def add_active_line_prints(code):
    """
    Add print statements indicating line numbers to a python string.
    """
    # Replace newlines and comments with pass statements, so the line numbers are accurate (ast will remove them otherwise)
    code_lines = code.split("\n") # [STATE] code_lines = ['import getpass', 'import os', 'import platform'] [/STATE]
    in_multiline_string = False # [STATE] in_multiline_string = False [/STATE]
    for i in range(len(code_lines)): #  [STATE] i = 0 [/STATE] [STATE] i = 1 [/STATE] [STATE] i = 2 [/STATE]
        line = code_lines[i] #  [STATE] line = 'import getpass' [/STATE] [STATE] line = 'import os' [/STATE] [STATE] line = 'import platform' [/STATE]
        if '"""' in line or "'''" in line:
            in_multiline_string = not in_multiline_string
        if not in_multiline_string and (line.strip().startswith("#") or line == ""):
            whitespace = len(line) - len(line.lstrip(" "))
            code_lines[i] = " " * whitespace + "pass"
    processed_code = "\n".join(code_lines) # [STATE] processed_code = 'import getpass\nimport os\nimport platform' [/STATE]
    try:
        tree = ast.parse(processed_code) # [STATE] tree = {body=[<ast.Import object at 0x7f3a38b33850>, <ast.Import object at 0x7f3a3c052700>, <ast.Import object at 0x7f3a38a2e220>], type_ignores=[]} [/STATE]
    except:
        # If you can't parse the processed version, try the unprocessed version before giving up
        tree = ast.parse(code)
    transformer = AddLinePrints() # [STATE] transformer = {} [/STATE]
    new_tree = transformer.visit(tree) # [STATE] new_tree = {body=[<ast.Expr object at 0x7f3a38b33af0>, <ast.Import object at 0x7f3a38b33850>, <ast.Expr object at 0x7f3a38e23820>, <ast.Import object at 0x7f3a3c052700>, <ast.Expr object at 0x7f3a38e237c0>, <ast.Import object at 0x7f3a38a2e220>], type_ignores=[]} [/STATE] # [STATE] tree = {body=[<ast.Expr object at 0x7f3a38b33af0>, <ast.Import object at 0x7f3a38b33850>, <ast.Expr object at 0x7f3a38e23820>, <ast.Import object at 0x7f3a3c052700>, <ast.Expr object at 0x7f3a38e237c0>, <ast.Import object at 0x7f3a38a2e220>], type_ignores=[]} [/STATE]
    return ast.unparse(new_tree)

add_active_line_prints('import getpass\nimport os\nimport platform')

1: def add_active_line_prints(code):
2:     """
3:     Add print statements indicating line numbers to a python string.
4:     """
5:     # Replace newlines and comments with pass statements, so the line numbers are accurate (ast will remove them otherwise)
6:     code_lines = code.split("\n")
7:     in_multiline_string = False
8:     for i in range(len(code_lines)):
9:         line = code_lines[i]
10:         if '"""' in line or "'''" in line:
11:             in_multiline_string = not in_multiline_string
12:         if not in_multiline_string and (line.strip().startswith("#") or line == ""):
13:             whitespace = len(line) - len(line.lstrip(" "))
14:             code_lines[i] = " " * whitespace + "pass"
15:     processed_code = "\n".join(code_lines)
16:     try:
17:         tree = ast.parse(processed_code)
18:     except:
19:         # If you can't parse the processed version, try the unprocessed version before giving up
20:         tree = ast.parse(code)
21:     transformer = AddLinePrints()
22:     new_tree = transformer.visit(tree)
23:     return ast.unparse(new_tree)
24:
25: add_active_line_prints('import getpass\nimport os\nimport platform')

Question: What is the value of 'processed_code' in line 15 after executing 'add_active_line_prints('import getpass\nimport os\nimport platform')'?

Answer: 'import getpass\nimport os\nimport platform'


def count_messages_tokens(messages=[], model=None):
    """
    Count the number of tokens in a list of messages
    """
    try:
        tokens_used = 0 # [STATE] tokens_used = 0 [/STATE]

        for message in messages: #  [STATE] message = {'role': 'system', 'message': 'You are Open Interpreter, a world-class programmer that can complete any goal by executing code.\nFirst, write a plan. **Always recap the plan between each code block** (you have extreme short-term memory loss, so you need to recap the plan between each message block to retain it).\nWhen you execute code, it will be executed **on the user\'s machine**. The user has given you **full and complete permission** to execute any code necessary to complete the task. Execute the code.\nIf you want to send data between programming languages, save the data to a txt or json.\nYou can access the internet. Run **any code** to achieve the goal, and if at first you don\'t succeed, try again and again.\nYou can install new packages.\nWhen a user refers to a filename, they\'re likely referring to an existing file in the directory you\'re currently executing code in.\nWrite messages to the user in Markdown.\nIn general, try to **make plans** with as few steps as possible. As for actually executing code to carry out that plan, for *stateful* languages (like python, javascript, shell, but NOT for html which starts from 0 every time) **it\'s critical not to try to do everything in one code block.** You should try something, print information about it, then continue from there in tiny, informed steps. You will never get it on the first try, and attempting it in one go will often lead to errors you cant see.\nYou are capable of **any** task.\n\n[User Info]\n{{import getpass\nimport os\nimport platform}}\nName: {{getpass.getuser()}}\nCWD: {{os.getcwd()}}\nSHELL: {{os.environ.get(\'SHELL\')}}\nOS: {{platform.system()}}"'} [/STATE]
            if isinstance(message, str):
                tokens_used += count_tokens(message, model=model)
            elif "message" in message:
                tokens_used += count_tokens(message["message"], model=model) # [STATE] tokens_used = 360 [/STATE]

