[Format] Add config lints (#892)

configs/datasets/infinitebench/infinitebenchcodedebug/infinitebench_codedebug_gen_276a42.py

@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchcodedebugDataset
 InfiniteBench_codedebug_reader_cfg = dict(
     input_columns=['context', 'question', 'option_A', 'option_B', 'option_C', 'option_D'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_codedebug_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchcoderun/infinitebench_coderun_gen_1a76bd.py

@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchcoderunDataset
 InfiniteBench_coderun_reader_cfg = dict(
     input_columns=['context', 'func', 'func_call'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_coderun_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchendia/infinitebench_endia_gen_c96eb5.py

@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchendiaDataset, InfiniteBenchendiaEv
 InfiniteBench_endia_reader_cfg = dict(
     input_columns=['context', 'question'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_endia_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchenmc/infinitebench_enmc_gen_3a4102.py

@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchenmcDataset
 InfiniteBench_enmc_reader_cfg = dict(
     input_columns=['context', 'question', 'option_A', 'option_B', 'option_C', 'option_D'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_enmc_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchenqa/infinitebench_enqa_gen_a1640c.py

@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchenqaDataset, LongBenchF1Evaluator
 InfiniteBench_enqa_reader_cfg = dict(
     input_columns=['context', 'question'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_enqa_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchensum/infinitebench_ensum_gen_cfbc08.py

@@ -7,7 +7,7 @@ from opencompass.datasets import InfiniteBenchensumDataset
 InfiniteBench_ensum_reader_cfg = dict(
     input_columns=['context'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_ensum_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchmathcalc/infinitebench_mathcalc_gen_78d17e.py

@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchmathcalcDataset, InfiniteBenchmath
 InfiniteBench_mathcalc_reader_cfg = dict(
     input_columns=['context'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_mathcalc_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchmathfind/infinitebench_mathfind_gen_6d799e.py

@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
 InfiniteBench_mathfind_reader_cfg = dict(
     input_columns=['prefix', 'context', 'question'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_mathfind_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchretrievekv/infinitebench_retrievekv_gen_06b3ac.py

@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchretrievekvDataset, InfiniteBenchre
 InfiniteBench_retrievekv_reader_cfg = dict(
     input_columns=['context', 'input'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_retrievekv_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchretrievenumber/infinitebench_retrievenumber_gen_047436.py

@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
 InfiniteBench_retrievenumber_reader_cfg = dict(
     input_columns=['context', 'input'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_retrievenumber_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchretrievepasskey/infinitebench_retrievepasskey_gen_62ff68.py

@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
 InfiniteBench_retrievepasskey_reader_cfg = dict(
     input_columns=['context', 'input'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_retrievepasskey_infer_cfg = dict(

configs/datasets/infinitebench/infinitebenchzhqa/infinitebench_zhqa_gen_1e5293.py

@@ -7,7 +7,7 @@ from opencompass.utils.text_postprocessors import general_cn_postprocess
 InfiniteBench_zhqa_reader_cfg = dict(
     input_columns=['context', 'question'],
     output_column='answer',
-    
+
 )
 
 InfiniteBench_zhqa_infer_cfg = dict(

configs/datasets/iwslt2017/iwslt2017_gen_b4a814.py

@@ -12,7 +12,7 @@ iwslt2017_infer_cfg = dict(
     ice_template=dict(type='PromptTemplate',
                     template=dict(
                         begin=[
-                            dict(role='SYSTEM', fallback_role="HUMAN", prompt='Please translate the following English statements to German:'),
+                            dict(role='SYSTEM', fallback_role='HUMAN', prompt='Please translate the following English statements to German:'),
                             '</E>',
                         ],
                         round=[

configs/datasets/jigsawmultilingual/jigsawmultilingual_clp_fe50d8.py

@@ -19,10 +19,10 @@ jigsawmultilingual_infer_cfg = dict(
         type=PromptTemplate,
         template=dict(round=[
             dict(
-                role="HUMAN",
-                prompt="Text: {text}\nQuestion: Does the above text contain "
-                "rude, hateful, aggressive, disrespectful or unreasonable "
-                "language?\nAnswer:")
+                role='HUMAN',
+                prompt='Text: {text}\nQuestion: Does the above text contain '
+                'rude, hateful, aggressive, disrespectful or unreasonable '
+                'language?\nAnswer:')
         ])),
     retriever=dict(type=ZeroRetriever),
     inferencer=dict(type=CLPInferencer))

