834 lines
30 KiB
Python
834 lines
30 KiB
Python
import os
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import sys
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import copy
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import json
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import logging
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import threading
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import heapq
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import traceback
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import gc
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import inspect
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from typing import List, Literal, NamedTuple, Optional
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import torch
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import nodes
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import comfy.model_management
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def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
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valid_inputs = class_def.INPUT_TYPES()
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input_data_all = {}
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for x in inputs:
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input_data = inputs[x]
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if isinstance(input_data, list):
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input_unique_id = input_data[0]
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output_index = input_data[1]
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if input_unique_id not in outputs:
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input_data_all[x] = (None,)
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continue
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obj = outputs[input_unique_id][output_index]
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input_data_all[x] = obj
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else:
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if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
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input_data_all[x] = [input_data]
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if "hidden" in valid_inputs:
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h = valid_inputs["hidden"]
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for x in h:
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if h[x] == "PROMPT":
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input_data_all[x] = [prompt]
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if h[x] == "EXTRA_PNGINFO":
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if "extra_pnginfo" in extra_data:
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input_data_all[x] = [extra_data['extra_pnginfo']]
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if h[x] == "UNIQUE_ID":
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input_data_all[x] = [unique_id]
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return input_data_all
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def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
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# check if node wants the lists
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input_is_list = False
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if hasattr(obj, "INPUT_IS_LIST"):
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input_is_list = obj.INPUT_IS_LIST
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if len(input_data_all) == 0:
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max_len_input = 0
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else:
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max_len_input = max([len(x) for x in input_data_all.values()])
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# get a slice of inputs, repeat last input when list isn't long enough
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def slice_dict(d, i):
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d_new = dict()
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for k,v in d.items():
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d_new[k] = v[i if len(v) > i else -1]
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return d_new
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results = []
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if input_is_list:
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if allow_interrupt:
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nodes.before_node_execution()
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results.append(getattr(obj, func)(**input_data_all))
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elif max_len_input == 0:
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if allow_interrupt:
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nodes.before_node_execution()
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results.append(getattr(obj, func)())
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else:
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for i in range(max_len_input):
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if allow_interrupt:
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nodes.before_node_execution()
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results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
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return results
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def get_output_data(obj, input_data_all):
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results = []
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uis = []
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return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
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for r in return_values:
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if isinstance(r, dict):
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if 'ui' in r:
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uis.append(r['ui'])
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if 'result' in r:
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results.append(r['result'])
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else:
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results.append(r)
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output = []
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if len(results) > 0:
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# check which outputs need concatenating
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output_is_list = [False] * len(results[0])
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if hasattr(obj, "OUTPUT_IS_LIST"):
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output_is_list = obj.OUTPUT_IS_LIST
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# merge node execution results
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for i, is_list in zip(range(len(results[0])), output_is_list):
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if is_list:
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output.append([x for o in results for x in o[i]])
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else:
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output.append([o[i] for o in results])
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ui = dict()
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if len(uis) > 0:
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ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
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return output, ui
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def format_value(x):
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if x is None:
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return None
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elif isinstance(x, (int, float, bool, str)):
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return x
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else:
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return str(x)
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def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage):
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unique_id = current_item
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inputs = prompt[unique_id]['inputs']
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class_type = prompt[unique_id]['class_type']
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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if unique_id in outputs:
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return (True, None, None)
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for x in inputs:
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input_data = inputs[x]
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if isinstance(input_data, list):
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input_unique_id = input_data[0]
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output_index = input_data[1]
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if input_unique_id not in outputs:
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result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
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if result[0] is not True:
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# Another node failed further upstream
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return result
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input_data_all = None
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try:
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input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
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if server.client_id is not None:
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server.last_node_id = unique_id
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server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
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obj = object_storage.get((unique_id, class_type), None)
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if obj is None:
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obj = class_def()
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object_storage[(unique_id, class_type)] = obj
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output_data, output_ui = get_output_data(obj, input_data_all)
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outputs[unique_id] = output_data
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if len(output_ui) > 0:
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outputs_ui[unique_id] = output_ui
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if server.client_id is not None:
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server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
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except comfy.model_management.InterruptProcessingException as iex:
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logging.info("Processing interrupted")
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# skip formatting inputs/outputs
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error_details = {
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"node_id": unique_id,
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}
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return (False, error_details, iex)
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except Exception as ex:
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typ, _, tb = sys.exc_info()
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exception_type = full_type_name(typ)
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input_data_formatted = {}
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if input_data_all is not None:
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input_data_formatted = {}
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for name, inputs in input_data_all.items():
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input_data_formatted[name] = [format_value(x) for x in inputs]
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output_data_formatted = {}
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for node_id, node_outputs in outputs.items():
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output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
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logging.error("!!! Exception during processing !!!")
