import copy
import glob
import inspect
import json
import os
import random
import sys
import re
from typing import Dict, List, Any, Callable, Tuple, TextIO

import black


from comfyui_to_python_utils import import_custom_nodes, find_path, add_comfyui_directory_to_sys_path, add_extra_model_paths,\
                                    get_value_at_index, parse_arg, save_image_wrapper

PACKAGED_FUNCTIONS = [
    get_value_at_index,
    find_path,
    add_comfyui_directory_to_sys_path,
    add_extra_model_paths,
    import_custom_nodes,
    save_image_wrapper,
    parse_arg
]

add_comfyui_directory_to_sys_path()
from nodes import NODE_CLASS_MAPPINGS
import nodes

class FileHandler:
    """Handles reading and writing files.

    This class provides methods to read JSON data from an input file and write code to an output file (either file-like objects or string paths).
    """

    @staticmethod
    def read_json_file(file_path: str | TextIO, encoding: str = "utf-8") -> dict:
        """
        Reads a JSON file and returns its contents as a dictionary.

        Args:
            file_path (str): The path to the JSON file.

        Returns:
            dict: The contents of the JSON file as a dictionary.

        Raises:
            FileNotFoundError: If the file is not found, it lists all JSON files in the directory of the file path.
            ValueError: If the file is not a valid JSON.
        """

        if hasattr(file_path, "read"): return json.load(file_path)
        with open(file_path, 'r', encoding="utf-8") as file:
            data = json.load(file)
        return data

    @staticmethod
    def write_code_to_file(file_path: str | TextIO, code: str) -> None:
        """Write the specified code to a Python file.

        Args:
            file_path (str): The path to the Python file.
            code (str): The code to write to the file.

        Returns:
            None
        """
        if isinstance(file_path, str):
            # Extract directory from the filename
            directory = os.path.dirname(file_path)

            # If the directory does not exist, create it
            if directory and not os.path.exists(directory):
                os.makedirs(directory)

            # Save the code to a .py file
            with open(file_path, 'w', encoding="utf-8") as file:
                file.write(code)
        else:
            file_path.write(code)


class LoadOrderDeterminer:
    """Determine the load order of each key in the provided dictionary.

    This class places the nodes without node dependencies first, then ensures that any node whose
    result is used in another node will be added to the list in the order it should be executed.

    Attributes:
        data (Dict): The dictionary for which to determine the load order.
        node_class_mappings (Dict): Mappings of node classes.
    """

    def __init__(self, data: Dict, node_class_mappings: Dict):
        """Initialize the LoadOrderDeterminer with the given data and node class mappings.

        Args:
            data (Dict): The dictionary for which to determine the load order.
            node_class_mappings (Dict): Mappings of node classes.
        """
        self.data = data
        self.node_class_mappings = node_class_mappings
        self.visited = {}
        self.load_order = []
        self.is_special_function = False

    def determine_load_order(self) -> List[Tuple[str, Dict, bool]]:
        """Determine the load order for the given data.

        Returns:
            List[Tuple[str, Dict, bool]]: A list of tuples representing the load order.
        """
        self._load_special_functions_first()
        self.is_special_function = False
        for key in self.data:
            if key not in self.visited:
                self._dfs(key)
        return self.load_order

    def _dfs(self, key: str) -> None:
        """Depth-First Search function to determine the load order.

        Args:
            key (str): The key from which to start the DFS.

        Returns:
            None
        """
        # Mark the node as visited.
        self.visited[key] = True
        inputs = self.data[key]['inputs']
        # Loop over each input key.
        for input_key, val in inputs.items():
            # If the value is a list and the first item in the list has not been visited yet,
            # then recursively apply DFS on the dependency.
            if isinstance(val, list) and val[0] not in self.visited:
                self._dfs(val[0])
        # Add the key and its corresponding data to the load order list.
        self.load_order.append((key, self.data[key], self.is_special_function))

    def _load_special_functions_first(self) -> None:
        """Load functions without dependencies, loaderes, and encoders first.

        Returns:
            None
        """
        # Iterate over each key in the data to check for loader keys.
        for key in self.data:
            class_def = self.node_class_mappings[self.data[key]['class_type']]()
            # Check if the class is a loader class or meets specific conditions.
            if (class_def.CATEGORY == 'loaders' or 
                class_def.FUNCTION in ['encode'] or 
                not any(isinstance(val, list) for val in self.data[key]['inputs'].values())):
                self.is_special_function = True
                # If the key has not been visited, perform a DFS from that key.
                if key not in self.visited:
                    self._dfs(key)


class CodeGenerator:
    """Generates Python code for a workflow based on the load order.