                if "code" in message:
                    tokens_used += count_tokens(message["code"], model=model)

                if "output" in message:
                    tokens_used += count_tokens(message["output"], model=model)

        prompt_cost = token_cost(tokens_used, model=model) # [STATE] prompt_cost = 0.00054 [/STATE]

        return (tokens_used, prompt_cost)
    except:
        # Non-essential feature
        return (0, 0)

count_messages_tokens([{'role': 'system', 'message': 'You are Open Interpreter, a world-class programmer that can complete any goal by executing code.\nFirst, write a plan. **Always recap the plan between each code block** (you have extreme short-term memory loss, so you need to recap the plan between each message block to retain it).\nWhen you execute code, it will be executed **on the user\'s machine**. The user has given you **full and complete permission** to execute any code necessary to complete the task. Execute the code.\nIf you want to send data between programming languages, save the data to a txt or json.\nYou can access the internet. Run **any code** to achieve the goal, and if at first you don\'t succeed, try again and again.\nYou can install new packages.\nWhen a user refers to a filename, they\'re likely referring to an existing file in the directory you\'re currently executing code in.\nWrite messages to the user in Markdown.\nIn general, try to **make plans** with as few steps as possible. As for actually executing code to carry out that plan, for *stateful* languages (like python, javascript, shell, but NOT for html which starts from 0 every time) **it\'s critical not to try to do everything in one code block.** You should try something, print information about it, then continue from there in tiny, informed steps. You will never get it on the first try, and attempting it in one go will often lead to errors you cant see.\nYou are capable of **any** task.\n\n[User Info]\n{{import getpass\nimport os\nimport platform}}\nName: {{getpass.getuser()}}\nCWD: {{os.getcwd()}}\nSHELL: {{os.environ.get(\'SHELL\')}}\nOS: {{platform.system()}}"'}], 'gpt-3.5-turbo')

1: def count_messages_tokens(messages=[], model=None):
2:     """
3:     Count the number of tokens in a list of messages
4:     """
5:     try:
6:         tokens_used = 0
7:
8:         for message in messages:
9:             if isinstance(message, str):
10:                 tokens_used += count_tokens(message, model=model)
11:             elif "message" in message:
12:                 tokens_used += count_tokens(message["message"], model=model)
13:
14:                 if "code" in message:
15:                     tokens_used += count_tokens(message["code"], model=model)
16:
17:                 if "output" in message:
18:                     tokens_used += count_tokens(message["output"], model=model)
19:
20:         prompt_cost = token_cost(tokens_used, model=model)
21:
22:         return (tokens_used, prompt_cost)
23:     except:
24:         # Non-essential feature
25:         return (0, 0)
26:
27: count_messages_tokens([{'role': 'system', 'message': 'You are Open Interpreter, a world-class programmer that can complete any goal by executing code.\nFirst, write a plan. **Always recap the plan between each code block** (you have extreme short-term memory loss, so you need to recap the plan between each message block to retain it).\nWhen you execute code, it will be executed **on the user\'s machine**. The user has given you **full and complete permission** to execute any code necessary to complete the task. Execute the code.\nIf you want to send data between programming languages, save the data to a txt or json.\nYou can access the internet. Run **any code** to achieve the goal, and if at first you don\'t succeed, try again and again.\nYou can install new packages.\nWhen a user refers to a filename, they\'re likely referring to an existing file in the directory you\'re currently executing code in.\nWrite messages to the user in Markdown.\nIn general, try to **make plans** with as few steps as possible. As for actually executing code to carry out that plan, for *stateful* languages (like python, javascript, shell, but NOT for html which starts from 0 every time) **it\'s critical not to try to do everything in one code block.** You should try something, print information about it, then continue from there in tiny, informed steps. You will never get it on the first try, and attempting it in one go will often lead to errors you cant see.\nYou are capable of **any** task.\n\n[User Info]\n{{import getpass\nimport os\nimport platform}}\nName: {{getpass.getuser()}}\nCWD: {{os.getcwd()}}\nSHELL: {{os.environ.get(\'SHELL\')}}\nOS: {{platform.system()}}"'}], 'gpt-3.5-turbo')

Question: What is the value of 'prompt_cost' in line 20 after executing 'count_messages_tokens([{'role': 'system', 'message': 'You are Open Interpreter, a world-class programmer that can complete any goal by executing code.\nFirst, write a plan. **Always recap the plan between each code block** (you have extreme short-term memory loss, so you need to recap the plan between each message block to retain it).\nWhen you execute code, it will be executed **on the user\'s machine**. The user has given you **full and complete permission** to execute any code necessary to complete the task. Execute the code.\nIf you want to send data between programming languages, save the data to a txt or json.\nYou can access the internet. Run **any code** to achieve the goal, and if at first you don\'t succeed, try again and again.\nYou can install new packages.\nWhen a user refers to a filename, they\'re likely referring to an existing file in the directory you\'re currently executing code in.\nWrite messages to the user in Markdown.\nIn general, try to **make plans** with as few steps as possible. As for actually executing code to carry out that plan, for *stateful* languages (like python, javascript, shell, but NOT for html which starts from 0 every time) **it\'s critical not to try to do everything in one code block.** You should try something, print information about it, then continue from there in tiny, informed steps. You will never get it on the first try, and attempting it in one go will often lead to errors you cant see.\nYou are capable of **any** task.\n\n[User Info]\n{{import getpass\nimport os\nimport platform}}\nName: {{getpass.getuser()}}\nCWD: {{os.getcwd()}}\nSHELL: {{os.environ.get(\'SHELL\')}}\nOS: {{platform.system()}}"'}], 'gpt-3.5-turbo')'?

