
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def 						lowercase_      (       A__			,		A__=False		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= OmegaConf.load(A__		)
    if display:
        print(yaml.dump(OmegaConf.to_container(A__		)		)		)
    return config



def 						lowercase_      (       A__			,		A__=None			,		A__=None		)							->							str:
    """simple docstring"""

    if conf_path is None:
        snake_case								= "./model_checkpoints/vqgan_only.yaml"
    snake_case								= load_config(A__			,		display=A__		)
    snake_case								= VQModel(**config.model.params		)
    if ckpt_path is None:
        snake_case								= "./model_checkpoints/vqgan_only.pt"
    snake_case								= torch.load(A__			,		map_location=A__		)
    if ".ckpt" in ckpt_path:
        snake_case								= sd["state_dict"]
    model.load_state_dict(A__			,		strict=A__		)
    model.to(A__		)
    del sd
    return model



def 						lowercase_      (       A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case					,							snake_case					,							snake_case								= model.encode(A__		)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'		)
    snake_case								= model.decode(A__		)
    return xrec



def 						lowercase_      (       A__			,		A__=False		)							->							List[str]:
    """simple docstring"""

    snake_case					,							snake_case								= string.rsplit("."			,		1		)
    if reload:
        snake_case								= importlib.import_module(A__		)
        importlib.reload(A__		)
    return getattr(importlib.import_module(A__			,		package=A__		)			,		cls		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    if "target" not in config:
        raise KeyError("Expected key `target` to instantiate."		)
    return get_obj_from_str(config["target"]		)(**config.get("params"			,		{}		)		)



def 						lowercase_      (       A__			,		A__			,		A__=True			,		A__=True		)							->							Dict:
    """simple docstring"""

    snake_case								= instantiate_from_config(A__		)
    if sd is not None:
        model.load_state_dict(A__		)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}





def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							str:
    """simple docstring"""

    if ckpt:
        snake_case								= torch.load(A__			,		map_location="cpu"		)
        snake_case								= pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'		)
    else:
        snake_case								= {"state_dict": None}
        snake_case								= None
    snake_case								= load_model_from_config(config.model			,		pl_sd["state_dict"]			,		gpu=A__			,		eval_mode=A__		)["model"]
    return model, global_step




from collections import defaultdict







class 					lowerCamelCase		:





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   List[str]       )						->						Union[str, Any]:
         snake_case								= total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         snake_case								= [
             [-1 for i in range(total + 1       )] for j in range(2 ** len(_A       )       )
         ]

         snake_case								= defaultdict(_A       )  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         snake_case								= (1 << len(_A       )) - 1





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Optional[Any]    ,   _A				:   List[Any]       )						->						str:
         # if mask == self.finalmask all persons are distributed tasks, return 1
         if mask == self.final_mask:
             return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
             return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
             return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         snake_case								= self.count_ways_until(_A    ,   task_no + 1       )

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
                 # if p is already given a task
                 if mask & (1 << p):
                     continue

                 # assign this task to p and change the mask value. And recursively
                 # assign tasks with the new mask value.
                 total_ways_util += self.count_ways_until(mask | (1 << p)    ,   task_no + 1       )

        # save the value.
         snake_case								= total_ways_util

         return self.dp[mask][task_no]





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Dict       )						->						Optional[Any]:
         # Store the list of persons for each task
         for i in range(len(_A       )       ):
             for j in task_performed[i]:
                 self.task[j].append(_A       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0    ,   1       )


if __name__ == "__main__":
   _A   =			5  # total no of tasks (the value of N)

   # the list of tasks that can be done by M persons.
   _A   =			[[1, 3, 4], [1, 2, 5], [3, 4]]
   print(
       AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
           task_performed
       )
   )