    Attributes:
        node_class_mappings (Dict): Mappings of node classes.
        base_node_class_mappings (Dict): Base mappings of node classes.
    """

    def __init__(self, node_class_mappings: Dict, base_node_class_mappings: Dict, prompt: Dict):
        """Initialize the CodeGenerator with given node class mappings.

        Args:
            node_class_mappings (Dict): Mappings of node classes.
            base_node_class_mappings (Dict): Base mappings of node classes.
        """
        self.node_class_mappings = node_class_mappings
        self.base_node_class_mappings = base_node_class_mappings
        self.prompt = prompt
    
    def can_be_imported(self, import_name: str):
        if import_name in self.base_node_class_mappings.keys():
            if getattr(nodes, import_name, None) is not None:
                return True
        
        return False

    def generate_workflow(self, load_order: List, queue_size: int = 1) -> str:
        """Generate the execution code based on the load order.

        Args:
            load_order (List): A list of tuples representing the load order.
            filename (str): The name of the Python file to which the code should be saved.
                Defaults to 'generated_code_workflow.py'.
            queue_size (int): The number of photos that will be created by the script.

        Returns:
            str: Generated execution code as a string.
        """
        include_prompt_data = False
        # Create the necessary data structures to hold imports and generated code
        import_statements, executed_variables, arg_inputs, special_functions_code, code = set(['NODE_CLASS_MAPPINGS']), {}, [], [], []
        # This dictionary will store the names of the objects that we have already initialized
        initialized_objects = {}

        custom_nodes = False
        # Loop over each dictionary in the load order list
        for idx, data, is_special_function in load_order:
            # Generate class definition and inputs from the data
            inputs, class_type = data['inputs'], data['class_type']

            input_types = self.node_class_mappings[class_type].INPUT_TYPES()
            missing = []
            for i, input in enumerate(input_types.get("required", {}).keys()):
                if input not in inputs:
                    input_var = f"{input}{len(arg_inputs)+1}"
                    arg_inputs.append((input_var, f"Argument {i}, input `{input}` for node \\\"{data.get('_meta', {}).get('title', class_type)}\\\" id {idx}"))
                    print("WARNING: Missing required input", input, "for", class_type)
                    print("That will be CLI arg " + str(len(arg_inputs)))
                    missing.append((input, input_var, len(arg_inputs)))

            class_def = self.node_class_mappings[class_type]()

            # If the class hasn't been initialized yet, initialize it and generate the import statements
            if class_type not in initialized_objects:
                # No need to use preview image nodes since we are executing the script in a terminal
                if class_type == 'PreviewImage':
                    continue
                
                class_type, import_statement, class_code = self.get_class_info(class_type)
                initialized_objects[class_type] = self.clean_variable_name(class_type)
                if self.can_be_imported(class_type):
                    import_statements.add(import_statement)
                if class_type not in self.base_node_class_mappings.keys():
                    custom_nodes = True
                special_functions_code.append(class_code)

            # Get all possible parameters for class_def
            class_def_params = self.get_function_parameters(getattr(class_def, class_def.FUNCTION))
            no_params = class_def_params is None

            # Remove any keyword arguments from **inputs if they are not in class_def_params
            inputs = {key: value for key, value in inputs.items() if no_params or key in class_def_params}
            for input, input_var, arg in missing:
                inputs[input] = {"variable_name": f"parse_arg(args." + input_var + ")"}
            # Deal with hidden variables
            if class_def_params is not None:
                if 'unique_id' in class_def_params:
                    inputs['unique_id'] = random.randint(1, 2**64)
                if 'prompt' in class_def_params:
                    inputs["prompt"] = {"variable_name": "PROMPT_DATA"}
                    include_prompt_data = True