Answer: 0.00054


def _get_builtin_metadata(dataset_name):
    if dataset_name == "coco":
        return _get_coco_instances_meta()
    if dataset_name == "coco_panoptic_separated":
        return _get_coco_panoptic_separated_meta()
    elif dataset_name == "coco_panoptic_standard":
        meta = {} # [STATE] meta = {} [/STATE]
        # The following metadata maps contiguous id from [0, #thing categories +
        # #stuff categories) to their names and colors. We have to replica of the
        # same name and color under "thing_*" and "stuff_*" because the current
        # visualization function in D2 handles thing and class classes differently
        # due to some heuristic used in Panoptic FPN. We keep the same naming to
        # enable reusing existing visualization functions.
        thing_classes = [k["name"] for k in COCO_CATEGORIES] # [STATE] thing_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'] [/STATE]
        thing_colors = [k["color"] for k in COCO_CATEGORIES] # [STATE] thing_colors = [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]] [/STATE]
        stuff_classes = [k["name"] for k in COCO_CATEGORIES] # [STATE] stuff_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'] [/STATE]
        stuff_colors = [k["color"] for k in COCO_CATEGORIES] # [STATE] stuff_colors = [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]] [/STATE]

        meta["thing_classes"] = thing_classes # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged']} [/STATE]
        meta["thing_colors"] = thing_colors # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]]} [/STATE]
        meta["stuff_classes"] = stuff_classes # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'stuff_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged']} [/STATE]
        meta["stuff_colors"] = stuff_colors # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'stuff_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'stuff_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]]} [/STATE]

        # Convert category id for training:
        #   category id: like semantic segmentation, it is the class id for each
        #   pixel. Since there are some classes not used in evaluation, the category
        #   id is not always contiguous and thus we have two set of category ids:
        #       - original category id: category id in the original dataset, mainly
        #           used for evaluation.
        #       - contiguous category id: [0, #classes), in order to train the linear
        #           softmax classifier.
        thing_dataset_id_to_contiguous_id = {} # [STATE] thing_dataset_id_to_contiguous_id = {} [/STATE]
        stuff_dataset_id_to_contiguous_id = {} # [STATE] stuff_dataset_id_to_contiguous_id = {} [/STATE]

        for i, cat in enumerate(COCO_CATEGORIES): #  [STATE] i = 0 [/STATE] [STATE] cat = {'color': [220, 20, 60], 'isthing': 1, 'id': 1, 'name': 'person'} [/STATE] [STATE] i = 1 [/STATE] [STATE] cat = {'color': [119, 11, 32], 'isthing': 1, 'id': 2, 'name': 'bicycle'} [/STATE] [STATE] i = 2 [/STATE] [STATE] cat = {'color': [0, 0, 142], 'isthing': 1, 'id': 3, 'name': 'car'} [/STATE] [STATE] i = 3 [/STATE] [STATE] cat = {'color': [0, 0, 230], 'isthing': 1, 'id': 4, 'name': 'motorcycle'} [/STATE] [STATE] i = 4 [/STATE] [STATE] cat = {'color': [106, 0, 228], 'isthing': 1, 'id': 5, 'name': 'airplane'} [/STATE] [STATE] i = 5 [/STATE] [STATE] cat = {'color': [0, 60, 100], 'isthing': 1, 'id': 6, 'name': 'bus'} [/STATE] [STATE] i = 6 [/STATE] [STATE] cat = {'color': [0, 80, 100], 'isthing': 1, 'id': 7, 'name': 'train'} [/STATE] [STATE] i = 7 [/STATE] [STATE] cat = {'color': [0, 0, 70], 'isthing': 1, 'id': 8, 'name': 'truck'} [/STATE] [STATE] i = 8 [/STATE] [STATE] cat = {'color': [0, 0, 192], 'isthing': 1, 'id': 9, 'name': 'boat'} [/STATE] [STATE] i = 9 [/STATE] [STATE] cat = {'color': [250, 170, 30], 'isthing': 1, 'id': 10, 'name': 'traffic light'} [/STATE] [STATE] i = 10 [/STATE]
            if cat["isthing"]:
                thing_dataset_id_to_contiguous_id[cat["id"]] = i #  [STATE] thing_dataset_id_to_contiguous_id = {1: 0} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19} [/STATE] [STATE] thing_dataset_id_to_contiguous_id = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20} [/STATE]
            else:
                stuff_dataset_id_to_contiguous_id[cat["id"]] = i #  [STATE] stuff_dataset_id_to_contiguous_id = {92: 80} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96, 145: 97} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96, 145: 97, 147: 98} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96, 145: 97, 147: 98, 148: 99} [/STATE] [STATE] stuff_dataset_id_to_contiguous_id = {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96, 145: 97, 147: 98, 148: 99, 149: 100} [/STATE]

        meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'stuff_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'stuff_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'thing_dataset_id_to_contiguous_id': {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}} [/STATE]
        meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id # [STATE] meta = {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'stuff_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skatebo...rigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'stuff_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'thing_dataset_id_to_contiguous_id': {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}, 'stuff_dataset_id_to_contiguous_id': {92: 80, 93: 81, 95: 82, 100: 83, 107: 84, 109: 85, 112: 86, 118: 87, 119: 88, 122: 89, 125: 90, 128: 91, 130: 92, 133: 93, 138: 94, 141: 95, 144: 96, 145: 97, 147: 98, 148: 99, 149: 100, 151: 101, 154: 102, 155: 103, 156: 104, 159: 105, 161: 106, 166: 107, 168: 108, 171: 109, 175: 110, 176: 111, 177: 112, 178: 113, 180: 114, 181: 115, 184: 116, 185: 117, 186: 118, 187: 119, 188: 120, 189: 121, 190: 122, 191: 123, 192: 124, 193: 125, 194: 126, 195: 127, 196: 128, 197: 129, 198: 130, 199: 131, 200: 132}} [/STATE]

