{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"72f1b921-8797-5328-8611-a3032c6068cf","task_key":"default--v0~2e1~2e0~5fhf--72f1b921-8797-5328-8611-a3032c6068cf","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 72f1b921-8797-5328-8611-a3032c6068cf","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from typing import List, Union\\nimport numpy as np\\nimport scipy.fft\\ndef task_func(data: List[Union[int, str]], repetitions: int = 1):\\n\",\"complete_prompt\":\"from typing import List, Union\\nimport numpy as np\\nimport scipy.fft\\n\\ndef task_func(data: List[Union[int, str]], repetitions: int = 1):\\n    \\\"\\\"\\\"\\n    Calculates the mode(s), their count(s), and the fast fourier transform of the data after repeating it a specified number of times.\\n    in a list of elements that can be repeated a specified number of times.\\n    \\n    Note:\\n    If the data is empty or the number of repetitions is less than or equal to 0, the function will return empty arrays.\\n    \\n    Parameters:\\n    - data (List[Union[int, str]]): The original list of elements (integers and/or strings).\\n    - repetitions (int, optional): The number of times to repeat the original list before calculating the mode. Defaults to 1.\\n\\n    Requirements:\\n    - numpy\\n    - scipy\\n    \\n    Returns:\\n    - dict: A dictionary with two keys:\\n        'mode': a numpy array of the mode(s), sorted in ascending order.\\n        'count': a numpy array of the count(s) of the mode(s).\\n        \\n    Examples:\\n    >>> task_func([1, '2', '2'], repetitions=1)\\n    {'mode': array(['2'], dtype='<U1'), 'count': [2], 'fft': array([ 5.-0.j, -1.+0.j, -1.-0.j])}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculates the mode(s), their count(s), and the fast fourier transform of the data after repeating it a specified number of times. in a list of elements that can be repeated a specified number of times.\\nNote that: If the data is empty or the number of repetitions is less than or equal to 0, the function will return empty arrays.\\nThe function should output with:\\n    dict: A dictionary with two keys:\\n    'mode': a numpy array of the mode(s), sorted in ascending order.\\n    'count': a numpy array of the count(s) of the mode(s).\\nYou should write self-contained code starting with:\\n```\\nfrom typing import List, Union\\nimport numpy as np\\nimport scipy.fft\\ndef task_func(data: List[Union[int, str]], repetitions: int = 1):\\n```\",\"libs\":\"['typing', 'numpy', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}