






"""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 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= argparse.ArgumentParser()
  parser.add_argument(
      '''-m'''		,       '''--pretrained_model_name_or_path'''		,       type=__UpperCamelCase		,       default=__UpperCamelCase		,       required=__UpperCamelCase		,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''		,       )
  parser.add_argument(
      '''-c'''		,       '''--caption'''		,       type=__UpperCamelCase		,       default='''robotic cat with wings'''		,       help='''Text used to generate images.'''		,       )
  parser.add_argument(
      '''-n'''		,       '''--images_num'''		,       type=__UpperCamelCase		,       default=4		,       help='''How much images to generate.'''		,       )
  parser.add_argument(
      '''-s'''		,       '''--seed'''		,       type=__UpperCamelCase		,       default=4_2		,       help='''Seed for random process.'''		,       )
  parser.add_argument(
      '''-ci'''		,       '''--cuda_id'''		,       type=__UpperCamelCase		,       default=0		,       help='''cuda_id.'''		,       )
  __A									= parser.parse_args()
  return args



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







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



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase="robotic cat with wings"		,       __UpperCamelCase=7.5		,       __UpperCamelCase=5_0		,       __UpperCamelCase=1		,       __UpperCamelCase=4_2		,       ):




  """simple docstring"""







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


lowercase_				     =     parse_args()
# Load models and create wrapper for stable diffusion
lowercase_				     =     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowercase_				     =     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowercase_				     =     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowercase_				     =     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowercase_				     =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase_				     =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
       lowercase_				     =     load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, 'unet', unet)
else:
       lowercase_				     =     unet.to(torch.device('cuda', args.cuda_id))
lowercase_				     =     pipeline.to(unet.device)
lowercase_     ,		lowercase_				     =     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()))))
lowercase_				     =     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 numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __A									= imread(__UpperCamelCase		,       0 )

  # Test for get_neighbors_pixel function() return not None
  __A									= 0
  __A									= 0
  __A									= image[x_coordinate][y_coordinate]

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __A									= np.zeros((image.shape[0], image.shape[1]) )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,       image.shape[0] ):
    for j in range(0		,       image.shape[1] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