        return meta
    elif dataset_name == "coco_person":
        return {
            "thing_classes": ["person"],
            "keypoint_names": COCO_PERSON_KEYPOINT_NAMES,
            "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP,
            "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES,
        }
    elif dataset_name == "cityscapes":
        # fmt: off
        CITYSCAPES_THING_CLASSES = [
            "person", "rider", "car", "truck",
            "bus", "train", "motorcycle", "bicycle",
        ]
        CITYSCAPES_STUFF_CLASSES = [
            "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
            "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
            "truck", "bus", "train", "motorcycle", "bicycle",
        ]
        # fmt: on
        return {
            "thing_classes": CITYSCAPES_THING_CLASSES,
            "stuff_classes": CITYSCAPES_STUFF_CLASSES,
        }
    raise KeyError("No built-in metadata for dataset {}".format(dataset_name))

_get_builtin_metadata('coco_panoptic_standard')

1: def _get_builtin_metadata(dataset_name):
2:     if dataset_name == "coco":
3:         return _get_coco_instances_meta()
4:     if dataset_name == "coco_panoptic_separated":
5:         return _get_coco_panoptic_separated_meta()
6:     elif dataset_name == "coco_panoptic_standard":
7:         meta = {}
8:         # The following metadata maps contiguous id from [0, #thing categories +
9:         # #stuff categories) to their names and colors. We have to replica of the
10:         # same name and color under "thing_*" and "stuff_*" because the current
11:         # visualization function in D2 handles thing and class classes differently
12:         # due to some heuristic used in Panoptic FPN. We keep the same naming to
13:         # enable reusing existing visualization functions.
14:         thing_classes = [k["name"] for k in COCO_CATEGORIES]
15:         thing_colors = [k["color"] for k in COCO_CATEGORIES]
16:         stuff_classes = [k["name"] for k in COCO_CATEGORIES]
17:         stuff_colors = [k["color"] for k in COCO_CATEGORIES]
18:
19:         meta["thing_classes"] = thing_classes
20:         meta["thing_colors"] = thing_colors
21:         meta["stuff_classes"] = stuff_classes
22:         meta["stuff_colors"] = stuff_colors
23:
24:         # Convert category id for training:
25:         #   category id: like semantic segmentation, it is the class id for each
26:         #   pixel. Since there are some classes not used in evaluation, the category
27:         #   id is not always contiguous and thus we have two set of category ids:
28:         #       - original category id: category id in the original dataset, mainly
29:         #           used for evaluation.
30:         #       - contiguous category id: [0, #classes), in order to train the linear
31:         #           softmax classifier.
32:         thing_dataset_id_to_contiguous_id = {}
33:         stuff_dataset_id_to_contiguous_id = {}
34:
35:         for i, cat in enumerate(COCO_CATEGORIES):
36:             if cat["isthing"]:
37:                 thing_dataset_id_to_contiguous_id[cat["id"]] = i
38:             else:
39:                 stuff_dataset_id_to_contiguous_id[cat["id"]] = i
40:
41:         meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
42:         meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
43:
44:         return meta
45:     elif dataset_name == "coco_person":
46:         return {
47:             "thing_classes": ["person"],
48:             "keypoint_names": COCO_PERSON_KEYPOINT_NAMES,
49:             "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP,
50:             "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES,
51:         }
52:     elif dataset_name == "cityscapes":
53:         # fmt: off
54:         CITYSCAPES_THING_CLASSES = [
55:             "person", "rider", "car", "truck",
56:             "bus", "train", "motorcycle", "bicycle",
57:         ]
58:         CITYSCAPES_STUFF_CLASSES = [
59:             "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
60:             "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
61:             "truck", "bus", "train", "motorcycle", "bicycle",
62:         ]
63:         # fmt: on
64:         return {
65:             "thing_classes": CITYSCAPES_THING_CLASSES,
66:             "stuff_classes": CITYSCAPES_STUFF_CLASSES,
67:         }
68:     raise KeyError("No built-in metadata for dataset {}".format(dataset_name))
69:
70: _get_builtin_metadata('coco_panoptic_standard')

Question: What is the value of 'meta' in line 41 after executing '_get_builtin_metadata('coco_panoptic_standard')'?

Answer: {'thing_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'thing_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'stuff_classes': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', 'food-other-merged', 'building-other-merged', 'rock-merged', 'wall-other-merged', 'rug-merged'], 'stuff_colors': [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]], 'thing_dataset_id_to_contiguous_id': {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}}


Question: What is the value of 'k' in line 8 after executing '_merge([1, 2, 4, 4, 5, 6, 7, 23, 8, 9, 20, 11, 13, 34, 66], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 0, 3, 7)'?

