import os
import sys
import copy
import json
import threading
import heapq
import traceback
import gc
import time
import itertools
from typing import List, Dict
import dataclasses
from dataclasses import dataclass

import torch
import nodes

import comfy.model_management


@dataclass
class CombinatorialBatches:
    batches: List
    input_to_index: Dict
    index_to_values: Dict
    indices: List
    combinations: List


def find(d, pred):
    for i, x in d.items():
        if pred(x):
            return i, x
    return None, None


def get_input_data_batches(input_data_all):
    """Given input data that can contain combinatorial input values, returns all
    the possible batches that can be made by combining the different input
    values together."""

    input_to_index = {}
    index_to_values = []
    index_to_axis = {}
    index_to_coords = []

    # Sort by input name first so the order which batch inputs are applied can
    # be easily calculated (node execution order first, then alphabetical input
    # name second)
    sorted_input_names = sorted(input_data_all.keys())

    i = 0
    for input_name in sorted_input_names:
        value = input_data_all[input_name]
        if isinstance(value, dict) and "combinatorial" in value:
            if "axis_id" in value:
                found_i = next((k for k, v in index_to_axis.items() if v == value["axis_id"]), None)
            else:
                found_i = None

            if found_i is not None:
                input_to_index[input_name] = found_i
            else:
                input_to_index[input_name] = i
                index_to_values.append(value["values"])
                index_to_coords.append(list(range(len(value["values"]))))
                if "axis_id" in value:
                    index_to_axis[i] = value["axis_id"]
                i += 1

    if len(index_to_values) == 0:
        # No combinatorial options.
        return CombinatorialBatches([input_data_all], input_to_index, index_to_values, None, None)

    batches = []

    indices = list(itertools.product(*index_to_coords))
    combinations = list(itertools.product(*index_to_values))
    for combination in combinations:
        batch = {}
        for input_name, value in input_data_all.items():
            if isinstance(value, dict) and "combinatorial" in value:
                combination_index = input_to_index[input_name]
                batch[input_name] = [combination[combination_index]]
            else:
                # already made into a list by get_input_data
                batch[input_name] = value
        batches.append(batch)

    print("------------------=+++++++++++++++++")
    for batch in batches:
        print(format_dict(batch))
    print(format_dict(input_to_index))
    print(format_dict({ "v": index_to_values }))
    print(index_to_coords)
    print("------------------=+++++++++++++++++")

    return CombinatorialBatches(batches, input_to_index, index_to_values, indices, combinations)


def format_dict(d):
    s = []
    for k,v in d.items():
        st = f"{k}: "
        if isinstance(v, list):
            st += f"list[len: {len(v)}]["
            i = []
            for v2 in v:
                if isinstance(v2, (int, float, bool)):
                    i.append(str(v2))
                else:
                    i.append(v2.__class__.__name__)
            st += ",".join(i) + "]"
        else:
            if isinstance(v, (int, float, bool)):
                st += str(v)
            else:
                st += str(type(v))
        s.append(st)
    return "( " + ", ".join(s) + " )"


def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
    """Given input data from the prompt, returns a list of input data dicts for
    each combinatorial batch."""
    valid_inputs = class_def.INPUT_TYPES()
    input_data_all = {}
    for x in inputs:
        input_data = inputs[x]
        required_or_optional = ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"])
        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                return None

            # This is a list of outputs for each batch of combinatorial inputs.
            # Without any combinatorial inputs, it's a list of length 1.
            outputs_for_all_batches = outputs[input_unique_id]

            def flatten(list_of_lists):
                return list(itertools.chain.from_iterable(list_of_lists))

            if len(outputs_for_all_batches) == 1:
                # Single batch, no combinatorial stuff
                input_data_all[x] = outputs_for_all_batches[0][output_index]
            else:
                # Make the outputs into a list for map-over-list use
                # (they are themselves lists so flatten them afterwards)
                input_values = [batch_output[output_index] for batch_output in outputs_for_all_batches]
                print("COMB")
                print(str(input_unique_id))
                print(str(output_index))
                print(format_dict({ "values": input_values }))
                input_values = {
                    "combinatorial": True,
                    "values": flatten(input_values),
                    "axis_id": prompt[input_unique_id].get("axis_id")
                }
                input_data_all[x] = input_values
        elif is_combinatorial_input(input_data):
            if required_or_optional:
                input_data_all[x] = {
                    "combinatorial": True,
                    "values": input_data["values"],
                    "axis_id": input_data.get("axis_id")
                }
        else:
            if required_or_optional:
                input_data_all[x] = [input_data]

