






"""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	(		):
	A_		:						str	    =   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."""   ,   )
	A_		:						List[str]	    =   parser.parse_args()
	return args






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	if not len(lowercase__					) == rows * cols:
		raise ValueError("""The specified number of rows and columns are not correct."""					)

	A_    ,     A_		:						int	    =   imgs[0].size
	A_		:						List[Any]	    =   Image.new("""RGB"""   ,   size=(cols * w, rows * h)					)
	A_    ,     A_		:						int	    =   grid.size

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__="robotic cat with wings"   ,   snake_case__=7.5   ,   snake_case__=50   ,   snake_case__=1   ,   snake_case__=42   ,   ):
	A_		:						Dict	    =   torch.Generator(pipeline.device					).manual_seed(lowercase__					)
	A_		:						List[Any]	    =   pipeline(
	    lowercase__   ,   guidance_scale=lowercase__   ,   num_inference_steps=lowercase__   ,   generator=lowercase__   ,   num_images_per_prompt=lowercase__   ,   ).images
	A_		:						Any	    =   int(math.sqrt(lowercase__					)					)
	A_		:						List[str]	    =   image_grid(lowercase__   ,   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"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				List[Any]    =				"""ViTImageProcessor"""
	_A       :				Optional[Any]    =				("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Union[str, Any]	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						Optional[int]	    =   image_processor if image_processor is not None else feature_extractor
		if image_processor is None:
			raise ValueError("""You need to specify an `image_processor`."""							)
		if tokenizer is None:
			raise ValueError("""You need to specify a `tokenizer`."""							)

		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if text is None and visual_prompt is None and images is None:
			raise ValueError("""You have to specify either text, visual prompt or images."""							)

		if text is not None and visual_prompt is not None:
			raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""							)

		if text is not None:
			A_		:						Tuple	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None:
			A_		:						Union[str, Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						Any	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None and images is not None:
			A_		:						List[str]	    =   {
			    """pixel_values""": image_features.pixel_values,
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		elif text is not None and images is not None:
			A_		:						str	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		elif visual_prompt is not None:
			A_		:						Any	    =   {
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor_class





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor

