336 lines
12 KiB
Python
336 lines
12 KiB
Python
import glob
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import inspect
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import json
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import logging
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import os
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import sys
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import black
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from typing import Dict, List, Any, Callable
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sys.path.append('../')
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from nodes import NODE_CLASS_MAPPINGS
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logging.basicConfig(level=logging.INFO)
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def read_json_file(file_path: str) -> dict:
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"""
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Reads a JSON file and returns its contents as a dictionary.
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Args:
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file_path (str): The path to the JSON file.
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Returns:
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dict: The contents of the JSON file as a dictionary.
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Raises:
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FileNotFoundError: If the file is not found, it lists all JSON files in the directory of the file path.
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ValueError: If the file is not a valid JSON.
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"""
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try:
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with open(file_path, 'r') as file:
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data = json.load(file)
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return data
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except FileNotFoundError:
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# Get the directory from the file_path
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directory = os.path.dirname(file_path)
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# If the directory is an empty string (which means file is in the current directory),
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# get the current working directory
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if not directory:
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directory = os.getcwd()
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# Find all JSON files in the directory
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json_files = glob.glob(f"{directory}/*.json")
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# Format the list of JSON files as a string
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json_files_str = "\n".join(json_files)
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raise FileNotFoundError(f"\n\nFile not found: {file_path}. JSON files in the directory:\n{json_files_str}")
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except json.JSONDecodeError:
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raise ValueError(f"Invalid JSON format in file: {file_path}")
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def determine_load_order(data: Dict) -> List:
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"""
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Determine the load order of each key in the provided dictionary. This code will place the
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nodes without node dependencies first, then ensure that any node whose result is used
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in another node will be added to the list in the order it should be executed.
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Args:
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data (Dict):
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The dictionary for which to determine the load order.
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Returns:
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List:
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A list of tuples where each tuple contains a key, its corresponding dictionary,
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and a boolean indicating whether or not the function is dependent on the output of
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a previous function, ordered by load order.
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"""
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# Create a dictionary to keep track of visited nodes.
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visited = {}
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# Create a list to store the load order for functions
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load_order = []
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# Boolean to indicate whether or not the class is a loader class that should not be
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# reloaded during every loop
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is_loader = False
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def dfs(key: str) -> None:
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"""
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Depth-First Search function.
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Args:
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key (str): The key from which to start the DFS.
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Returns:
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None
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"""
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# Mark the node as visited.
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visited[key] = True
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inputs = data[key]['inputs']
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# Loop over each input key.
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for input_key, val in inputs.items():
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# If the value is a list and the first item in the list (which should be a key)
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# has not been visited yet, then recursively apply dfs on the dependency.
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if isinstance(val, list) and val[0] not in visited:
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dfs(val[0])
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# Add the key and its corresponding data to the load order list.
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load_order.append((key, data[key], is_loader))
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# Load Loader keys first
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for key in data:
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class_def = NODE_CLASS_MAPPINGS[data[key]['class_type']]()
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if class_def.CATEGORY == 'loaders' or class_def.FUNCTION in ['encode'] or not any(isinstance(val, list) for val in data[key]['inputs'].values()):
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is_loader = True
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# If the key has not been visited, perform a DFS from that key.
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if key not in visited:
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dfs(key)
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# Reset is_loader bool
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is_loader = False
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# Loop over each key in the data.
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for key in data:
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# If the key has not been visited, perform a DFS from that key.
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if key not in visited:
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dfs(key)
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return load_order
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def create_function_call_code(obj_name: str, func: str, variable_name: str, is_loader: bool, **kwargs) -> str:
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"""
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This function generates Python code for a function call.
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Args:
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obj_name (str): The name of the initialized object.
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func (str): The function to be called.
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variable_name (str): The name of the variable that the function result should be assigned to.
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is_loader (bool): Determines the code indentation.
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**kwargs: The keyword arguments for the function.
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Returns:
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str: The generated Python code.
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"""
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def format_arg(key: str, value: any) -> str:
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"""Formats arguments based on key and value."""
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if key == 'noise_seed':
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return f'{key}=random.randint(1, 2**64)'
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elif isinstance(value, str):
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value = value.replace("\n", "\\n")
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return f'{key}="{value}"'
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elif key == 'images' and "saveimage" in obj_name and isinstance(value, dict) and 'variable_name' in value:
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return f'{key}={value["variable_name"]}.detach()'
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elif isinstance(value, dict) and 'variable_name' in value:
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return f'{key}={value["variable_name"]}'
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return f'{key}={value}'
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args = ', '.join(format_arg(key, value) for key, value in kwargs.items())
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# Generate the Python code
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code = f'{variable_name} = {obj_name}.{func}({args})\n'
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# If the code contains dependencies, indent the code because it will be placed inside
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# of a for loop
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if not is_loader:
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code = f'\t{code}'
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return code
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def update_inputs(inputs: Dict, executed_variables: Dict) -> Dict:
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"""
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Update inputs based on the executed variables.
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Args:
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inputs (Dict): Inputs dictionary to update.
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executed_variables (Dict): Dictionary storing executed variable names.