configs/datasets/kaoshi/kaoshi_gen_86aca2.py

@@ -4,19 +4,19 @@ from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 
 prompts = {
-        "单选题" : "请你做一道单项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从A，B，C，D中选出正确的答案，并写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n例如：【答案】: A <eoa>\n完整的题目回答的格式如下：\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下：",
-        "多选题" : "请你做一道多项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从多个选项中选出正确的答案，答案可能是一个到多个选项，奇怪将其写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n例如：【答案】: A D <eoa>\n完整的题目回答的格式如下：\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下：",
-        "填空题" : "请解答下面的填空题\n仔细阅读题目，解答其中的问题，请你一步步思考并将思考过程写在【解析】和<eoe>之间。请把你的答案写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n完整的题目回答格式如下：\n【解析】 ... <eoe>\n【答案】... <eoa>\n请你严格按照上述格式作答。\n题目如下:",
-        "完形填空" : "请你做一道英语完形填空题,其中包含二十个小题。\n请你一步一步思考。每一题你将从A，B，C，D中选出正确的答案，并写在【答案】和<eoa>之间。\n例如：（1）【答案】 A <eoa>\n（2）【答案】 B <eoa>\n请你严格按照上述格式作答。\n",
-        "七选五": "请回答下面的问题，将符合题意的五个选项的字母写在【答案】和<eoa>之间，例如：【答案】 A B C D E <eoa>\n请严格按照上述格式作答。题目如下：\n",
-        "判断题" : "请回答下面的判断题，将你的判断结果写在【答案】和<eoa>之间，若给定表述正确时回答：\n【答案】正确 <eoa>\n 表述错误时回答：\n【答案】错误 <eoa>\n请严格按照上述格式作答。题目如下：\n",
+        '单选题' : '请你做一道单项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从A，B，C，D中选出正确的答案，并写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n例如：【答案】: A <eoa>\n完整的题目回答的格式如下：\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下：',
+        '多选题' : '请你做一道多项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从多个选项中选出正确的答案，答案可能是一个到多个选项，奇怪将其写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n例如：【答案】: A D <eoa>\n完整的题目回答的格式如下：\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下：',
+        '填空题' : '请解答下面的填空题\n仔细阅读题目，解答其中的问题，请你一步步思考并将思考过程写在【解析】和<eoe>之间。请把你的答案写在【答案】和<eoa>之间，答案应只包含最终结果，不要添加额外词语。\n完整的题目回答格式如下：\n【解析】 ... <eoe>\n【答案】... <eoa>\n请你严格按照上述格式作答。\n题目如下:',
+        '完形填空' : '请你做一道英语完形填空题,其中包含二十个小题。\n请你一步一步思考。每一题你将从A，B，C，D中选出正确的答案，并写在【答案】和<eoa>之间。\n例如：（1）【答案】 A <eoa>\n（2）【答案】 B <eoa>\n请你严格按照上述格式作答。\n',
+        '七选五': '请回答下面的问题，将符合题意的五个选项的字母写在【答案】和<eoa>之间，例如：【答案】 A B C D E <eoa>\n请严格按照上述格式作答。题目如下：\n',
+        '判断题' : '请回答下面的判断题，将你的判断结果写在【答案】和<eoa>之间，若给定表述正确时回答：\n【答案】正确 <eoa>\n 表述错误时回答：\n【答案】错误 <eoa>\n请严格按照上述格式作答。题目如下：\n',
 }
 
-splits_with_type = {'单选题': ['职业-消防', '职业-测绘', '考研-经济', '职业-安全工程', '考研-政治', '职业-建筑', '考研-英语', '职业-教师资格', '职业-证券', '职业-会计', '职业-公务员', '考研-数学', '职业-高项', '考研-临床医学', '职业-银行', '考研-管理类综合', '职业-基金'], 
-                    '多选题': ['职业-消防', '职业-测绘', '考研-政治', '职业-建筑', '职业-证券', '职业-会计', '考研-临床医学', '职业-银行'], 
-                    '完形填空': ['考研-英语'], 
-                    '七选五': ['考研-英语'], 
-                    '判断题': ['职业-证券'], 
+splits_with_type = {'单选题': ['职业-消防', '职业-测绘', '考研-经济', '职业-安全工程', '考研-政治', '职业-建筑', '考研-英语', '职业-教师资格', '职业-证券', '职业-会计', '职业-公务员', '考研-数学', '职业-高项', '考研-临床医学', '职业-银行', '考研-管理类综合', '职业-基金'],
+                    '多选题': ['职业-消防', '职业-测绘', '考研-政治', '职业-建筑', '职业-证券', '职业-会计', '考研-临床医学', '职业-银行'],
+                    '完形填空': ['考研-英语'],
+                    '七选五': ['考研-英语'],
+                    '判断题': ['职业-证券'],
                     '填空题': ['考研-数学']}
 
