-            list(islice(task.test_docs(), limit))
-            if task.has_test_docs()
-            else list(islice(task.validation_docs(), limit))
-        )
-        if "multiple_choice" in task._config.output_type:
-            _array = [task.doc_to_choice(doc) for doc in arr]
-            # assert all(len(x) == 4 for x in _array)
-            assert all(isinstance(x, list) for x in _array)
-            assert all(isinstance(x[0], str) for x in _array)
-
-    def test_doc_to_target(self, task_class, limit):
-        task = task_class()
-        arr = (
-            list(islice(task.test_docs(), limit))
-            if task.has_test_docs()
-            else list(islice(task.validation_docs(), limit))
-        )
-        _array_target = [task.doc_to_target(doc) for doc in arr]
-        if task._config.output_type == "multiple_choice":
-            assert all(isinstance(label, int) for label in _array_target)
-        # _array_text = [task.doc_to_text(doc) for doc in arr]
-        # Not working
-        # assert all(tgt[0] == " " or txt[-1] == "\n" if  len(txt) != 0 else True for txt, tgt in zip(_array_text, _array_target))
-
-    def test_build_all_requests(self, task_class, limit):
-        task_class().build_all_requests(rank=1, limit=limit, world_size=1)
-        assert task_class.instances is not None
-
-    # ToDO: Add proper testing
-    def test_construct_requests(self, task_class, limit):
-        task = task_class()
-        arr = (
-            list(islice(task.test_docs(), limit))
-            if task.has_test_docs()
-            else list(islice(task.validation_docs(), limit))
-        )
-        requests = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
-        # assert all(isinstance(doc, list) for doc in requests)
-        assert len(requests) == limit if limit else True

tests/extra/test_utils.py

-import json
-from typing import List
-from lm_eval.utils import load_yaml_config
-from pathlib import Path
-
-
-FILE_PATH = file_path = ".github/outputs/tasks_all_changed_and_modified_files.txt"
-
-
-def load_changed_files(file_path: str = FILE_PATH) -> List[str]:
-    with open(file_path, "r") as f:
-        return [l for line in f.readlines() for l in line.strip().split(" ")]
-
-
-def parser(full_path: List[str]) -> List[str]:
-    _output = set()
-    for x in full_path:
-        if x.endswith(".yaml"):
-            _output.add(load_yaml_config(x)["task"])
-        elif x.endswith(".py"):
-            path = [str(x) for x in (list(Path(x).parent.glob("*.yaml")))]
-            _output |= {load_yaml_config(x)["task"] for x in path}
-    return list(_output)

tests/models/test_huggingface.py

 from __future__ import annotations
 import pytest
+from pathlib import Path
 import numpy as np
 from lm_eval.models.huggingface import HFLM
 from lm_eval.api.instance import Instance
 import lm_eval.tasks as tasks
+import sys
+import torch
 
 
 class Test_HFLM:
-
+    torch.use_deterministic_algorithms(True)
+    version_minor = sys.version_info.minor
     multiple_choice_task = tasks.TASK_REGISTRY.get("arc_easy")()  # type: ignore
     multiple_choice_task.build_all_requests(limit=10, rank=0, world_size=1)
     MULTIPLE_CH: list[Instance] = multiple_choice_task.instances
@@ -90,8 +94,15 @@ class Test_HFLM:
     def test_logliklihood(self) -> None:
         res = self.LM.loglikelihood(self.MULTIPLE_CH)
         _RES, _res = self.MULTIPLE_CH_RES, [r[0] for r in res]
-        # change atol in case of consistent failure
-        assert np.allclose(_res, _RES, atol=1e-4)
+        # log samples to CI
+        dir_path = Path("test_logs")
+        dir_path.mkdir(parents=True, exist_ok=True)
+
+        file_path = dir_path / f"outputs_log_{self.version_minor}.txt"
+        file_path = file_path.resolve()
+        with open(file_path, "w") as f:
+            f.write("\n".join(str(x) for x in _res))
+        assert np.allclose(_res, _RES, atol=1e-2)
         # check indices for Multiple Choice
         argmax_RES, argmax_res = np.argmax(
             np.array(_RES).reshape(-1, 4), axis=1

tests/test_tasks.py

 from itertools import islice
 import pytest
-from typing import List
+from .utils import new_tasks
 import lm_eval.tasks as tasks
 from lm_eval.api.task import ConfigurableTask
 