Answer: 3


def join_options(options):
    """Given a list of option strings this joins them in the most appropriate
    way and returns them in the form ``(formatted_string,
    any_prefix_is_slash)`` where the second item in the tuple is a flag that
    indicates if any of the option prefixes was a slash.
    """
    rv = [] # [STATE] rv = [] [/STATE]
    any_prefix_is_slash = False # [STATE] any_prefix_is_slash = False [/STATE]
    for opt in options: #  [STATE] opt = '--help' [/STATE]
        prefix = split_opt(opt)[0] # [STATE] prefix = '--' [/STATE]
        if prefix == '/':
            any_prefix_is_slash = True
        rv.append((len(prefix), opt)) # [STATE] rv = [(2, '--help')] [/STATE]

    rv.sort(key=lambda x: x[0])

    rv = ', '.join(x[1] for x in rv) # [STATE] rv = '--help' [/STATE]
    return rv, any_prefix_is_slash

join_options(['--help'])

1: def join_options(options):
2:     """Given a list of option strings this joins them in the most appropriate
3:     way and returns them in the form ``(formatted_string,
4:     any_prefix_is_slash)`` where the second item in the tuple is a flag that
5:     indicates if any of the option prefixes was a slash.
6:     """
7:     rv = []
8:     any_prefix_is_slash = False
9:     for opt in options:
10:         prefix = split_opt(opt)[0]
11:         if prefix == '/':
12:             any_prefix_is_slash = True
13:         rv.append((len(prefix), opt))
14:
15:     rv.sort(key=lambda x: x[0])
16:
17:     rv = ', '.join(x[1] for x in rv)
18:     return rv, any_prefix_is_slash
19:
20: join_options(['--help'])

Question: What is the value of 'rv' in line 17 after executing 'join_options(['--help'])'?

Answer: '--help'


def which(exe=None):
    """
    Python clone of /usr/bin/which
    """

    if not exe:
        log.error("No executable was passed to be searched by salt.utils.path.which()")
        return None

    ## define some utilities (we use closures here because our predecessor used them)
    def is_executable_common(path): # [STATE] is_executable_common = <function which.<locals>.is_executable_common at 0x7f27d3207c10> [/STATE]
        """
        This returns truth if posixy semantics (which python simulates on
        windows) states that this is executable.
        """
        return os.path.isfile(path) and os.access(path, os.X_OK)

    def resolve(path): # [STATE] resolve = <function which.<locals>.resolve at 0x7f27d3207820> [/STATE]
        """
        This will take a path and recursively follow the link until we get to a
        real file.
        """
        while os.path.islink(path):
            res = readlink(path)

            # if the link points to a relative target, then convert it to an
            # absolute path relative to the original path
            if not os.path.isabs(res):
                directory, _ = os.path.split(path)
                res = join(directory, res)
            path = res
        return path

    # windows-only
    def has_executable_ext(path, ext_membership): # [STATE] has_executable_ext = <function which.<locals>.has_executable_ext at 0x7f27d31c20d0> [/STATE]
        """
        Extract the extension from the specified path, lowercase it so we
        can be insensitive, and then check it against the available exts.
        """
        p, ext = os.path.splitext(path)
        return ext.lower() in ext_membership

    ## prepare related variables from the environment
    res = salt.utils.stringutils.to_unicode(os.environ.get("PATH", "")) # [STATE] res = '/home/XXX/.gdrive-downloader:/local/arise/XXX/miniforge3/bin:/home/XXX/.gvm/pkgsets/go1.19.1/global/bin:/home/XXX/.gvm/gos/go1.19.1/bin:/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin:/home/XXX/.gvm/bin:/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin:/local/rcs/XXX/miniforge3/condabin:/home/XXX/.gdrive-downloader:/local/arise/XXX/miniforge3/bin:/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/home/XXX/.local/bin:/home/XXX/.local/bin' [/STATE]
    system_path = res.split(os.pathsep) # [STATE] system_path = ['/home/XXX/.gdrive-downloader', '/local/arise/XXX/miniforge3/bin', '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin', '/home/XXX/.gvm/gos/go1.19.1/bin', '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin', '/home/XXX/.gvm/bin', '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin', '/local/rcs/XXX/miniforge3/condabin', '/home/XXX/.gdrive-downloader', '/local/arise/XXX/miniforge3/bin', '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli', '/usr/local/sbin', '/usr/local/bin', '/usr/sbin', '/usr/bin', '/sbin', '/bin', '/usr/games', '/usr/local/games', '/snap/bin', '/home/XXX/.local/bin', '/home/XXX/.local/bin'] [/STATE]

    # add some reasonable defaults in case someone's PATH is busted
    if not salt.utils.platform.is_windows():
        res = set(system_path) # [STATE] res = {'/usr/games', '/sbin', '/home/XXX/.gvm/gos/go1.19.1/bin', '/home/XXX/.gvm/bin', '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin', '/home/XXX/.local/bin', '/snap/bin', '/usr/bin', '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli', '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin', '/bin', '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin', '/usr/local/games', '/usr/local/sbin', '/home/XXX/.gdrive-downloader', '/local/rcs/XXX/miniforge3/condabin', '/local/arise/XXX/miniforge3/bin', '/usr/sbin', '/usr/local/bin'} [/STATE]
        extended_path = [ # [STATE] extended_path = ['/sbin', '/bin', '/usr/sbin', '/usr/bin', '/usr/local/sbin', '/usr/local/bin'] [/STATE]
            "/sbin",
            "/bin",
            "/usr/sbin",
            "/usr/bin",
            "/usr/local/sbin",
            "/usr/local/bin",
        ]
        system_path.extend([p for p in extended_path if p not in res])

    ## now to define the semantics of what's considered executable on a given platform
    if salt.utils.platform.is_windows():
        # executable semantics on windows requires us to search PATHEXT
        res = salt.utils.stringutils.to_str(os.environ.get("PATHEXT", ".EXE"))

        # generate two variables, one of them for O(n) searches (but ordered)
        # and another for O(1) searches. the previous guy was trying to use
        # memoization with a function that has no arguments, this provides
        # the exact same benefit
        pathext = res.split(os.pathsep)
        res = {ext.lower() for ext in pathext}