    if "hidden" in valid_inputs:
        h = valid_inputs["hidden"]
        for x in h:
            if h[x] == "PROMPT":
                input_data_all[x] = [prompt]
            if h[x] == "EXTRA_PNGINFO":
                if "extra_pnginfo" in extra_data:
                    input_data_all[x] = [extra_data['extra_pnginfo']]
            if h[x] == "UNIQUE_ID":
                input_data_all[x] = [unique_id]

    input_data_all_batches = get_input_data_batches(input_data_all)

    print("---------------------------------")
    from pprint import pp
    for batch in input_data_all_batches.batches:
        print(format_dict(batch));
    print("---------------------------------")

    return input_data_all_batches

def slice_lists_into_dict(d, i):
    """
    get a slice of inputs, repeat last input when list isn't long enough
    d={ "seed": [ 1, 2, 3 ], "steps": [ 4, 8 ] }, i=2 -> { "seed": 3, "steps": 8 }
    """
    d_new = {}
    for k, v in d.items():
        d_new[k] = v[i if len(v) > i else -1]
    return d_new

def map_node_over_list(obj, input_data_all, func, allow_interrupt=False, callback=None):
    # check if node wants the lists
    input_is_list = False
    if hasattr(obj, "INPUT_IS_LIST"):
        input_is_list = obj.INPUT_IS_LIST

    max_len_input = max(len(x) for x in input_data_all.values())

    results = []
    if input_is_list:
        if allow_interrupt:
            nodes.before_node_execution()
        results.append(getattr(obj, func)(**input_data_all))
    else: 
        for i in range(max_len_input):
            if allow_interrupt:
                nodes.before_node_execution()
            results.append(getattr(obj, func)(**slice_lists_into_dict(input_data_all, i)))
            if callback is not None:
                callback(i + 1, max_len_input)
    return results

def get_output_data(obj, input_data_all_batches, server, unique_id, prompt_id):
    all_outputs = []
    all_outputs_ui = []
    total_batches = len(input_data_all_batches.batches)

    total_inner_batches = 0
    for batch in input_data_all_batches.batches:
        total_inner_batches += max(len(x) for x in batch.values())

    inner_totals = 0

    def send_batch_progress(inner_num):
        if server.client_id is not None:
            message = {
                "node": unique_id,
                "prompt_id": prompt_id,
                "batch_num": inner_totals + inner_num,
                "total_batches": total_inner_batches
            }
            server.send_sync("batch_progress", message, server.client_id)

    send_batch_progress(0)

    for batch_num, batch in enumerate(input_data_all_batches.batches):
        def cb(inner_num, inner_total):
            send_batch_progress(inner_num)

        return_values = map_node_over_list(obj, batch, obj.FUNCTION, allow_interrupt=True, callback=cb)

        inner_totals += max(len(x) for x in batch.values())

        uis = []
        results = []

        for r in return_values:
            if isinstance(r, dict):
                if 'ui' in r:
                    uis.append(r['ui'])
                if 'result' in r:
                    results.append(r['result'])
            else:
                results.append(r)

        output = []
        if len(results) > 0:
            # check which outputs need concatenating
            output_is_list = [False] * len(results[0])
            if hasattr(obj, "OUTPUT_IS_LIST"):
                output_is_list = obj.OUTPUT_IS_LIST

            # merge node execution results
            for i, is_list in zip(range(len(results[0])), output_is_list):
                if is_list:
                    output.append([x for o in results for x in o[i]])
                else:
                    output.append([o[i] for o in results])

        output_ui = None
        if len(uis) > 0:
            output_ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}

        all_outputs.append(output)
        all_outputs_ui.append(output_ui)

        outputs_ui_to_send = None
        if any(all_outputs_ui):
            outputs_ui_to_send = all_outputs_ui