-# Using fixtures to get the task class and limit
-@pytest.fixture()
-def task_class() -> ConfigurableTask:
-    task_name = ["arc_easy"]
-    x = [cls for name, cls in tasks.TASK_REGISTRY.items() if name in task_name]
-    return x[0]
+
+# Default Task
+TASKS = ["arc_easy"]
+
+
+def task_class():
+    global TASKS
+    # CI: new_tasks checks if any modifications have been made
+    task_classes = new_tasks()
+    # Check if task_classes is empty
+    if task_classes:
+        return [tasks.TASK_REGISTRY.get(x)() for x in task_classes]
+    else:
+        return [tasks.TASK_REGISTRY.get(x)() for x in TASKS]
 
 
 @pytest.fixture()
@@ -18,109 +26,96 @@ def limit() -> int:
 
 
 # Tests
-
-
-def test_download(task_class: ConfigurableTask):
-    task_class().download()
-    assert task_class().dataset is not None
-
-
-def test_has_training_docs(task_class: ConfigurableTask):
-    assert task_class().has_training_docs() in [True, False]
-
-
-def test_check_training_docs(task_class: ConfigurableTask):
-    task = task_class()
-    if task.has_training_docs():
-        assert task._config["training_split"] is not None
-
-
-def test_has_validation_docs(task_class):
-    assert task_class().has_validation_docs() in [True, False]
-
-
-def test_check_validation_docs(task_class):
-    task = task_class()
-    if task.has_validation_docs():
-        assert task._config["validation_split"] is not None
-
-
-def test_has_test_docs(task_class):
-    assert task_class().has_test_docs() in [True, False]
-
-
-def test_check_test_docs(task_class):
-    task = task_class()
-    if task.has_test_docs():
-        assert task._config["test_split"] is not None
-
-
-def test_should_decontaminate(task_class):
-    task = task_class()
-    assert task.should_decontaminate() in [True, False]
-    if task.should_decontaminate():
-        assert task._config["doc_to_decontamination_query"] is not None
-
-
-def test_doc_to_text(task_class, limit):
-    task = task_class()
-    arr = (
-        list(islice(task.test_docs(), limit))
-        if task.has_test_docs()
-        else list(islice(task.validation_docs(), limit))
-    )
-    _array = [task.doc_to_text(doc) for doc in arr]
-    # space convention; allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
-    assert all(
-        isinstance(x, str) and (x[-1] != " " if len(x) != 0 else True) for x in _array
-    )
-
-
-def test_create_choices(task_class, limit):
-    task = task_class()
-    arr = (
-        list(islice(task.test_docs(), limit))
-        if task.has_test_docs()
-        else list(islice(task.validation_docs(), limit))
-    )
-    if "multiple_choice" in task._config.output_type:
-        _array = [task.doc_to_choice(doc) for doc in arr]
-        # assert all(len(x) == 4 for x in _array)
-        assert all(isinstance(x, list) for x in _array)
-        assert all(isinstance(x[0], str) for x in _array)
-
-
-def test_doc_to_target(task_class, limit):
-    task = task_class()
-    arr = (
-        list(islice(task.test_docs(), limit))
-        if task.has_test_docs()
-        else list(islice(task.validation_docs(), limit))
-    )
-    _array_target = [task.doc_to_target(doc) for doc in arr]
-    if task._config.output_type == "multiple_choice":
-        assert all(isinstance(label, int) for label in _array_target)
-    # _array_text = [task.doc_to_text(doc) for doc in arr]
-    # Not working
-    # assert all(tgt[0] == " " or txt[-1] == "\n" if  len(txt) != 0 else True for txt, tgt in zip(_array_text, _array_target))
-
-
-def test_build_all_requests(task_class, limit):
-    task_class().build_all_requests(rank=1, limit=limit, world_size=1)
-    assert task_class.instances is not None
-
-
-# ToDO: Add proper testing
-def test_construct_requests(task_class, limit):
-    task = task_class()
-    arr = (
-        list(islice(task.test_docs(), limit))
-        if task.has_test_docs()
-        else list(islice(task.validation_docs(), limit))
-    )
-    requests = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
-    # assert all(isinstance(doc, list) for doc in requests)
-    assert len(requests) == limit if limit else True
+@pytest.mark.parametrize("task_class", task_class())
+class TestNewTasks:
+    def test_download(self, task_class: ConfigurableTask):
+        task_class.download()
+        assert task_class.dataset is not None
+
+    def test_has_training_docs(self, task_class: ConfigurableTask):
+        assert task_class.has_training_docs() in [True, False]
+
+    def test_check_training_docs(self, task_class: ConfigurableTask):
+        if task_class.has_training_docs():
+            assert task_class._config["training_split"] is not None
+
+    def test_has_validation_docs(self, task_class):
+        assert task_class.has_validation_docs() in [True, False]
+
+    def test_check_validation_docs(self, task_class):
+        if task_class.has_validation_docs():
+            assert task_class._config["validation_split"] is not None
+
+    def test_has_test_docs(self, task_class):
+        assert task_class.has_test_docs() in [True, False]
+
+    def test_check_test_docs(self, task_class):
+        task = task_class
+        if task.has_test_docs():
+            assert task._config["test_split"] is not None
+
+    def test_should_decontaminate(self, task_class):
+        task = task_class
+        assert task.should_decontaminate() in [True, False]
+        if task.should_decontaminate():
+            assert task._config["doc_to_decontamination_query"] is not None
+
+    def test_doc_to_text(self, task_class, limit):
+        task = task_class
+        arr = (
+            list(islice(task.test_docs(), limit))
+            if task.has_test_docs()
+            else list(islice(task.validation_docs(), limit))
+        )
+        _array = [task.doc_to_text(doc) for doc in arr]
+        # space convention; allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
+        assert all(
+            isinstance(x, str) and (x[-1] != " " if len(x) != 0 else True)
+            for x in _array
+        )
+
+    def test_create_choices(self, task_class, limit):
+        task = task_class
+        arr = (
+            list(islice(task.test_docs(), limit))
+            if task.has_test_docs()
+            else list(islice(task.validation_docs(), limit))
+        )
+        if "multiple_choice" in task._config.output_type:
+            _array = [task.doc_to_choice(doc) for doc in arr]
+            # assert all(len(x) == 4 for x in _array)
+            assert all(isinstance(x, list) for x in _array)
+            assert all(isinstance(x[0], str) for x in _array)
+
+    def test_doc_to_target(self, task_class, limit):
+        task = task_class
+        arr = (
+            list(islice(task.test_docs(), limit))
+            if task.has_test_docs()
+            else list(islice(task.validation_docs(), limit))
+        )
+        _array_target = [task.doc_to_target(doc) for doc in arr]
+        if task._config.output_type == "multiple_choice":
+            assert all(isinstance(label, int) for label in _array_target)
+        # _array_text = [task.doc_to_text(doc) for doc in arr]
+        # Not working
+        # assert all(tgt[0] == " " or txt[-1] == "\n" if  len(txt) != 0 else True for txt, tgt in zip(_array_text, _array_target))
+
+    def test_build_all_requests(self, task_class, limit):
+        task_class.build_all_requests(rank=1, limit=limit, world_size=1)
+        assert task_class.instances is not None
+
+    # ToDO: Add proper testing
+    def test_construct_requests(self, task_class, limit):
+        task = task_class
+        arr = (
+            list(islice(task.test_docs(), limit))
+            if task.has_test_docs()
+            else list(islice(task.validation_docs(), limit))
+        )
+        requests = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
+        # assert all(isinstance(doc, list) for doc in requests)
+        assert len(requests) == limit if limit else True
 