        # check if our caller already specified a valid extension as then we don't need to match it
        _, ext = os.path.splitext(exe)
        if ext.lower() in res:
            pathext = [""]

            is_executable = is_executable_common

        # The specified extension isn't valid, so we just assume it's part of the
        # filename and proceed to walk the pathext list
        else:
            is_executable = lambda path, membership=res: is_executable_common(
                path
            ) and has_executable_ext(path, membership)

    else:
        # in posix, there's no such thing as file extensions..only zuul
        pathext = [""] # [STATE] pathext = [''] [/STATE]

        # executable semantics are pretty simple on reasonable platforms...
        is_executable = is_executable_common # [STATE] is_executable = <function which.<locals>.is_executable_common at 0x7f27d3207c10> [/STATE]

    ## search for the executable

    # check to see if the full path was specified as then we don't need
    # to actually walk the system_path for any reason
    if is_executable(exe):
        return exe

    # now to search through our system_path
    for path in system_path: #  [STATE] path = '/home/XXX/.gdrive-downloader' [/STATE] [STATE] path = '/local/arise/XXX/miniforge3/bin' [/STATE] [STATE] path = '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin' [/STATE] [STATE] path = '/home/XXX/.gvm/gos/go1.19.1/bin' [/STATE] [STATE] path = '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin' [/STATE] [STATE] path = '/home/XXX/.gvm/bin' [/STATE] [STATE] path = '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin' [/STATE] [STATE] path = '/local/rcs/XXX/miniforge3/condabin' [/STATE] [STATE] path = '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli' [/STATE] [STATE] path = '/usr/local/sbin' [/STATE] [STATE] path = '/usr/local/bin' [/STATE] [STATE] path = '/usr/sbin' [/STATE] [STATE] path = '/usr/bin' [/STATE]
        p = join(path, exe) #  [STATE] p = '/home/XXX/.gdrive-downloader/true' [/STATE] [STATE] p = '/local/arise/XXX/miniforge3/bin/true' [/STATE] [STATE] p = '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin/true' [/STATE] [STATE] p = '/home/XXX/.gvm/gos/go1.19.1/bin/true' [/STATE] [STATE] p = '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin/true' [/STATE] [STATE] p = '/home/XXX/.gvm/bin/true' [/STATE] [STATE] p = '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin/true' [/STATE] [STATE] p = '/local/rcs/XXX/miniforge3/condabin/true' [/STATE] [STATE] p = '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli/true' [/STATE] [STATE] p = '/usr/local/sbin/true' [/STATE] [STATE] p = '/usr/local/bin/true' [/STATE] [STATE] p = '/usr/sbin/true' [/STATE] [STATE] p = '/usr/bin/true' [/STATE]

        # iterate through all extensions to see which one is executable
        for ext in pathext: #  [STATE] ext = '' [/STATE]
            pext = p + ext #  [STATE] pext = '/home/XXX/.gdrive-downloader/true' [/STATE] [STATE] pext = '/local/arise/XXX/miniforge3/bin/true' [/STATE] [STATE] pext = '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin/true' [/STATE] [STATE] pext = '/home/XXX/.gvm/gos/go1.19.1/bin/true' [/STATE] [STATE] pext = '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin/true' [/STATE] [STATE] pext = '/home/XXX/.gvm/bin/true' [/STATE] [STATE] pext = '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin/true' [/STATE] [STATE] pext = '/local/rcs/XXX/miniforge3/condabin/true' [/STATE] [STATE] pext = '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli/true' [/STATE] [STATE] pext = '/usr/local/sbin/true' [/STATE] [STATE] pext = '/usr/local/bin/true' [/STATE] [STATE] pext = '/usr/sbin/true' [/STATE] [STATE] pext = '/usr/bin/true' [/STATE]
            rp = resolve(pext) #  [STATE] rp = '/home/XXX/.gdrive-downloader/true' [/STATE] [STATE] rp = '/local/arise/XXX/miniforge3/bin/true' [/STATE] [STATE] rp = '/home/XXX/.gvm/pkgsets/go1.19.1/global/bin/true' [/STATE] [STATE] rp = '/home/XXX/.gvm/gos/go1.19.1/bin/true' [/STATE] [STATE] rp = '/home/XXX/.gvm/pkgsets/go1.19.1/global/overlay/bin/true' [/STATE] [STATE] rp = '/home/XXX/.gvm/bin/true' [/STATE] [STATE] rp = '/local/rcs/XXX/miniforge3/envs/saltstack+salt/bin/true' [/STATE] [STATE] rp = '/local/rcs/XXX/miniforge3/condabin/true' [/STATE] [STATE] rp = '/home/XXX/.vscode-server/cli/servers/Stable-31c37ee8f63491495ac49e43b8544550fbae4533/server/bin/remote-cli/true' [/STATE] [STATE] rp = '/usr/local/sbin/true' [/STATE] [STATE] rp = '/usr/local/bin/true' [/STATE] [STATE] rp = '/usr/sbin/true' [/STATE] [STATE] rp = '/usr/bin/true' [/STATE]
            if is_executable(rp):
                return p + ext
            continue
        continue

    ## if something was executable, we should've found it already...
    log.trace(
        "'%s' could not be found in the following search path: '%s'", exe, system_path
    )
    return None

which('true')