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logging.error(traceback.format_exc())
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error_details = {
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"node_id": unique_id,
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"exception_message": str(ex),
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"exception_type": exception_type,
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"traceback": traceback.format_tb(tb),
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"current_inputs": input_data_formatted,
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"current_outputs": output_data_formatted
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}
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return (False, error_details, ex)
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executed.add(unique_id)
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return (True, None, None)
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def recursive_will_execute(prompt, outputs, current_item):
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unique_id = current_item
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inputs = prompt[unique_id]['inputs']
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will_execute = []
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if unique_id in outputs:
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return []
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for x in inputs:
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input_data = inputs[x]
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if isinstance(input_data, list):
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input_unique_id = input_data[0]
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output_index = input_data[1]
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if input_unique_id not in outputs:
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will_execute += recursive_will_execute(prompt, outputs, input_unique_id)
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return will_execute + [unique_id]
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def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
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unique_id = current_item
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inputs = prompt[unique_id]['inputs']
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class_type = prompt[unique_id]['class_type']
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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is_changed_old = ''
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is_changed = ''
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to_delete = False
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if hasattr(class_def, 'IS_CHANGED'):
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if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
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is_changed_old = old_prompt[unique_id]['is_changed']
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if 'is_changed' not in prompt[unique_id]:
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input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
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if input_data_all is not None:
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try:
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#is_changed = class_def.IS_CHANGED(**input_data_all)
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is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
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prompt[unique_id]['is_changed'] = is_changed
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except:
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to_delete = True
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else:
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is_changed = prompt[unique_id]['is_changed']
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if unique_id not in outputs:
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return True
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if not to_delete:
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if is_changed != is_changed_old:
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to_delete = True
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elif unique_id not in old_prompt:
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to_delete = True
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elif inputs == old_prompt[unique_id]['inputs']:
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for x in inputs:
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input_data = inputs[x]
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if isinstance(input_data, list):
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input_unique_id = input_data[0]
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output_index = input_data[1]
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if input_unique_id in outputs:
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to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
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else:
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to_delete = True
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if to_delete:
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break
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else:
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to_delete = True
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if to_delete:
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d = outputs.pop(unique_id)
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del d
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return to_delete
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class PromptExecutor:
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def __init__(self, server):
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self.server = server
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self.reset()
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def reset(self):
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self.outputs = {}
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self.object_storage = {}
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self.outputs_ui = {}
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self.status_messages = []
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self.success = True
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self.old_prompt = {}
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def add_message(self, event, data, broadcast: bool):
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self.status_messages.append((event, data))
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if self.server.client_id is not None or broadcast:
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self.server.send_sync(event, data, self.server.client_id)
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def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
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node_id = error["node_id"]
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class_type = prompt[node_id]["class_type"]
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# First, send back the status to the frontend depending
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# on the exception type
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if isinstance(ex, comfy.model_management.InterruptProcessingException):
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mes = {
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"prompt_id": prompt_id,
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"node_id": node_id,
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"node_type": class_type,
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"executed": list(executed),
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}
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self.add_message("execution_interrupted", mes, broadcast=True)
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else:
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mes = {
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"prompt_id": prompt_id,
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"node_id": node_id,
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"node_type": class_type,
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"executed": list(executed),
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"exception_message": error["exception_message"],
