{"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":"cf9fb6c4-0490-535a-bd83-7f70f37de2f4","task_key":"default--v0~2e1~2e0~5fhf--cf9fb6c4-0490-535a-bd83-7f70f37de2f4","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf cf9fb6c4-0490-535a-bd83-7f70f37de2f4","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport itertools\\ndef task_func(n_walks, n_steps, seed=None):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport itertools\\n\\ndef task_func(n_walks, n_steps, seed=None):\\n    \\\"\\\"\\\"\\n    Create and plot `n_walks` number of random walks, each with `n_steps` steps.\\n\\n    The function checks for valid n_walks and n_steps, then generates walks via numpy.\\n    Each walk is plotted in a different color cycling through a predefined set of colors:\\n    ['b', 'g', 'r', 'c', 'm', 'y', 'k'].\\n\\n    Parameters:\\n    - n_walks (int): The number of random walks to be generated and plotted.\\n    - n_steps (int): The number of steps in each random walk.\\n    - seed (int, optional): Seed for random number generation. Default is None.\\n\\n    Returns:\\n    - ax (plt.Axes): A Matplotlib Axes containing the plotted random walks.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib\\n    - itertools\\n\\n    Example:\\n    >>> ax = task_func(5, 100, seed=42)\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    >>> ax.get_xticklabels()\\n    [Text(-20.0, 0, '−20'), Text(0.0, 0, '0'), Text(20.0, 0, '20'), Text(40.0, 0, '40'), Text(60.0, 0, '60'), Text(80.0, 0, '80'), Text(100.0, 0, '100'), Text(120.0, 0, '120')]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create and plot `n_walks` number of random walks, each with `n_steps` steps. The function checks for valid n_walks and n_steps, then generates walks via numpy. Each walk is plotted in a different color cycling through a predefined set of colors: ['b', 'g', 'r', 'c', 'm', 'y', 'k'].\\nThe function should output with:\\n    ax (plt.Axes): A Matplotlib Axes containing the plotted random walks.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport itertools\\ndef task_func(n_walks, n_steps, seed=None):\\n```\",\"libs\":\"['itertools', 'numpy', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}