'''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      UpperCamelCase_						(					)					->     Any:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       Any						,		snake_case_       :       str      )					->     str:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       Union[str, Any]="robotic cat with wings"						,		snake_case_       :       Any=7.5						,		snake_case_       :       Any=50						,		snake_case_       :       List[Any]=1						,		snake_case_       :       int=42						,		)					->     Optional[int]:






 '''simple docstring'''

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


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

_A			:      int					      =				StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_A			:      Tuple					      =				lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
 _A			:      Any					      =				load(args.pretrained_model_name_or_path, model=unet)
 unet.eval()
 setattr(pipeline, '''unet''', unet)
else:
 _A			:      List[Any]					      =				unet.to(torch.device('''cuda''', args.cuda_id))
_A			:      Optional[Any]					      =				pipeline.to(unet.device)
_A      ,     _A			:      Union[str, Any]					      =				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			:      Optional[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)))







'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 @staticmethod
 @abstractmethod
 def a					(      SCREAMING_SNAKE_CASE__   :	ArgumentParser							)  ->    Tuple:
  raise NotImplementedError()





 @abstractmethod
 def a					(      self   :	int							)  ->    Union[str, Any]:
  raise NotImplementedError()






