






"""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    ()				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	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.'''					,       )
    SCREAMING_SNAKE_CASE_   :						int							=	parser.parse_args()
    return args



def       _A    (__a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[Any]							=	imgs[0].size
    SCREAMING_SNAKE_CASE_   :						str							=	Image.new('''RGB'''					,       size=(cols * w, rows * h)							)
    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						str							=	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					,       )				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	torch.Generator(pipeline.device							).manual_seed(__a							)
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	pipeline(
        __a					,       guidance_scale=__a					,       num_inference_steps=__a					,       generator=__a					,       num_images_per_prompt=__a					,       ).images
    SCREAMING_SNAKE_CASE_   :						Tuple							=	int(math.sqrt(__a							)							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image_grid(__a					,       rows=_rows					,       cols=num_images_per_prompt // _rows							)
    return grid, images


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

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

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

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




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