        # update the UI after each batch finishes
        if server.client_id is not None:
            message = {
                "node": unique_id,
                "output": outputs_ui_to_send,
                "prompt_id": prompt_id,
                "batch_num": inner_totals,
                "total_batches": total_inner_batches,
            }
            if input_data_all_batches.indices:
                message["indices"] = input_data_all_batches.indices[batch_num]
            server.send_sync("executed", message, server.client_id)

    return all_outputs, all_outputs_ui

def format_value(x):
    if x is None:
        return None
    elif isinstance(x, (int, float, bool, str)):
        return x
    else:
        return str(x)

def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui):
    unique_id = current_item
    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
    if unique_id in outputs:
        return (True, None, None)

    for x in inputs:
        input_data = inputs[x]

        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui)
                if result[0] is not True:
                    # Another node failed further upstream
                    return result

    input_data_all_batches = None
    try:
        input_data_all_batches = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
        if server.client_id is not None:
            server.last_node_id = unique_id
            combinations = None
            if input_data_all_batches.indices:
                combinations = {
                    "input_to_index": input_data_all_batches.input_to_index,
                    "indices": input_data_all_batches.indices
                }
            mes = {
                "node": unique_id,
                "prompt_id": prompt_id,
                "combinations": combinations
            }
            server.send_sync("executing", mes, server.client_id)

        obj = class_def()

        output_data_from_batches, output_ui_from_batches = get_output_data(obj, input_data_all_batches, server, unique_id, prompt_id)
        outputs[unique_id] = output_data_from_batches
        if any(output_ui_from_batches):
            outputs_ui[unique_id] = output_ui_from_batches
        elif unique_id in outputs_ui:
            outputs_ui.pop(unique_id)
    except comfy.model_management.InterruptProcessingException as iex:
        print("Processing interrupted")

        # skip formatting inputs/outputs
        error_details = {
            "node_id": unique_id,
        }

        return (False, error_details, iex)
    except Exception as ex:
        typ, _, tb = sys.exc_info()
        exception_type = full_type_name(typ)

        print("!!! Exception during processing !!!")
        print(traceback.format_exc())

        input_data_formatted = []
        if input_data_all_batches is not None:
            d = {}
            for batch in input_data_all_batches.batches:
                for name, inputs in batch.items():
                    d[name] = [format_value(x) for x in inputs]
                input_data_formatted.append(d)

        output_data_formatted = []
        for node_id, node_outputs in outputs.items():
            d = {}
            for batch_outputs in node_outputs:
                d[node_id] = [[format_value(x) for x in l] for l in batch_outputs]
            output_data_formatted.append(d)

        error_details = {
            "node_id": unique_id,
            "exception_message": str(ex),
            "exception_type": exception_type,
            "traceback": traceback.format_tb(tb),
            "current_inputs": input_data_formatted,
            "current_outputs": output_data_formatted
        }
        return (False, error_details, ex)

    executed.add(unique_id)

    return (True, None, None)

def recursive_will_execute(prompt, outputs, current_item):
    unique_id = current_item
    inputs = prompt[unique_id]['inputs']
    will_execute = []
    if unique_id in outputs:
        return []

    for x in inputs:
        input_data = inputs[x]
        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                will_execute += recursive_will_execute(prompt, outputs, input_unique_id)

    return will_execute + [unique_id]

def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
    unique_id = current_item
    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]

    is_changed_old = ''
    is_changed = ''
    to_delete = False
    if hasattr(class_def, 'IS_CHANGED'):
        if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
            is_changed_old = old_prompt[unique_id]['is_changed']
        if 'is_changed' not in prompt[unique_id]:
            input_data_all_batches = get_input_data(inputs, class_def, unique_id, outputs, prompt)
            if input_data_all_batches is not None:
                 try:
                    #is_changed = class_def.IS_CHANGED(**input_data_all)
                    for batch in input_data_all_batches.batches:
                        if map_node_over_list(class_def, batch, "IS_CHANGED"):
                            is_changed = True
                            break
                    prompt[unique_id]['is_changed'] = is_changed
                 except:
                    to_delete = True
        else:
            is_changed = prompt[unique_id]['is_changed']

    if unique_id not in outputs:
        return True

    if not to_delete:
        if is_changed != is_changed_old:
            to_delete = True
        elif unique_id not in old_prompt:
            to_delete = True
        elif inputs == old_prompt[unique_id]['inputs']:
            for x in inputs:
                input_data = inputs[x]

                if isinstance(input_data, list):
                    input_unique_id = input_data[0]
                    output_index = input_data[1]
                    if input_unique_id in outputs:
                        to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
                    else:
                        to_delete = True
                    if to_delete:
                        break
        else:
            to_delete = True

    if to_delete:
        d = outputs.pop(unique_id)
        del d
    return to_delete

class PromptExecutor:
    def __init__(self, server):
        self.outputs = {}
        self.outputs_ui = {}
        self.old_prompt = {}
        self.server = server

    def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
        node_id = error["node_id"]
        class_type = prompt[node_id]["class_type"]

        # First, send back the status to the frontend depending
        # on the exception type
        if isinstance(ex, comfy.model_management.InterruptProcessingException):
            mes = {
                "prompt_id": prompt_id,
                "node_id": node_id,
                "node_type": class_type,
                "executed": list(executed),
            }
            self.server.send_sync("execution_interrupted", mes, self.server.client_id)
        else:
            if self.server.client_id is not None:
                mes = {
                    "prompt_id": prompt_id,
                    "node_id": node_id,
                    "node_type": class_type,
                    "executed": list(executed),

                    "exception_message": error["exception_message"],
                    "exception_type": error["exception_type"],
                    "traceback": error["traceback"],
                    "current_inputs": error["current_inputs"],
                    "current_outputs": error["current_outputs"],
                }
                self.server.send_sync("execution_error", mes, self.server.client_id)

        # Next, remove the subsequent outputs since they will not be executed
        to_delete = []
        for o in self.outputs:
            if (o not in current_outputs) and (o not in executed):
                to_delete += [o]
                if o in self.old_prompt:
                    d = self.old_prompt.pop(o)
                    del d
        for o in to_delete:
            d = self.outputs.pop(o)
            del d

    def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
        nodes.interrupt_processing(False)

        if "client_id" in extra_data:
            self.server.client_id = extra_data["client_id"]
        else:
            self.server.client_id = None

        execution_start_time = time.perf_counter()
        if self.server.client_id is not None:
            self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id)

        with torch.inference_mode():
            #delete cached outputs if nodes don't exist for them
            to_delete = []
            for o in self.outputs:
                if o not in prompt:
                    to_delete += [o]
            for o in to_delete:
                d = self.outputs.pop(o)
                del d

            for x in prompt:
                recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)

            current_outputs = set(self.outputs.keys())
            for x in list(self.outputs_ui.keys()):
                if x not in current_outputs:
                    d = self.outputs_ui.pop(x)
                    del d

            if self.server.client_id is not None:
                self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id)
            executed = set()
            output_node_id = None
            to_execute = []

            for node_id in list(execute_outputs):
                to_execute += [(0, node_id)]

            while len(to_execute) > 0:
                #always execute the output that depends on the least amount of unexecuted nodes first
                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
                output_node_id = to_execute.pop(0)[-1]

                # This call shouldn't raise anything if there's an error deep in
                # the actual SD code, instead it will report the node where the
                # error was raised
                success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui)
                if success is not True:
                    self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
                    break

            for x in executed:
                self.old_prompt[x] = copy.deepcopy(prompt[x])
            self.server.last_node_id = None
            if self.server.client_id is not None:
                self.server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, self.server.client_id)

        print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
        gc.collect()
        comfy.model_management.soft_empty_cache()


def is_combinatorial_input(val):
    return isinstance(val, dict) and "__inputType__" in val


def get_raw_inputs(raw_val):
    if isinstance(raw_val, list):
        # link to another node
        return [raw_val]
    elif is_combinatorial_input(raw_val):
        return raw_val["values"]
    return [raw_val]


def clamp_input(val, info, class_type, obj_class, x):
    errors = []

    if is_combinatorial_input(val):
        if len(val["values"]) == 0:
            error = {
                "type": "combinatorial_input_missing_values",
                "message": f"Combinatorial input has no values in its list.",
                "details": f"{x}",
                "extra_info": {
                    "input_name": x,
                    "input_config": info,
                    "received_value": val,
                }
            }
            return (False, None, error)
        for i, val_choice in enumerate(val["values"]):
            r = clamp_input(val_choice, info, class_type, obj_class, x)
            if r[0] == False:
                return r
            val["values"][i] = r[1]
        return (True, val, None)

    type_input = info[0]

    try:
        if type_input == "INT":
            val = int(val)
        if type_input == "FLOAT":
            val = float(val)
        if type_input == "STRING":
            val = str(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)
            }
        }
        return (False, None, error)

