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






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   str:
   if not len(a      ) == rows * cols:
      raise ValueError('''The specified number of rows and columns are not correct.'''      )

   _A			,  _A:  Union[str, Any]					=       imgs[0].size
   _A:  Optional[int]					=       Image.new('''RGB'''		,		size=(cols * w, rows * h)      )
   _A			,  _A:  Tuple					=       grid.size

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






def   lowerCamelCase__							(					a		,		a="robotic cat with wings"		,		a=7.5		,		a=50		,		a=1		,		a=42		,		)      ->   str:
   _A:  Optional[Any]					=       torch.Generator(pipeline.device      ).manual_seed(a      )
   _A:  List[Any]					=       pipeline(
       a		,		guidance_scale=a		,		num_inference_steps=a		,		generator=a		,		num_images_per_prompt=a		,		).images
   _A:  str					=       int(math.sqrt(a      )      )
   _A:  int					=       image_grid(a		,		rows=_rows		,		cols=num_images_per_prompt // _rows      )
   return grid, images


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

UpperCAmelCase__		:     List[str]     			=      StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
UpperCAmelCase__		:     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__		:     Any     			=      pipeline.to(unet.device)
UpperCAmelCase__			,UpperCAmelCase__		:     List[str]     			=      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)))





import random

from .binary_exp_mod import bin_exp_mod






def   lowerCamelCase__							(					a		,		a=10_00      )      ->   Optional[int]:
   if n < 2:
      return False

   if n % 2 == 0:
      return n == 2

   # this means n is odd
   _A:  List[Any]					=       n - 1
   _A:  Dict					=       0
   while d % 2 == 0:
      d /= 2
      exp += 1

   # n - 1=d*(2**exp)
   _A:  List[str]					=       0
   while count < prec:
      _A:  Optional[int]					=       random.randint(2		,		n - 1      )
      _A:  Union[str, Any]					=       bin_exp_mod(a		,		a		,		a      )
      if b != 1:
         _A:  Optional[Any]					=       True
         for _ in range(a      ):
            if b == n - 1:
               _A:  int					=       False
               break
            _A:  Optional[Any]					=       b * b
            b %= n
         if flag:
            return False
         count += 1
   return True


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      abs(int(input('Enter bound : ').strip()))
   print('Here\'s the list of primes:')
   print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))



