





"""simple docstring"""
from collections import defaultdict







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= total  # total no of tasks (N)

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

  lowerCAmelCase    				= 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
  lowerCAmelCase    				= (1 << len(_snake_case     )) - 1



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """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
  lowerCAmelCase    				= 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.
  lowerCAmelCase    				= total_ways_util

  return self.dp[mask][task_no]



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """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__":
       __UpperCamelCase     :						Tuple						       =  5  # total no of tasks (the value of N)

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












"""simple docstring"""
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser()
 parser.add_argument(
     '-m'  ,     '--pretrained_model_name_or_path'  ,     type=_UpperCAmelCase  ,     default=_UpperCAmelCase  ,     required=_UpperCAmelCase  ,     help='Path to pretrained model or model identifier from huggingface.co/models.'  ,     )
 parser.add_argument(
     '-c'  ,     '--caption'  ,     type=_UpperCAmelCase  ,     default='robotic cat with wings'  ,     help='Text used to generate images.'  ,     )
 parser.add_argument(
     '-n'  ,     '--images_num'  ,     type=_UpperCAmelCase  ,     default=4  ,     help='How much images to generate.'  ,     )
 parser.add_argument(
     '-s'  ,     '--seed'  ,     type=_UpperCAmelCase  ,     default=42  ,     help='Seed for random process.'  ,     )
 parser.add_argument(
     '-ci'  ,     '--cuda_id'  ,     type=_UpperCAmelCase  ,     default=0  ,     help='cuda_id.'  ,     )
 lowerCAmelCase    				= parser.parse_args()
 return args

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[int]			):
 if not len(_UpperCAmelCase			) == rows * cols:
  raise ValueError('The specified number of rows and columns are not correct.'			)

 lowerCAmelCase				,lowerCAmelCase    				= imgs[0].size
 lowerCAmelCase    				= Image.new('RGB'  ,     size=(cols * w, rows * h)			)
 lowerCAmelCase				,lowerCAmelCase    				= grid.size

 for i, img in enumerate(_UpperCAmelCase			):
  grid.paste(_UpperCAmelCase  ,     box=(i % cols * w, i // cols * h)			)
 return grid

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Union[str, Any]="robotic cat with wings"  ,     _UpperCAmelCase					:    Optional[int]=7.5  ,     _UpperCAmelCase					:    Dict=50  ,     _UpperCAmelCase					:    Tuple=1  ,     _UpperCAmelCase					:    int=42  ,     ):
 lowerCAmelCase    				= torch.Generator(pipeline.device			).manual_seed(_UpperCAmelCase			)
 lowerCAmelCase    				= pipeline(
     _UpperCAmelCase  ,     guidance_scale=_UpperCAmelCase  ,     num_inference_steps=_UpperCAmelCase  ,     generator=_UpperCAmelCase  ,     num_images_per_prompt=_UpperCAmelCase  ,     ).images
 lowerCAmelCase    				= int(math.sqrt(_UpperCAmelCase			)			)
 lowerCAmelCase    				= image_grid(_UpperCAmelCase  ,     rows=_rows  ,     cols=num_images_per_prompt // _rows			)
 return grid, images


__UpperCamelCase     :						Optional[Any]						       =  parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase     :						List[Any]						       =  CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase     :						str						       =  CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase     :						Optional[int]						       =  AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase     :						List[str]						       =  UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

__UpperCamelCase     :						Tuple						       =  StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase     :						Union[str, Any]						       =  lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
       __UpperCamelCase     :						Dict						       =  load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, '''unet''', unet)
else:
       __UpperCamelCase     :						Dict						       =  unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase     :						Optional[Any]						       =  pipeline.to(unet.device)
__UpperCamelCase       ,__UpperCamelCase     :						List[Any]						       =  generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase     :						int						       =  os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
       image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))





