





'''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 						__magic_name__       (							)						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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."""							,	)
   __SCREAMING_SNAKE_CASE      							=       parser.parse_args()
   return args

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   if not len(__UpperCAmelCase      ) == rows * cols:
      raise ValueError("""The specified number of rows and columns are not correct."""      )

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

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase="robotic cat with wings"							,	__UpperCAmelCase=7.5							,	__UpperCAmelCase=50							,	__UpperCAmelCase=1							,	__UpperCAmelCase=42							,	)						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       torch.Generator(pipeline.device      ).manual_seed(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       pipeline(
       __UpperCAmelCase							,	guidance_scale=__UpperCAmelCase							,	num_inference_steps=__UpperCAmelCase							,	generator=__UpperCAmelCase							,	num_images_per_prompt=__UpperCAmelCase							,	).images
   __SCREAMING_SNAKE_CASE      							=       int(math.sqrt(__UpperCAmelCase      )      )
   __SCREAMING_SNAKE_CASE      							=       image_grid(__UpperCAmelCase							,	rows=_rows							,	cols=num_images_per_prompt // _rows      )
   return grid, images


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

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












'''simple docstring'''

from __future__ import annotations

import bisect

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_left(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_right(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      ) - 1

   while left <= right:
      __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2
      __SCREAMING_SNAKE_CASE      							=       sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         __SCREAMING_SNAKE_CASE      							=       midpoint - 1
      else:
         __SCREAMING_SNAKE_CASE      							=       midpoint + 1
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       bisect.bisect_left(__UpperCAmelCase							,	__UpperCAmelCase      )
   if index != len(__UpperCAmelCase      ) and sorted_collection[index] == item:
      return index
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   if right < left:
      return None

   __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint - 1      )
   else:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint + 1							,	__UpperCAmelCase      )


if __name__ == "__main__":
    a        =						input("Enter numbers separated by comma:\n").strip()
    a        =						sorted(int(item) for item in user_input.split(","))
    a        =						int(input("Enter a single number to be found in the list:\n"))
    a        =						binary_search(collection, target)
    if result is None:
        print(F'''{target} was not found in {collection}.''')
    else:
        print(F'''{target} was found at position {result} in {collection}.''')