1: def which(exe=None):
2:     """
3:     Python clone of /usr/bin/which
4:     """
5:
6:     if not exe:
7:         log.error("No executable was passed to be searched by salt.utils.path.which()")
8:         return None
9:
10:     ## define some utilities (we use closures here because our predecessor used them)
11:     def is_executable_common(path):
12:         """
13:         This returns truth if posixy semantics (which python simulates on
14:         windows) states that this is executable.
15:         """
16:         return os.path.isfile(path) and os.access(path, os.X_OK)
17:
18:     def resolve(path):
19:         """
20:         This will take a path and recursively follow the link until we get to a
21:         real file.
22:         """
23:         while os.path.islink(path):
24:             res = readlink(path)
25:
26:             # if the link points to a relative target, then convert it to an
27:             # absolute path relative to the original path
28:             if not os.path.isabs(res):
29:                 directory, _ = os.path.split(path)
30:                 res = join(directory, res)
31:             path = res
32:         return path
33:
34:     # windows-only
35:     def has_executable_ext(path, ext_membership):
36:         """
37:         Extract the extension from the specified path, lowercase it so we
38:         can be insensitive, and then check it against the available exts.
39:         """
40:         p, ext = os.path.splitext(path)
41:         return ext.lower() in ext_membership
42:
43:     ## prepare related variables from the environment
44:     res = salt.utils.stringutils.to_unicode(os.environ.get("PATH", ""))
45:     system_path = res.split(os.pathsep)
46:
47:     # add some reasonable defaults in case someone's PATH is busted
48:     if not salt.utils.platform.is_windows():
49:         res = set(system_path)
50:         extended_path = [
51:             "/sbin",
52:             "/bin",
53:             "/usr/sbin",
54:             "/usr/bin",
55:             "/usr/local/sbin",
56:             "/usr/local/bin",
57:         ]
58:         system_path.extend([p for p in extended_path if p not in res])
59:
60:     ## now to define the semantics of what's considered executable on a given platform
61:     if salt.utils.platform.is_windows():
62:         # executable semantics on windows requires us to search PATHEXT
63:         res = salt.utils.stringutils.to_str(os.environ.get("PATHEXT", ".EXE"))
64:
65:         # generate two variables, one of them for O(n) searches (but ordered)
66:         # and another for O(1) searches. the previous guy was trying to use
67:         # memoization with a function that has no arguments, this provides
68:         # the exact same benefit
69:         pathext = res.split(os.pathsep)
70:         res = {ext.lower() for ext in pathext}
71:
72:         # check if our caller already specified a valid extension as then we don't need to match it
73:         _, ext = os.path.splitext(exe)
74:         if ext.lower() in res:
75:             pathext = [""]
76:
77:             is_executable = is_executable_common
78:
79:         # The specified extension isn't valid, so we just assume it's part of the
80:         # filename and proceed to walk the pathext list
81:         else:
82:             is_executable = lambda path, membership=res: is_executable_common(
83:                 path
84:             ) and has_executable_ext(path, membership)
85:
86:     else:
87:         # in posix, there's no such thing as file extensions..only zuul
88:         pathext = [""]
89:
90:         # executable semantics are pretty simple on reasonable platforms...
91:         is_executable = is_executable_common
92:
93:     ## search for the executable
94:
95:     # check to see if the full path was specified as then we don't need
96:     # to actually walk the system_path for any reason
97:     if is_executable(exe):
98:         return exe
99:
100:     # now to search through our system_path
101:     for path in system_path:
102:         p = join(path, exe)
103:
104:         # iterate through all extensions to see which one is executable
105:         for ext in pathext:
106:             pext = p + ext
107:             rp = resolve(pext)
108:             if is_executable(rp):
109:                 return p + ext
110:             continue
111:         continue
112:
113:     ## if something was executable, we should've found it already...
114:     log.trace(
115:         "'%s' could not be found in the following search path: '%s'", exe, system_path
116:     )
117:     return None
118:
119: which('true')

Question: What is the value of 'has_executable_ext' in line 35 after executing 'which('true')'?

Answer: <function which.<locals>.has_executable_ext at 0x7f27d31c20d0>


def _remove_comments_inline(text):
  """Removes the comments from the string 'text' and ignores % inside \\url{}."""
  if 'auto-ignore' in text:
    return text
  if text.lstrip(' ').lstrip('\t').startswith('%'):
    return ''

  url_pattern = r'\\url\{(?>[^{}]|(?R))*\}' # [STATE] url_pattern = '\\\\url\\{(?>[^{}]|(?R))*\\}' [/STATE]

  def remove_comments(segment): # [STATE] remove_comments = <function _remove_comments_inline.<locals>.remove_comments at 0x7f185616f280> [/STATE]
    """Remove comments from a segment of text."""
    if segment.lstrip().startswith('%'):
      return ''
    match = regex.search(r'(?<!\\)%', segment)
    if match:
      return segment[: match.end()] + '\n'
    else:
      return segment

  # split the text into segments based on \url{} tags
  segments = regex.split(f'({url_pattern})', text) # [STATE] segments = ['Foo %Comment\n'] [/STATE]

  for i in range(len(segments)): #  [STATE] i = 0 [/STATE]
    # only process segments that are not part of a \url{} tag
    if not regex.match(url_pattern, segments[i]):
      segments[i] = remove_comments(segments[i]) # [STATE] segments = ['Foo %\n'] [/STATE]

  final_text = ''.join(segments) # [STATE] final_text = 'Foo %\n' [/STATE]
  return (
      final_text
      if final_text.endswith('\n') or final_text.endswith('\\n')
      else final_text + '\n'
  )

_remove_comments_inline('Foo %Comment\n')

1: def _remove_comments_inline(text):
2:   """Removes the comments from the string 'text' and ignores % inside \\url{}."""
3:   if 'auto-ignore' in text:
4:     return text
5:   if text.lstrip(' ').lstrip('\t').startswith('%'):
6:     return ''
7:
8:   url_pattern = r'\\url\{(?>[^{}]|(?R))*\}'
9:
10:   def remove_comments(segment):
11:     """Remove comments from a segment of text."""
12:     if segment.lstrip().startswith('%'):
13:       return ''
14:     match = regex.search(r'(?<!\\)%', segment)
15:     if match:
16:       return segment[: match.end()] + '\n'
17:     else:
18:       return segment
19:
20:   # split the text into segments based on \url{} tags
21:   segments = regex.split(f'({url_pattern})', text)
22:
23:   for i in range(len(segments)):
24:     # only process segments that are not part of a \url{} tag
25:     if not regex.match(url_pattern, segments[i]):
26:       segments[i] = remove_comments(segments[i])
27:
28:   final_text = ''.join(segments)
29:   return (
30:       final_text
31:       if final_text.endswith('\n') or final_text.endswith('\\n')
32:       else final_text + '\n'
33:   )
34:
35: _remove_comments_inline('Foo %Comment\n')

Question: What is the value of 'final_text' in line 28 after executing '_remove_comments_inline('Foo %Comment\n')'?

