





"""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        lowercase__     (       )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							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   :						Union[str, Any]    =							parser.parse_args()
      return args




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

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

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

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




def        lowercase__     (       lowercase_		,lowercase_="robotic cat with wings"		,lowercase_=7.5		,lowercase_=50		,lowercase_=1		,lowercase_=42		,)       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							torch.Generator(pipeline.device      ).manual_seed(lowercase_      )
      _UpperCamelCase   :						List[str]    =							pipeline(
          lowercase_		,guidance_scale=lowercase_		,num_inference_steps=lowercase_		,generator=lowercase_		,num_images_per_prompt=lowercase_		,).images
      _UpperCamelCase   :						List[Any]    =							int(math.sqrt(lowercase_      )      )
      _UpperCamelCase   :						Tuple    =							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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)
