





from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_UpperCAmelCase       =				logging.get_logger(__name__)  # pylint: disable=invalid-name






class   snake_case_							(						lowerCamelCase__		):






  def __init__(				self					:	List[Any]  ,    _snake_case					:	CLIPSegForImageSegmentation  ,    _snake_case					:	CLIPSegProcessor  ,    _snake_case					:	AutoencoderKL  ,    _snake_case					:	CLIPTextModel  ,    _snake_case					:	CLIPTokenizer  ,    _snake_case					:	UNetaDConditionModel  ,    _snake_case					:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,    _snake_case					:	StableDiffusionSafetyChecker  ,    _snake_case					:	CLIPImageProcessor  ,    )->Tuple:






   '''simple docstring'''

   super().__init__()

   if hasattr(scheduler.config  ,    """steps_offset"""     ) and scheduler.config.steps_offset != 1:
    __lowerCAmelCase	:			List[str]         =  (
        F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
        F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
        "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
        " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
        " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
        " file"
    )
    deprecate("""steps_offset!=1"""  ,    """1.0.0"""  ,    _snake_case  ,    standard_warn=_snake_case     )
    __lowerCAmelCase	:			List[str]         =  dict(scheduler.config     )
    __lowerCAmelCase	:			List[str]         =  1
    __lowerCAmelCase	:			Any         =  FrozenDict(_snake_case     )

   if hasattr(scheduler.config  ,    """skip_prk_steps"""     ) and scheduler.config.skip_prk_steps is False:
    __lowerCAmelCase	:			Optional[int]         =  (
        F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
        " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
        " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
        " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
        " Hub, it would be very nice if you could open a Pull request for the"
        " `scheduler/scheduler_config.json` file"
    )
    deprecate("""skip_prk_steps not set"""  ,    """1.0.0"""  ,    _snake_case  ,    standard_warn=_snake_case     )
    __lowerCAmelCase	:			Any         =  dict(scheduler.config     )
    __lowerCAmelCase	:			List[Any]         =  True
    __lowerCAmelCase	:			int         =  FrozenDict(_snake_case     )

   if safety_checker is None:
    logger.warning(
        F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
        """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
        """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
        """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
        """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
        """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""     )

   self.register_modules(
       segmentation_model=_snake_case  ,    segmentation_processor=_snake_case  ,    vae=_snake_case  ,    text_encoder=_snake_case  ,    tokenizer=_snake_case  ,    unet=_snake_case  ,    scheduler=_snake_case  ,    safety_checker=_snake_case  ,    feature_extractor=_snake_case  ,    )






  def  UpperCAmelCase__		(				self					:	Tuple  ,    _snake_case					:	Optional[Union[str, int]] = "auto"     )->Union[str, Any]:






   '''simple docstring'''

   if slice_size == "auto":
    # half the attention head size is usually a good trade-off between
    # speed and memory
    __lowerCAmelCase	:			List[str]         =  self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(_snake_case     )






  def  UpperCAmelCase__		(				self					:	Optional[int]     )->int:






   '''simple docstring'''

   self.enable_attention_slicing(_snake_case     )






  def  UpperCAmelCase__		(				self					:	Any     )->str:






   '''simple docstring'''

   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

   __lowerCAmelCase	:			Any         =  torch.device("""cuda"""     )

   for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
    if cpu_offloaded_model is not None:
     cpu_offload(_snake_case  ,    _snake_case     )






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def  UpperCAmelCase__		(				self					:	List[Any]     )->str:






   '''simple docstring'''

   if self.device != torch.device("""meta"""     ) or not hasattr(self.unet  ,    """_hf_hook"""     ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(_snake_case  ,    """_hf_hook"""     )
        and hasattr(module._hf_hook  ,    """execution_device"""     )
        and module._hf_hook.execution_device is not None
    ):
     return torch.device(module._hf_hook.execution_device     )
   return self.device






  @torch.no_grad()
  def __call__(				self					:	Any  ,    _snake_case					:	Union[str, List[str]]  ,    _snake_case					:	Union[torch.FloatTensor, PIL.Image.Image]  ,    _snake_case					:	str  ,    _snake_case					:	int = 512  ,    _snake_case					:	int = 512  ,    _snake_case					:	int = 50  ,    _snake_case					:	float = 7.5  ,    _snake_case					:	Optional[Union[str, List[str]]] = None  ,    _snake_case					:	Optional[int] = 1  ,    _snake_case					:	float = 0.0  ,    _snake_case					:	Optional[torch.Generator] = None  ,    _snake_case					:	Optional[torch.FloatTensor] = None  ,    _snake_case					:	Optional[str] = "pil"  ,    _snake_case					:	bool = True  ,    _snake_case					:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,    _snake_case					:	int = 1  ,    **_snake_case					:	Union[str, Any]  ,    )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase	:			str         =  self.segmentation_processor(
       text=[text]  ,    images=[image]  ,    padding="""max_length"""  ,    return_tensors="""pt"""     ).to(self.device     )
   __lowerCAmelCase	:			str         =  self.segmentation_model(**_snake_case     )
   __lowerCAmelCase	:			Tuple         =  torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
   __lowerCAmelCase	:			Dict         =  self.numpy_to_pil(_snake_case     )[0].resize(image.size     )

   # Run inpainting pipeline with the generated mask
   __lowerCAmelCase	:			Union[str, Any]         =  StableDiffusionInpaintPipeline(
       vae=self.vae  ,    text_encoder=self.text_encoder  ,    tokenizer=self.tokenizer  ,    unet=self.unet  ,    scheduler=self.scheduler  ,    safety_checker=self.safety_checker  ,    feature_extractor=self.feature_extractor  ,    )
   return inpainting_pipeline(
       prompt=_snake_case  ,    image=_snake_case  ,    mask_image=_snake_case  ,    height=_snake_case  ,    width=_snake_case  ,    num_inference_steps=_snake_case  ,    guidance_scale=_snake_case  ,    negative_prompt=_snake_case  ,    num_images_per_prompt=_snake_case  ,    eta=_snake_case  ,    generator=_snake_case  ,    latents=_snake_case  ,    output_type=_snake_case  ,    return_dict=_snake_case  ,    callback=_snake_case  ,    callback_steps=_snake_case  ,    )






from collections import defaultdict


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      total  # total no of tasks (N)

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

        A_       :					Optional[int]        =      defaultdict(snake_case	)  # 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
        A_       :					Dict        =      (1 << len(snake_case	)) - 1



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




        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
        A_       :					Tuple        =      self.count_ways_until(snake_case      ,   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.
        A_       :					Any        =      total_ways_util

        return self.dp[mask][task_no]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]	):





        '''simple docstring'''




        for i in range(len(snake_case	)	):
           for j in task_performed[i]:
              self.task[j].append(snake_case	)

        # 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__":
 _lowerCAmelCase				:   Dict						       =						5  # total no of tasks (the value of N)

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