






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       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """simple docstring"""

    __A       =						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=4_2      ,					help="""Seed for random process."""      ,					)
    parser.add_argument(
        """-ci"""      ,					"""--cuda_id"""      ,					type=__lowercase      ,					default=0      ,					help="""cuda_id."""      ,					)
    __A       =						parser.parse_args()
    return args



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Union[str, Any]      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[str]     )    ->   Any:





    """simple docstring"""

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

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

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Any="robotic cat with wings"      ,					__lowercase :      Dict=7.5      ,					__lowercase :      Optional[int]=5_0      ,					__lowercase :      List[Any]=1      ,					__lowercase :      List[str]=4_2      ,					)    ->   Dict:





    """simple docstring"""

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


__a   :							Any						= parse_args()
# Load models and create wrapper for stable diffusion
__a   :							Any						= 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   :							Optional[Any]						= AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
__a   :							Optional[int]						= UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

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

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         warnings.warn(
             """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use DeiTImageProcessor instead."""			,						UpperCamelCase_			,						)
         super().__init__(*UpperCamelCase_			,						**UpperCamelCase_					)