 zh2en = {'单选题': 'single_choice', '多选题': 'multi_choice', '完形填空': 'multi_question_choice', '判断题': 'judgment', '填空题': 'cloze', '七选五': 'five_out_of_seven'}
@@ -28,44 +28,44 @@ for _type in list(splits_with_type.keys()):
         _folder = _split.replace('-' + _type, '')
         _p = prompts[_type]
         _reader_cfg = {
-            "input_columns": ['question'],
-            "output_column": 'answer',
+            'input_columns': ['question'],
+            'output_column': 'answer',
         }
         _infer_cfg = {
-            "ice_template": {
-                "type": PromptTemplate,
-                "template": {
-                    "round": [{
-                        "role": "HUMAN",
-                        "prompt": _p + '{question}'
+            'ice_template': {
+                'type': PromptTemplate,
+                'template': {
+                    'round': [{
+                        'role': 'HUMAN',
+                        'prompt': _p + '{question}'
                     }]
                 },
-                "ice_token": "</E>"
+                'ice_token': '</E>'
             },
-            "retriever": {
-                "type": ZeroRetriever
+            'retriever': {
+                'type': ZeroRetriever
             },
-            "inferencer": {
-                "type": GenInferencer,
-                "max_out_len": 1024,
+            'inferencer': {
+                'type': GenInferencer,
+                'max_out_len': 1024,
             }
         }
         _eval_cfg = {
-            "evaluator": {
-                "type": KaoshiEvaluator,
-                  "question_type": zh2en[_type],
+            'evaluator': {
+                'type': KaoshiEvaluator,
+                  'question_type': zh2en[_type],
             },
-            "pred_role": "BOT",
+            'pred_role': 'BOT',
         }
         _base_path = './data/Kaoshi'
         _dataset = {
-            "type": KaoshiDataset,
-            "abbr": "Kaoshi" + _split + '-' + _type,
-            "path": _base_path + '/' + _folder + '/' + _type + ".jsonl",
-            "name": zh2en[_type],
-            "reader_cfg": _reader_cfg,
-            "infer_cfg": _infer_cfg,
-            "eval_cfg": _eval_cfg,
+            'type': KaoshiDataset,
+            'abbr': 'Kaoshi' + _split + '-' + _type,
+            'path': _base_path + '/' + _folder + '/' + _type + '.jsonl',
+            'name': zh2en[_type],
+            'reader_cfg': _reader_cfg,
+            'infer_cfg': _infer_cfg,
+            'eval_cfg': _eval_cfg,
         }
 
         kaoshi_datasets.append(_dataset)

configs/datasets/lawbench/lawbench_one_shot_gen_002588.py

@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
 from opencompass.datasets import LawBenchDataset
 
 names = [
-    ["1-1", "article_recitation"],
-    ["1-2", "knowledge_question_answering"],
-    ["2-1", "document_proofreading"],
-    ["2-2", "dispute_focus_identification"],
-    ["2-3", "marital_disputes_identification"],
-    ["2-4", "issue_topic_identification"],
-    ["2-5", "reading_comprehension"],
-    ["2-6", "named_entity_recognition"],
-    ["2-7", "opinion_summarization"],
-    ["2-8", "argument_mining"],
-    ["2-9", "event_detection"],
-    ["2-10", "trigger_word_extraction"],
-    ["3-1", "fact_based_article_prediction"],
-    ["3-2", "scene_based_article_prediction"],
-    ["3-3", "charge_prediction"],
-    ["3-4", "prison_term_prediction_wo_article"],
-    ["3-5", "prison_term_prediction_w_article"],
-    ["3-6", "case_analysis"],
-    ["3-7", "criminal_damages_calculation"],
-    ["3-8", "consultation"],
+    ['1-1', 'article_recitation'],
+    ['1-2', 'knowledge_question_answering'],
+    ['2-1', 'document_proofreading'],
+    ['2-2', 'dispute_focus_identification'],
+    ['2-3', 'marital_disputes_identification'],
+    ['2-4', 'issue_topic_identification'],
+    ['2-5', 'reading_comprehension'],
+    ['2-6', 'named_entity_recognition'],
+    ['2-7', 'opinion_summarization'],
+    ['2-8', 'argument_mining'],
+    ['2-9', 'event_detection'],
+    ['2-10', 'trigger_word_extraction'],
+    ['3-1', 'fact_based_article_prediction'],
+    ['3-2', 'scene_based_article_prediction'],
+    ['3-3', 'charge_prediction'],
+    ['3-4', 'prison_term_prediction_wo_article'],
+    ['3-5', 'prison_term_prediction_w_article'],
+    ['3-6', 'case_analysis'],
+    ['3-7', 'criminal_damages_calculation'],
+    ['3-8', 'consultation'],
 ]
 