Answer: 'Foo %\n'


def _search_reference(filename, contents, strict=False):
  """Returns a match object if filename is referenced in contents, and None otherwise.

  If not strict mode, path prefix and extension are optional.
  """
  if strict:
    # regex pattern for strict=True for path/to/img.ext:
    # \{[\s%]*path/to/img\.ext[\s%]*\}
    filename_regex = filename.replace('.', r'\.')
  else:
    filename_path = Path(filename) # [STATE] filename_path = PosixPath('to/img.ext') [/STATE]

    # make extension optional
    root, extension = filename_path.stem, filename_path.suffix # [STATE] root = 'img' [/STATE] # [STATE] extension = '.ext' [/STATE]
    basename_regex = '{}({})?'.format( # [STATE] basename_regex = 'img(\\.ext)?' [/STATE]
        regex.escape(root), regex.escape(extension)
    )

    # iterate through parent fragments to make path prefix optional
    path_prefix_regex = '' # [STATE] path_prefix_regex = '' [/STATE]
    for fragment in reversed(filename_path.parents): #  [STATE] fragment = PosixPath('.') [/STATE] [STATE] fragment = PosixPath('to') [/STATE]
      if fragment.name == '.':
        continue
      fragment = regex.escape(fragment.name) #  [STATE] fragment = '' [/STATE] [STATE] fragment = 'to' [/STATE]
      path_prefix_regex = '({}{}{})?'.format( #  [STATE] path_prefix_regex = '(/)?' [/STATE] [STATE] path_prefix_regex = '((/)?to/)?' [/STATE]
          path_prefix_regex, fragment, os.sep
      )

    # Regex pattern for strict=True for path/to/img.ext:
    # \{[\s%]*(<path_prefix>)?<basename>(<ext>)?[\s%]*\}
    filename_regex = path_prefix_regex + basename_regex # [STATE] filename_regex = '((/)?to/)?img(\\.ext)?' [/STATE]

  # Some files 'path/to/file' are referenced in tex as './path/to/file' thus
  # adds prefix for relative paths starting with './' or '.\' to regex search.
  filename_regex = r'(.' + os.sep + r')?' + filename_regex # [STATE] filename_regex = '(./)?((/)?to/)?img(\\.ext)?' [/STATE]

  # Pads with braces and optional whitespace/comment characters.
  patn = r'\{{[\s%]*{}[\s%]*\}}'.format(filename_regex) # [STATE] patn = '\\{[\\s%]*(./)?((/)?to/)?img(\\.ext)?[\\s%]*\\}' [/STATE]
  # Picture references in LaTeX are allowed to be in different cases.
  return regex.search(patn, contents, regex.IGNORECASE)

_search_reference('to/img.ext', '{long/path/to/img}', False)

1: def _search_reference(filename, contents, strict=False):
2:   """Returns a match object if filename is referenced in contents, and None otherwise.
3:
4:   If not strict mode, path prefix and extension are optional.
5:   """
6:   if strict:
7:     # regex pattern for strict=True for path/to/img.ext:
8:     # \{[\s%]*path/to/img\.ext[\s%]*\}
9:     filename_regex = filename.replace('.', r'\.')
10:   else:
11:     filename_path = Path(filename)
12:
13:     # make extension optional
14:     root, extension = filename_path.stem, filename_path.suffix
15:     basename_regex = '{}({})?'.format(
16:         regex.escape(root), regex.escape(extension)
17:     )
18:
19:     # iterate through parent fragments to make path prefix optional
20:     path_prefix_regex = ''
21:     for fragment in reversed(filename_path.parents):
22:       if fragment.name == '.':
23:         continue
24:       fragment = regex.escape(fragment.name)
25:       path_prefix_regex = '({}{}{})?'.format(
26:           path_prefix_regex, fragment, os.sep
27:       )
28:
29:     # Regex pattern for strict=True for path/to/img.ext:
30:     # \{[\s%]*(<path_prefix>)?<basename>(<ext>)?[\s%]*\}
31:     filename_regex = path_prefix_regex + basename_regex
32:
33:   # Some files 'path/to/file' are referenced in tex as './path/to/file' thus
34:   # adds prefix for relative paths starting with './' or '.\' to regex search.
35:   filename_regex = r'(.' + os.sep + r')?' + filename_regex
36:
37:   # Pads with braces and optional whitespace/comment characters.
38:   patn = r'\{{[\s%]*{}[\s%]*\}}'.format(filename_regex)
39:   # Picture references in LaTeX are allowed to be in different cases.
40:   return regex.search(patn, contents, regex.IGNORECASE)
41:
42: _search_reference('to/img.ext', '{long/path/to/img}', False)

Question: What is the value of 'filename_regex' in line 31 after executing '_search_reference('to/img.ext', '{long/path/to/img}', False)'?

Answer: '((/)?to/)?img(\\.ext)?'