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Returns:
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Dict: Updated inputs dictionary.
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"""
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for key in inputs.keys():
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if isinstance(inputs[key], list) and inputs[key][0] in executed_variables.keys():
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inputs[key] = {'variable_name': f"{executed_variables[inputs[key][0]]}[{inputs[key][1]}]"}
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return inputs
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def get_class_info(class_type: str) -> (str, str, str):
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"""
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Generates and returns necessary information about class type.
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Args:
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class_type (str): Class type
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Returns:
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class_type (str): Updated class type
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import_statement (str): Import statement string
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class_code (str): Class initialization code
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"""
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import_statement = class_type
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class_code = f'{class_type.lower()} = {class_type}()'
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return class_type, import_statement, class_code
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def assemble_python_code(import_statements: set, loader_code: List[str], code: List[str], queue_size: int) -> str:
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"""
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Generates final code string.
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Args:
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import_statements (set): A set of unique import statements
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code (List[str]): A list of code strings
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queue_size (int): Number of photos that will be generated by the script.
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Returns:
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final_code (str): Generated final code as a string
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"""
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static_imports = ['import random', 'import torch']
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imports_code = [f"from nodes import {', '.join([class_name for class_name in import_statements])}" ]
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main_function_code = f"def main():\n\t" + 'with torch.inference_mode():\n\t\t' + '\n\t\t'.join(loader_code) + f'\n\n\t\tfor q in range({queue_size}):\n\t\t' + '\n\t\t'.join(code)
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final_code = '\n'.join(static_imports + ['import sys\nsys.path.append("../")'] + imports_code + ['', main_function_code, '', 'if __name__ == "__main__":', '\tmain()'])
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final_code = black.format_str(final_code, mode=black.Mode())
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return final_code
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def write_code_to_file(filename: str, code: str) -> None:
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"""
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Writes given code to a .py file. If the directory does not exist, it creates it.
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Args:
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filename (str): The name of the Python file to save the code to.
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code (str): The code to save.
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"""
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# Extract directory from the filename
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directory = os.path.dirname(filename)
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# If the directory does not exist, create it
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if directory and not os.path.exists(directory):
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os.makedirs(directory)
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# Save the code to a .py file
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with open(filename, 'w') as file:
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file.write(code)
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def get_function_parameters(func: Callable) -> List:
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"""Get the names of a function's parameters.
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Args:
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func (Callable): The function whose parameters we want to inspect.
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Returns:
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List: A list containing the names of the function's parameters.
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"""
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signature = inspect.signature(func)
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parameters = {name: param.default if param.default != param.empty else None
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for name, param in signature.parameters.items()}
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return list(parameters.keys())
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def generate_workflow(load_order: List, filename: str = 'generated_code_workflow.py', queue_size: int = 10) -> str:
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"""
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Generate the execution code based on the load order.
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Args:
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load_order (List): A list of tuples representing the load order.
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filename (str): The name of the Python file to which the code should be saved.
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Defaults to 'generated_code_workflow.py'.
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queue_size (int): The number of photos that will be created by the script.
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Returns:
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str: Generated execution code as a string.
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"""
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# Create the necessary data structures to hold imports and generated code
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import_statements, executed_variables, loader_code, code = set(), {}, [], []
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# This dictionary will store the names of the objects that we have already initialized
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initialized_objects = {}
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# Loop over each dictionary in the load order list
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for idx, data, is_loader in load_order:
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# Generate class definition and inputs from the data
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inputs, class_type = data['inputs'], data['class_type']
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class_def = NODE_CLASS_MAPPINGS[class_type]()
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# If the class hasn't been initialized yet, initialize it and generate the import statements
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if class_type not in initialized_objects:
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class_type, import_statement, class_code = get_class_info(class_type)
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initialized_objects[class_type] = class_type.lower()
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import_statements.add(import_statement)
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loader_code.append(class_code)
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# Get all possible parameters for class_def
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class_def_params = get_function_parameters(getattr(class_def, class_def.FUNCTION))
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# Remove any keyword arguments from **inputs if they are not in class_def_params
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inputs = {key: value for key, value in inputs.items() if key in class_def_params}
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# Create executed variable and generate code
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executed_variables[idx] = f'{class_type.lower()}_{idx}'
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inputs = update_inputs(inputs, executed_variables)
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if is_loader:
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loader_code.append(create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_loader, **inputs))
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else:
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code.append(create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_loader, **inputs))
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# Generate final code by combining imports and code, and wrap them in a main function
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final_code = assemble_python_code(import_statements, loader_code, code, queue_size)
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# Save the code to a .py file
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write_code_to_file(filename, final_code)
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return final_code
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def main(input, queue_size=10):
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"""
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Main function to be executed.
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"""
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# Load JSON data from the input file
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prompt = read_json_file(input)
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load_order = determine_load_order(prompt)
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output_file = input.replace('.json', '.py')
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code = generate_workflow(load_order, filename=output_file, queue_size=queue_size)
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logging.info(code)
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if __name__ == '__main__':
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input = 'workflow_api.json'
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queue_size = 10
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main(input, queue_size)
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