 lawbench_datasets = []
@@ -37,7 +37,7 @@ for index, name in names:
             type=PromptTemplate,
             template=dict(
                 round=[
-                    dict(role="HUMAN", prompt="{instruction}\n{question}"),
+                    dict(role='HUMAN', prompt='{instruction}\n{question}'),
                 ]
             ),
         ),

configs/datasets/lawbench/lawbench_zero_shot_gen_002588.py

@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
 from opencompass.datasets import LawBenchDataset
 
 names = [
-    ["1-1", "article_recitation"],
-    ["1-2", "knowledge_question_answering"],
-    ["2-1", "document_proofreading"],
-    ["2-2", "dispute_focus_identification"],
-    ["2-3", "marital_disputes_identification"],
-    ["2-4", "issue_topic_identification"],
-    ["2-5", "reading_comprehension"],
-    ["2-6", "named_entity_recognition"],
-    ["2-7", "opinion_summarization"],
-    ["2-8", "argument_mining"],
-    ["2-9", "event_detection"],
-    ["2-10", "trigger_word_extraction"],
-    ["3-1", "fact_based_article_prediction"],
-    ["3-2", "scene_based_article_prediction"],
-    ["3-3", "charge_prediction"],
-    ["3-4", "prison_term_prediction_wo_article"],
-    ["3-5", "prison_term_prediction_w_article"],
-    ["3-6", "case_analysis"],
-    ["3-7", "criminal_damages_calculation"],
-    ["3-8", "consultation"],
+    ['1-1', 'article_recitation'],
+    ['1-2', 'knowledge_question_answering'],
+    ['2-1', 'document_proofreading'],
+    ['2-2', 'dispute_focus_identification'],
+    ['2-3', 'marital_disputes_identification'],
+    ['2-4', 'issue_topic_identification'],
+    ['2-5', 'reading_comprehension'],
+    ['2-6', 'named_entity_recognition'],
+    ['2-7', 'opinion_summarization'],
+    ['2-8', 'argument_mining'],
+    ['2-9', 'event_detection'],
+    ['2-10', 'trigger_word_extraction'],
+    ['3-1', 'fact_based_article_prediction'],
+    ['3-2', 'scene_based_article_prediction'],
+    ['3-3', 'charge_prediction'],
+    ['3-4', 'prison_term_prediction_wo_article'],
+    ['3-5', 'prison_term_prediction_w_article'],
+    ['3-6', 'case_analysis'],
+    ['3-7', 'criminal_damages_calculation'],
+    ['3-8', 'consultation'],
 ]
 
 lawbench_datasets = []
@@ -37,7 +37,7 @@ for index, name in names:
             type=PromptTemplate,
             template=dict(
                 round=[
-                    dict(role="HUMAN", prompt="{instruction}\n{question}"),
+                    dict(role='HUMAN', prompt='{instruction}\n{question}'),
                 ]
             ),
         ),

configs/datasets/leval/leval.py

@@ -19,5 +19,5 @@ with read_base():
     from .levaltvshowsumm.leval_tvshow_summ_gen import LEval_tvshow_summ_datasets
     from .levalscientificqa.leval_scientificqa_gen import LEval_scientificqa_datasets
     from .levalreviewsumm.leval_review_summ_gen import LEval_review_summ_datasets
-    
-leval_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
+
+leval_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
\ No newline at end of file

configs/datasets/leval/levalcoursera/leval_coursera_gen_36a006.py

@@ -28,7 +28,7 @@ LEval_coursera_infer_cfg = dict(
 )
 
 LEval_coursera_eval_cfg = dict(
-    evaluator=dict(type=AccEvaluator), 
+    evaluator=dict(type=AccEvaluator),
     pred_postprocessor=dict(type=first_capital_postprocess_multi),
     pred_role='BOT'
 )

configs/datasets/leval/levalfinancialqa/leval_financialqa_gen_b03798.py

@@ -27,7 +27,7 @@ LEval_financialqa_infer_cfg = dict(
 )
 
 LEval_financialqa_eval_cfg = dict(
-    evaluator=dict(type=RougeEvaluator), 
+    evaluator=dict(type=RougeEvaluator),
     pred_role='BOT'
 )