            # Create executed variable and generate code
            executed_variables[idx] = f'{self.clean_variable_name(class_type)}_{idx}'
            inputs = self.update_inputs(inputs, executed_variables)
            
            if class_type == 'SaveImage':
                save_code = self.create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_special_function, **inputs).strip()
                return_code = ['if __name__ != "__main__":', '\treturn dict(' + ', '.join(self.format_arg(key, value) for key, value in inputs.items()) + ')', 'else:', '\t' + save_code]

                if is_special_function:
                    special_functions_code.extend(return_code)
                else:
                    code.extend(return_code) ### This should presumably NEVER occur for a valid workflow
            else:
                if is_special_function:
                    special_functions_code.append(self.create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_special_function, **inputs))
                else:
                    code.append(self.create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_special_function, **inputs))

        # Generate final code by combining imports and code, and wrap them in a main function
        final_code = self.assemble_python_code(import_statements, special_functions_code, arg_inputs, code, queue_size, custom_nodes, include_prompt_data)

        return final_code

    def create_function_call_code(self, obj_name: str, func: str, variable_name: str, is_special_function: bool, **kwargs) -> str:
        """Generate Python code for a function call.

        Args:
            obj_name (str): The name of the initialized object.
            func (str): The function to be called.
            variable_name (str): The name of the variable that the function result should be assigned to.
            is_special_function (bool): Determines the code indentation.
            **kwargs: The keyword arguments for the function.

        Returns:
            str: The generated Python code.
        """
        args = ', '.join(self.format_arg(key, value) for key, value in kwargs.items())

        # Generate the Python code
        code = f'{variable_name} = {obj_name}.{func}({args})\n'

        return code

    def format_arg(self, key: str, value: any) -> str:
        """Formats arguments based on key and value.

        Args:
            key (str): Argument key.
            value (any): Argument value.

        Returns:
            str: Formatted argument as a string.
        """
        # Randomize the seed if it's a set value
        if isinstance(value, int) and (key == 'noise_seed' or key == 'seed'):
            return f'{key}=random.randint(1, 2**64)'
        elif isinstance(value, str):
            return f'{key}={repr(value)}'
        elif isinstance(value, dict) and 'variable_name' in value:
            return f'{key}={value["variable_name"]}'
        return f'{key}={value}'

    def assemble_python_code(self, import_statements: set, special_functions_code: List[str], arg_inputs: List[Tuple[str, str]], code: List[str], queue_size: int, custom_nodes=False, include_prompt_data=True) -> str:
        """Generates the final code string.

        Args:
            import_statements (set): A set of unique import statements.
            speical_functions_code (List[str]): A list of special functions code strings.
            code (List[str]): A list of code strings.
            queue_size (int): Number of photos that will be generated by the script.
            custom_nodes (bool): Whether to include custom nodes in the code.

        Returns:
            str: Generated final code as a string.
        """
        # Get the source code of the utils functions as a string
        func_strings = []
        for func in PACKAGED_FUNCTIONS:
            func_strings.append(f'\n{inspect.getsource(func)}')
        
        argparse_code = [f'parser = argparse.ArgumentParser(description="A converted ComfyUI workflow. Required inputs listed below. Values passed should be valid JSON (assumes string if not valid JSON).")']
        for i, (input_name, arg_desc) in enumerate(arg_inputs):
            argparse_code.append(f'parser.add_argument("{input_name}", help="{arg_desc} (autogenerated)")\n')
        argparse_code.append(f'parser.add_argument("--queue-size", "-q", type=int, default={queue_size}, help="How many times the workflow will be executed (default: {queue_size})")\n')
        argparse_code.append('parser.add_argument("--comfyui-directory", "-c", default=None, help="Where to look for ComfyUI (default: current directory)")\n')
        argparse_code.append(f'parser.add_argument("--output", "-o", default=None, help="The location to save the output image. Either a file path, a directory, or - for stdout (default: the ComfyUI output directory)")\n')
        argparse_code.append(f'parser.add_argument("--disable-metadata", action="store_true", help="Disables writing workflow metadata to the outputs")\n')
        argparse_code.append('''
comfy_args = [sys.argv[0]]
if __name__ == "__main__" and "--" in sys.argv:
    idx = sys.argv.index("--")
    comfy_args += sys.argv[idx+1:]
    sys.argv = sys.argv[:idx]

args = None
if __name__ == "__main__":
    args = parser.parse_args()
    sys.argv = comfy_args
if args is not None and args.output is not None and args.output == "-":
    ctx = contextlib.redirect_stdout(sys.stderr)
else:
    ctx = contextlib.nullcontext()
''')
        