    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,
                }
            }
            return (False, None, error)
        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,
                }
            }
            return (False, None, error)

    return (True, val, None)


def validate_inputs(prompt, item, validated):
    unique_id = item
    if unique_id in validated:
        return validated[unique_id]

    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]

    class_inputs = obj_class.INPUT_TYPES()
    required_inputs = class_inputs['required']

    errors = []
    valid = True

    for x in required_inputs:
        if x not in inputs:
            error = {
                "type": "required_input_missing",
                "message": "Required input is missing",
                "details": f"{x}",
                "extra_info": {
                    "input_name": x
                }
            }
            errors.append(error)
            continue

        val = inputs[x]
        info = required_inputs[x]
        type_input = info[0]
        if isinstance(val, list):
            if len(val) != 2:
                error = {
                    "type": "bad_linked_input",
                    "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
                    "details": f"{x}",
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "received_value": val
                    }
                }
                errors.append(error)
                continue

            o_id = val[0]
            o_class_type = prompt[o_id]['class_type']
            r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
            if r[val[1]] != type_input:
                received_type = r[val[1]]
                details = f"{x}, {received_type} != {type_input}"
                error = {
                    "type": "return_type_mismatch",
                    "message": "Return type mismatch between linked nodes",
                    "details": details,
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "received_type": received_type,
                        "linked_node": val
                    }
                }
                errors.append(error)
                continue
            try:
                r = validate_inputs(prompt, o_id, validated)
                if r[0] is False:
                    # `r` will be set in `validated[o_id]` already
                    valid = False
                    continue
            except Exception as ex:
                typ, _, tb = sys.exc_info()
                valid = False
                exception_type = full_type_name(typ)
                reasons = [{
                    "type": "exception_during_inner_validation",
                    "message": "Exception when validating inner node",
                    "details": str(ex),
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "exception_message": str(ex),
                        "exception_type": exception_type,
                        "traceback": traceback.format_tb(tb),
                        "linked_node": val
                    }
                }]
                validated[o_id] = (False, reasons, o_id)
                continue
        else:
            r = clamp_input(val, info, class_type, obj_class, x)
            if r[0] == False:
                errors.append(r[2])
                continue
            else:
                inputs[x] = r[1]

            if hasattr(obj_class, "VALIDATE_INPUTS"):
                input_data_all_batches = get_input_data(inputs, obj_class, unique_id, {}, prompt)
                #ret = obj_class.VALIDATE_INPUTS(**input_data_all)
                for batch in input_data_all_batches.batches:
                    ret = map_node_over_list(obj_class, batch, "VALIDATE_INPUTS")
                    for r in ret:
                        if r != 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
            else:
                if isinstance(type_input, list):
                    # Account for more than one combinatorial value
                    raw_vals = get_raw_inputs(val)
                    for raw_val in raw_vals:
                        if raw_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}: '{raw_val}' not in {list_info}",
                                "extra_info": {
                                    "input_name": x,
                                    "input_config": input_config,
                                    "received_value": raw_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:
            print(f"Failed to validate prompt for output {o}:")
            if len(reasons) > 0:
                print("* (prompt):")
                for reason in reasons:
                    print(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
                        }
                        print(f"* {class_type} {node_id}:")
                        for reason in reasons:
                            print(f"  - {reason['message']}: {reason['details']}")
                    node_errors[node_id]["dependent_outputs"].append(o)
            print("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)


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 = {}
        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):
        with self.not_empty:
            while len(self.queue) == 0:
                self.not_empty.wait()
            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)

    def task_done(self, item_id, outputs):
        with self.mutex:
            prompt = self.currently_running.pop(item_id)
            self.history[prompt[1]] = { "prompt": prompt, "outputs": {} }
            for o in outputs:
                self.history[prompt[1]]["outputs"][o] = outputs[o]
            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):
        with self.mutex:
            return copy.deepcopy(self.history)

    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)