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"exception_type": error["exception_type"],
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"traceback": error["traceback"],
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"current_inputs": error["current_inputs"],
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"current_outputs": error["current_outputs"],
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}
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self.add_message("execution_error", mes, broadcast=False)
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# Next, remove the subsequent outputs since they will not be executed
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to_delete = []
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for o in self.outputs:
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if (o not in current_outputs) and (o not in executed):
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to_delete += [o]
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if o in self.old_prompt:
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d = self.old_prompt.pop(o)
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del d
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for o in to_delete:
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d = self.outputs.pop(o)
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del d
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def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
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nodes.interrupt_processing(False)
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if "client_id" in extra_data:
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self.server.client_id = extra_data["client_id"]
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else:
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self.server.client_id = None
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self.status_messages = []
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self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
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with torch.inference_mode():
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#delete cached outputs if nodes don't exist for them
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to_delete = []
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for o in self.outputs:
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if o not in prompt:
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to_delete += [o]
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for o in to_delete:
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d = self.outputs.pop(o)
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del d
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to_delete = []
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for o in self.object_storage:
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if o[0] not in prompt:
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to_delete += [o]
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else:
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p = prompt[o[0]]
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if o[1] != p['class_type']:
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to_delete += [o]
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for o in to_delete:
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d = self.object_storage.pop(o)
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del d
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for x in prompt:
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recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
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current_outputs = set(self.outputs.keys())
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for x in list(self.outputs_ui.keys()):
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if x not in current_outputs:
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d = self.outputs_ui.pop(x)
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del d
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comfy.model_management.cleanup_models()
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self.add_message("execution_cached",
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{ "nodes": list(current_outputs) , "prompt_id": prompt_id},
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broadcast=False)
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executed = set()
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output_node_id = None
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to_execute = []
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for node_id in list(execute_outputs):
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to_execute += [(0, node_id)]
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while len(to_execute) > 0:
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#always execute the output that depends on the least amount of unexecuted nodes first
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to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
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output_node_id = to_execute.pop(0)[-1]
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# This call shouldn't raise anything if there's an error deep in
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# the actual SD code, instead it will report the node where the
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# error was raised
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self.success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage)
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if self.success is not True:
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self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
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break
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for x in executed:
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self.old_prompt[x] = copy.deepcopy(prompt[x])
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self.server.last_node_id = None
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if comfy.model_management.DISABLE_SMART_MEMORY:
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comfy.model_management.unload_all_models()
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def validate_inputs(prompt, item, validated):
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unique_id = item
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if unique_id in validated:
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return validated[unique_id]
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inputs = prompt[unique_id]['inputs']
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class_type = prompt[unique_id]['class_type']
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obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
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class_inputs = obj_class.INPUT_TYPES()
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required_inputs = class_inputs['required']
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errors = []
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valid = True
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validate_function_inputs = []
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if hasattr(obj_class, "VALIDATE_INPUTS"):
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validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args
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for x in required_inputs:
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if x not in inputs:
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error = {
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"type": "required_input_missing",
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"message": "Required input is missing",
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"details": f"{x}",
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"extra_info": {
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"input_name": x
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}
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}
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errors.append(error)
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continue
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val = inputs[x]
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info = required_inputs[x]
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type_input = info[0]
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if isinstance(val, list):