        # Define static import statements required for the script
        static_imports = ['import os', 'import random', 'import sys', 'import json', 'import argparse', 'import contextlib', 'from typing import Sequence, Mapping, Any, Union', 
                          'import torch'] + func_strings + argparse_code
        if include_prompt_data:
            static_imports.append(f'PROMPT_DATA = json.loads({repr(json.dumps(self.prompt))})')
        # Check if custom nodes should be included
        if custom_nodes:
            static_imports.append(f'\n{inspect.getsource(import_custom_nodes)}\n')
        newline_doubletab = '\n\t\t' # You can't use backslashes in f-strings
        newline_tripletab = '\n\t\t\t' # Same
        # Assemble the main function code, including custom nodes if applicable
        main_function_code = f"""
_custom_nodes_imported = {str(not custom_nodes)}
_custom_path_added = False

def main(*func_args, **func_kwargs):
    global args, _custom_nodes_imported, _custom_path_added
    if __name__ == "__main__":
        if args is None:
            args = parser.parse_args()
    else:
        defaults = dict((arg, parser.get_default(arg)) for arg in ['queue_size', 'comfyui_directory', 'output', 'disable_metadata'])
        ordered_args = dict(zip({[input_name for input_name, _ in arg_inputs]}, func_args))

        all_args = dict()
        all_args.update(defaults)
        all_args.update(ordered_args)
        all_args.update(func_kwargs)

        args = argparse.Namespace(**all_args)
    
    with ctx:
        if not _custom_path_added:
            add_comfyui_directory_to_sys_path()
            add_extra_model_paths()

            _custom_path_added = True
        
        if not _custom_nodes_imported:
            import_custom_nodes()

            _custom_nodes_imported = True
        
        from nodes import {', '.join([class_name for class_name in import_statements])}
    
    with torch.inference_mode(), ctx:
        {newline_doubletab.join(special_functions_code)}
        for q in range(args.queue_size):
            {newline_tripletab.join(code)}""".replace("    ", "\t")
        # Concatenate all parts to form the final code
        final_code = '\n'.join(static_imports + [main_function_code, '', 'if __name__ == "__main__":', '\tmain()'])
        # Format the final code according to PEP 8 using the Black library
        final_code = black.format_str(final_code, mode=black.Mode())

        return final_code
    
    def get_class_info(self, class_type: str) -> Tuple[str, str, str]:
        """Generates and returns necessary information about class type.

        Args:
            class_type (str): Class type.

        Returns:
            Tuple[str, str, str]: Updated class type, import statement string, class initialization code.
        """
        import_statement = class_type
        variable_name = self.clean_variable_name(class_type)
        before = ""
        after = ""
        if class_type.strip() == 'SaveImage':
            before = 'save_image_wrapper(' + 'ctx, '
            after = ')'
        
        if self.can_be_imported(class_type):
            class_code = f'{variable_name} = {before}{class_type.strip()}{after}()'
        else:
            class_code = f'{variable_name} = {before}NODE_CLASS_MAPPINGS["{class_type}"]{after}()'

        return class_type, import_statement, class_code
    
    @staticmethod
    def clean_variable_name(class_type: str) -> str:
        """
        Remove any characters from variable name that could cause errors running the Python script.

        Args:
            class_type (str): Class type.

        Returns:
            str: Cleaned variable name with no special characters or spaces
        """
        # Convert to lowercase and replace spaces with underscores
        clean_name = class_type.lower().strip().replace("-", "_").replace(" ", "_")
        
        # Remove characters that are not letters, numbers, or underscores
        clean_name = re.sub(r'[^a-z0-9_]', '', clean_name)
        
        # Ensure that it doesn't start with a number
        if clean_name[0].isdigit():
            clean_name = "_" + clean_name
        
        return clean_name

    def get_function_parameters(self, func: Callable) -> List:
        """Get the names of a function's parameters.

        Args:
            func (Callable): The function whose parameters we want to inspect.

        Returns:
            List: A list containing the names of the function's parameters.
        """
        signature = inspect.signature(func)
        parameters = {name: param.default if param.default != param.empty else None
                    for name, param in signature.parameters.items()}
        catch_all = any(param.kind == inspect.Parameter.VAR_KEYWORD for param in signature.parameters.values())
        return list(parameters.keys()) if not catch_all else None

    def update_inputs(self, inputs: Dict, executed_variables: Dict) -> Dict:
        """Update inputs based on the executed variables.