 
 # def test_create_choices(task_class):

tests/extra/utilities_testing.py → tests/utils.py

-import json
 from typing import List
 from lm_eval.utils import load_yaml_config
 from pathlib import Path
-import sys
+from typing import Union
+import os
 
+
+# {{{CI}}}
 # This is the path where the output for the changed files for the tasks folder is stored
 # FILE_PATH = file_path = ".github/outputs/tasks_all_changed_and_modified_files.txt"
 
-
 # reads a text file and returns a list of words
 # used to read the output of the changed txt from tj-actions/changed-files
 def load_changed_files(file_path: str) -> List[str]:
     with open(file_path, "r") as f:
         content = f.read()
         words_list = [x for x in content.split()]
-        sys.stdout.write(f"list of files: {words_list}")
     return words_list
 
 
@@ -30,3 +30,18 @@ def parser(full_path: List[str]) -> List[str]:
             path = [str(x) for x in (list(Path(x).parent.glob("*.yaml")))]
             _output |= {load_yaml_config(x)["task"] for x in path}
     return list(_output)
+
+
+def new_tasks() -> Union[list[str], None]:
+    FILENAME = ".github/outputs/tasks_all_changed_and_modified_files.txt"
+    if os.path.exists(FILENAME):
+        # If tasks folder has changed then we get the list of files from FILENAME
+        # and parse the yaml files to get the task names.
+        return parser(load_changed_files(FILENAME))
+    elif os.getenv("API") is not None:
+        # Or if API has changed then we set the ENV variable API to True
+        # and run  given tasks.
+        return ["arc_easy", "hellaswag", "piqa", "wikitext"]
+    # if both not true just do arc_easy
+    else:
+        return