|
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if len(val) != 2:
|
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error = {
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"type": "bad_linked_input",
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"message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
|
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"details": f"{x}",
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"extra_info": {
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"input_name": x,
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"input_config": info,
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"received_value": val
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}
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}
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errors.append(error)
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continue
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|
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o_id = val[0]
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o_class_type = prompt[o_id]['class_type']
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r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
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if r[val[1]] != type_input:
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received_type = r[val[1]]
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details = f"{x}, {received_type} != {type_input}"
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error = {
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"type": "return_type_mismatch",
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"message": "Return type mismatch between linked nodes",
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"details": details,
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"extra_info": {
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"input_name": x,
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"input_config": info,
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"received_type": received_type,
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"linked_node": val
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}
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}
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errors.append(error)
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continue
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try:
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r = validate_inputs(prompt, o_id, validated)
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if r[0] is False:
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# `r` will be set in `validated[o_id]` already
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valid = False
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continue
|
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except Exception as ex:
|
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typ, _, tb = sys.exc_info()
|
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valid = False
|
|
exception_type = full_type_name(typ)
|
|
reasons = [{
|
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"type": "exception_during_inner_validation",
|
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"message": "Exception when validating inner node",
|
|
"details": str(ex),
|
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"extra_info": {
|
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"input_name": x,
|
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"input_config": info,
|
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"exception_message": str(ex),
|
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"exception_type": exception_type,
|
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"traceback": traceback.format_tb(tb),
|
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"linked_node": val
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}
|
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}]
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validated[o_id] = (False, reasons, o_id)
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continue
|
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else:
|
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try:
|
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if type_input == "INT":
|
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val = int(val)
|
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inputs[x] = val
|
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if type_input == "FLOAT":
|
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val = float(val)
|
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inputs[x] = val
|
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if type_input == "STRING":
|
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val = str(val)
|
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inputs[x] = val
|
|
except Exception as ex:
|
|
error = {
|
|
"type": "invalid_input_type",
|
|
"message": f"Failed to convert an input value to a {type_input} value",
|
|
"details": f"{x}, {val}, {ex}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
"exception_message": str(ex)
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if len(info) > 1:
|
|
if "min" in info[1] and val < info[1]["min"]:
|
|
error = {
|
|
"type": "value_smaller_than_min",
|
|
"message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
if "max" in info[1] and val > info[1]["max"]:
|
|
error = {
|
|
"type": "value_bigger_than_max",
|
|
"message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if x not in validate_function_inputs:
|
|
if isinstance(type_input, list):
|
|
if val not in type_input:
|
|
input_config = info
|
|
list_info = ""
|
|
|
|
# Don't send back gigantic lists like if they're lots of
|
|
# scanned model filepaths
|
|
if len(type_input) > 20:
|
|
list_info = f"(list of length {len(type_input)})"
|
|
input_config = None
|
|
else:
|
|
list_info = str(type_input)
|
|
|
|
error = {
|
|
"type": "value_not_in_list",
|
|
"message": "Value not in list",
|
|
"details": f"{x}: '{val}' not in {list_info}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": input_config,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if len(validate_function_inputs) > 0:
|
|
input_data_all = get_input_data(inputs, obj_class, unique_id)
|
|
input_filtered = {}
|
|
for x in input_data_all:
|
|
if x in validate_function_inputs:
|
|
input_filtered[x] = input_data_all[x]
|
|
|
|
#ret = obj_class.VALIDATE_INPUTS(**input_filtered)
|
|
ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
|
|
for x in input_filtered:
|
|
for i, r in enumerate(ret):
|
|
if r is not True:
|
|
details = f"{x}"
|
|
if r is not False:
|
|
details += f" - {str(r)}"
|
|
|
|
error = {
|
|
"type": "custom_validation_failed",
|
|
"message": "Custom validation failed for node",
|
|
"details": details,
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if len(errors) > 0 or valid is not True:
|
|
ret = (False, errors, unique_id)
|
|
else:
|
|
ret = (True, [], unique_id)
|
|
|
|
validated[unique_id] = ret
|
|
return ret
|
|
|
|
def full_type_name(klass):
|
|
module = klass.__module__
|
|
if module == 'builtins':
|
|
return klass.__qualname__
|
|
return module + '.' + klass.__qualname__
|
|
|
|
def validate_prompt(prompt):
|
|
outputs = set()
|
|
for x in prompt:
|
|
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
|
|
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True:
|
|
outputs.add(x)
|
|
|
|
if len(outputs) == 0:
|
|
error = {
|
|
"type": "prompt_no_outputs",
|
|
"message": "Prompt has no outputs",
|
|
"details": "",
|
|
"extra_info": {}
|
|
}
|
|
return (False, error, [], [])
|
|
|
|
good_outputs = set()
|
|
errors = []
|
|
node_errors = {}
|
|
validated = {}
|
|
for o in outputs:
|
|
valid = False
|
|
reasons = []
|
|
try:
|
|
m = validate_inputs(prompt, o, validated)
|
|
valid = m[0]
|
|
reasons = m[1]
|
|
except Exception as ex:
|
|
typ, _, tb = sys.exc_info()
|
|
valid = False
|
|
exception_type = full_type_name(typ)
|
|
reasons = [{
|
|
"type": "exception_during_validation",
|
|
"message": "Exception when validating node",
|
|
"details": str(ex),
|
|
"extra_info": {
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb)
|
|
}
|
|
}]
|
|
validated[o] = (False, reasons, o)
|
|
|
|
if valid is True:
|
|
good_outputs.add(o)
|
|
else:
|
|
logging.error(f"Failed to validate prompt for output {o}:")
|
|
if len(reasons) > 0:
|
|
logging.error("* (prompt):")
|
|
for reason in reasons:
|
|
logging.error(f" - {reason['message']}: {reason['details']}")
|
|
errors += [(o, reasons)]
|
|
for node_id, result in validated.items():
|
|
valid = result[0]
|
|
reasons = result[1]
