





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





     """simple docstring"""

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







def    a__							(							lowercase   :	Tuple,   lowercase   :	int,   lowercase   :	int )						->						List[str]:





     """simple docstring"""

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

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

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







def    a__							(							lowercase   :	List[str],   lowercase   :	int="robotic cat with wings",   lowercase   :	Optional[int]=7.5,   lowercase   :	List[str]=50,   lowercase   :	List[str]=1,   lowercase   :	str=42,   )						->						Tuple:





     """simple docstring"""

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


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

lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase__		:     Optional[Any]			=							lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
   lowercase__		:     int			=							load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, 'unet', unet)
else:
   lowercase__		:     str			=							unet.to(torch.device('cuda', args.cuda_id))
lowercase__		:     str			=							pipeline.to(unet.device)
lowercase__			,  lowercase__		:     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()))))
lowercase__		:     Dict			=							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 ..utils import DummyObject, requires_backends
class 							__lowerCAmelCase	(       metaclass=__magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['transformers', 'torch', 'note_seq']





       def __init__(				self  :						Tuple			,					*lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            requires_backends(self			,					['''transformers''', '''torch''', '''note_seq''']	)





       @classmethod
       def snake_case__		(				cls  :						Dict			,					*lowerCAmelCase__  :						Optional[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Any:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)







       @classmethod
       def snake_case__		(				cls  :						str			,					*lowerCAmelCase__  :						Any			,					**lowerCAmelCase__  :						List[Any]	)       ->	Tuple:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)



