

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 	_A  (					):






 """simple docstring"""



 __lowercase													=				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.'''  ,       )
 __lowercase													=				parser.parse_args()
 return args


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

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


def 	_A  (					A__  ,       A__="robotic cat with wings"  ,       A__=7.5  ,       A__=50  ,       A__=1  ,       A__=42  ,       ):






 """simple docstring"""



 __lowercase													=				torch.Generator(pipeline.device   ).manual_seed(A__   )
 __lowercase													=				pipeline(
     A__  ,       guidance_scale=A__  ,       num_inference_steps=A__  ,       generator=A__  ,       num_images_per_prompt=A__  ,       ).images
 __lowercase													=				int(math.sqrt(A__   )   )
 __lowercase													=				image_grid(A__  ,       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)))




'''simple docstring'''






from collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