|
|
# If a node upstream has errors, the nodes downstream will also
|
|
# be reported as invalid, but there will be no errors attached.
|
|
# So don't return those nodes as having errors in the response.
|
|
if valid is not True and len(reasons) > 0:
|
|
if node_id not in node_errors:
|
|
class_type = prompt[node_id]['class_type']
|
|
node_errors[node_id] = {
|
|
"errors": reasons,
|
|
"dependent_outputs": [],
|
|
"class_type": class_type
|
|
}
|
|
logging.error(f"* {class_type} {node_id}:")
|
|
for reason in reasons:
|
|
logging.error(f" - {reason['message']}: {reason['details']}")
|
|
node_errors[node_id]["dependent_outputs"].append(o)
|
|
logging.error("Output will be ignored")
|
|
|
|
if len(good_outputs) == 0:
|
|
errors_list = []
|
|
for o, errors in errors:
|
|
for error in errors:
|
|
errors_list.append(f"{error['message']}: {error['details']}")
|
|
errors_list = "\n".join(errors_list)
|
|
|
|
error = {
|
|
"type": "prompt_outputs_failed_validation",
|
|
"message": "Prompt outputs failed validation",
|
|
"details": errors_list,
|
|
"extra_info": {}
|
|
}
|
|
|
|
return (False, error, list(good_outputs), node_errors)
|
|
|
|
return (True, None, list(good_outputs), node_errors)
|
|
|
|
MAXIMUM_HISTORY_SIZE = 10000
|
|
|
|
class PromptQueue:
|
|
def __init__(self, server):
|
|
self.server = server
|
|
self.mutex = threading.RLock()
|
|
self.not_empty = threading.Condition(self.mutex)
|
|
self.task_counter = 0
|
|
self.queue = []
|
|
self.currently_running = {}
|
|
self.history = {}
|
|
self.flags = {}
|
|
server.prompt_queue = self
|
|
|
|
def put(self, item):
|
|
with self.mutex:
|
|
heapq.heappush(self.queue, item)
|
|
self.server.queue_updated()
|
|
self.not_empty.notify()
|
|
|
|
def get(self, timeout=None):
|
|
with self.not_empty:
|
|
while len(self.queue) == 0:
|
|
self.not_empty.wait(timeout=timeout)
|
|
if timeout is not None and len(self.queue) == 0:
|
|
return None
|
|
item = heapq.heappop(self.queue)
|
|
i = self.task_counter
|
|
self.currently_running[i] = copy.deepcopy(item)
|
|
self.task_counter += 1
|
|
self.server.queue_updated()
|
|
return (item, i)
|
|
|
|
class ExecutionStatus(NamedTuple):
|
|
status_str: Literal['success', 'error']
|
|
completed: bool
|
|
messages: List[str]
|
|
|
|
def task_done(self, item_id, outputs,
|
|
status: Optional['PromptQueue.ExecutionStatus']):
|
|
with self.mutex:
|
|
prompt = self.currently_running.pop(item_id)
|
|
if len(self.history) > MAXIMUM_HISTORY_SIZE:
|
|
self.history.pop(next(iter(self.history)))
|
|
|
|
status_dict: Optional[dict] = None
|
|
if status is not None:
|
|
status_dict = copy.deepcopy(status._asdict())
|
|
|
|
self.history[prompt[1]] = {
|
|
"prompt": prompt,
|
|
"outputs": copy.deepcopy(outputs),
|
|
'status': status_dict,
|
|
}
|
|
self.server.queue_updated()
|
|
|
|
def get_current_queue(self):
|
|
with self.mutex:
|
|
out = []
|
|
for x in self.currently_running.values():
|
|
out += [x]
|
|
return (out, copy.deepcopy(self.queue))
|
|
|
|
def get_tasks_remaining(self):
|
|
with self.mutex:
|
|
return len(self.queue) + len(self.currently_running)
|
|
|
|
def wipe_queue(self):
|
|
with self.mutex:
|
|
self.queue = []
|
|
self.server.queue_updated()
|
|
|
|
def delete_queue_item(self, function):
|
|
with self.mutex:
|
|
for x in range(len(self.queue)):
|
|
if function(self.queue[x]):
|
|
if len(self.queue) == 1:
|
|
self.wipe_queue()
|
|
else:
|
|
self.queue.pop(x)
|
|
heapq.heapify(self.queue)
|
|
self.server.queue_updated()
|
|
return True
|
|
return False
|
|
|
|
def get_history(self, prompt_id=None, max_items=None, offset=-1):
|
|
with self.mutex:
|
|
if prompt_id is None:
|
|
out = {}
|
|
i = 0
|
|
if offset < 0 and max_items is not None:
|
|
offset = len(self.history) - max_items
|
|
for k in self.history:
|
|
if i >= offset:
|
|
out[k] = self.history[k]
|
|
if max_items is not None and len(out) >= max_items:
|
|
break
|
|
i += 1
|
|
return out
|
|
elif prompt_id in self.history:
|
|
return {prompt_id: copy.deepcopy(self.history[prompt_id])}
|
|
else:
|
|
return {}
|
|
|
|
def wipe_history(self):
|
|
with self.mutex:
|
|
self.history = {}
|
|
|
|
def delete_history_item(self, id_to_delete):
|
|
with self.mutex:
|
|
self.history.pop(id_to_delete, None)
|
|
|
|
def set_flag(self, name, data):
|
|
with self.mutex:
|
|
self.flags[name] = data
|
|
self.not_empty.notify()
|
|
|
|
def get_flags(self, reset=True):
|
|
with self.mutex:
|
|
if reset:
|
|
ret = self.flags
|
|
self.flags = {}
|
|
return ret
|
|
else:
|
|
return self.flags.copy()
|