        Args:
            inputs (Dict): Inputs dictionary to update.
            executed_variables (Dict): Dictionary storing executed variable names.

        Returns:
            Dict: Updated inputs dictionary.
        """
        for key in inputs.keys():
            if isinstance(inputs[key], list) and inputs[key][0] in executed_variables.keys():
                inputs[key] = {'variable_name': f"get_value_at_index({executed_variables[inputs[key][0]]}, {inputs[key][1]})"}
        return inputs
    

class ComfyUItoPython:
    """Main workflow to generate Python code from a workflow_api.json file.

    Attributes:
        input_file (str): Path to the input JSON file.
        output_file (str): Path to the output Python file.
        queue_size (int): The number of photos that will be created by the script.
        node_class_mappings (Dict): Mappings of node classes.
        base_node_class_mappings (Dict): Base mappings of node classes.
    """

    def __init__(self, workflow: str = "", input_file: str = "", output_file: (str | TextIO) = "", queue_size: int = 1, node_class_mappings: Dict = NODE_CLASS_MAPPINGS,
                 needs_init_custom_nodes: bool = False):
        """Initialize the ComfyUItoPython class with the given parameters. Exactly one of workflow or input_file must be specified.

        Args:
            workflow (str): The workflow's JSON.
            input_file (str): Path to the input JSON file.
            output_file (str | TextIO): Path to the output file or a file-like object.
            queue_size (int): The number of times a workflow will be executed by the script. Defaults to 1.
            node_class_mappings (Dict): Mappings of node classes. Defaults to NODE_CLASS_MAPPINGS.
            needs_init_custom_nodes (bool): Whether to initialize custom nodes. Defaults to False.
        """
        if input_file and workflow:
            raise ValueError("Can't provide both input_file and workflow")
        elif not input_file and not workflow:
            raise ValueError("Needs input_file or workflow")
        
        if not output_file:
            raise ValueError("Needs output_file")
        
        self.workflow = workflow
        self.input_file = input_file
        self.output_file = output_file
        self.queue_size = queue_size
        self.node_class_mappings = node_class_mappings
        self.needs_init_custom_nodes = needs_init_custom_nodes
        
        self.base_node_class_mappings = copy.deepcopy(self.node_class_mappings)
        self.execute()

    def execute(self):
        """Execute the main workflow to generate Python code.

        Returns:
            None
        """
        # Step 1: Import all custom nodes if we need to
        if self.needs_init_custom_nodes:
            import_custom_nodes()
        else:
            # If they're already imported, we don't know which nodes are custom nodes, so we need to import all of them
            self.base_node_class_mappings = {}

        # Step 2: Read JSON data from the input file
        if self.input_file:
            data = FileHandler.read_json_file(self.input_file)
        else:
            data = json.loads(self.workflow)

        # Step 3: Determine the load order
        load_order_determiner = LoadOrderDeterminer(data, self.node_class_mappings)
        load_order = load_order_determiner.determine_load_order()

        # Step 4: Generate the workflow code
        code_generator = CodeGenerator(self.node_class_mappings, self.base_node_class_mappings, data)
        generated_code = code_generator.generate_workflow(load_order, queue_size=self.queue_size)

        # Step 5: Write the generated code to a file
        FileHandler.write_code_to_file(self.output_file, generated_code)

        print(f"Code successfully generated and written to {self.output_file}")


if __name__ == '__main__':
    import argparse
    
    ap = argparse.ArgumentParser(description="Converts a ComfyUI-style workflow.json file to a Python file. Must have been exported with API calls")
    
    ap.add_argument("workflow", help="The workflow.json file to convert")
    ap.add_argument("--output", "-o", default=None, help="The output file (defaults to [input file].py)")
    ap.add_argument("--queue-size", "-q", default=1, type=int, help="The queue size per run")
    ap.add_argument("--yes", "--overwrite", "-y", action="store_true", help="Overwrite the output file if it exists")
    
    args = ap.parse_args()
    
    output = args.output if args.output else args.workflow + ".py"
    if os.path.isfile(output):
        if not args.yes:
            if input("Are you sure you want to overwrite " + output + "?\nY/n").strip().lower() not in ("y", "yes"):
                print("Exiting.")
                sys.exit(1)

    # Convert ComfyUI workflow to Python
    ComfyUItoPython(input_file=args.workflow, output_file=output, queue_size=args.queue_size, needs_init_custom_nodes=True)