from __future__ import annotations

from collections import deque
class     a_			:






 '''simple docstring'''






 def __init__(      self :  List[Any]      ,   lowercase__ :  list[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        self.adlist.append(
            {'value': '', 'next_states': [], 'fail_state': 0, 'output': []})

        for keyword in keywords:
               self.add_keyword(_lowerCamelCase)
        self.set_fail_transitions()




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  int      ,   lowercase__ :  str):







        '''simple docstring'''



        for state in self.adlist[current_state]["next_states"]:
               if char == self.adlist[state]["value"]:
                      return state
        return None




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = 0
        for character in keyword:
               lowerCAmelCase__					     = self.find_next_state(_lowerCamelCase      ,   _lowerCamelCase)
               if next_state is None:
                      self.adlist.append(
                          {
                              'value': character,
                              'next_states': [],
                              'fail_state': 0,
                              'output': [],
                          })
                      self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
                      lowerCAmelCase__					     = len(self.adlist) - 1
               else:
                      lowerCAmelCase__					     = next_state
        self.adlist[current_state]["output"].append(_lowerCamelCase)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = deque()
        for node in self.adlist[0]["next_states"]:
               q.append(_lowerCamelCase)
               lowerCAmelCase__					     = 0
        while q:
               lowerCAmelCase__					     = q.popleft()
               for child in self.adlist[r]["next_states"]:
                      q.append(_lowerCamelCase)
                      lowerCAmelCase__					     = self.adlist[r]['fail_state']
                      while (
                          self.find_next_state(_lowerCamelCase      ,   self.adlist[child]['value']) is None
                          and state != 0
                      ):
                             lowerCAmelCase__					     = self.adlist[state]['fail_state']
                      lowerCAmelCase__					     = self.find_next_state(
                          _lowerCamelCase      ,   self.adlist[child]['value'])
                      if self.adlist[child]["fail_state"] is None:
                             lowerCAmelCase__					     = 0
                      lowerCAmelCase__					     = (
                          self.adlist[child]['output']
                          + self.adlist[self.adlist[child]['fail_state']]['output']
                      )




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = {}  # returns a dict with keywords and list of its occurrences
        lowerCAmelCase__					     = 0
        for i in range(len(_lowerCamelCase)):
               while (
                   self.find_next_state(_lowerCamelCase      ,   string[i]) is None
                   and current_state != 0
               ):
                      lowerCAmelCase__					     = self.adlist[current_state]['fail_state']
               lowerCAmelCase__					     = self.find_next_state(_lowerCamelCase      ,   string[i])
               if next_state is None:
                      lowerCAmelCase__					     = 0
               else:
                      lowerCAmelCase__					     = next_state
                      for key in self.adlist[current_state]["output"]:
                             if key not in result:
                                    lowerCAmelCase__					     = []
                             result[key].append(i - len(_lowerCamelCase) + 1)
        return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == 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(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			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()))))
lowerCAmelCase__     						=			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)))
