

"""simple docstring"""




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class   __UpperCamelCase      (   lowercase_       ,       lowercase_       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       IFImgaImgPipeline
	SCREAMING_SNAKE_CASE__      			=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
	SCREAMING_SNAKE_CASE__      			=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	SCREAMING_SNAKE_CASE__      			=       PipelineTesterMixin.required_optional_params - {"""latents"""}




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		return self._get_dummy_components()




	def  __A   (						self		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Optional[Any]=0							):


		'''simple docstring'''







		if str(lowerCAmelCase							).startswith("mps"							):
			UpperCAmelCase_    			=     torch.manual_seed(lowerCAmelCase							)
		else:
			UpperCAmelCase_    			=     torch.Generator(device=lowerCAmelCase							).manual_seed(lowerCAmelCase							)

		UpperCAmelCase_    			=     floats_tensor((1, 3, 32, 32)						,		rng=random.Random(lowerCAmelCase							)							).to(lowerCAmelCase							)

		UpperCAmelCase_    			=     {
		    "prompt": "A painting of a squirrel eating a burger",
		    "image": image,
		    "generator": generator,
		    "num_inference_steps": 2,
		    "output_type": "numpy",
		}

		return inputs




	def  __A   (						self		:  int							):


		'''simple docstring'''







		self._test_save_load_optional_components()




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()						,		reason="XFormers attention is only available with CUDA and `xformers` installed"						,		)
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)




	@unittest.skipIf(torch_device != "cuda"						,		reason="float16 requires CUDA"							)
	def  __A   (						self		:  Any							):


		'''simple docstring'''







		super().test_save_load_floataa(expected_max_diff=1e-1							)




	@unittest.skipIf(torch_device != "cuda"						,		reason="float16 requires CUDA"							)
	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		super().test_floataa_inference(expected_max_diff=1e-1							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)




	def  __A   (						self		:  str							):


		'''simple docstring'''







		self._test_save_load_local()




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2						,		)


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def  __lowerCAmelCase (  A    ,       A=3      ):
	UpperCAmelCase_    			=     int(mask / 2      )
	# copy image size
	UpperCAmelCase_    			=     zeros_like(A      )
	for i in range(A    ,       gray_img.shape[0] - bd      ):
		for j in range(A    ,       gray_img.shape[1] - bd      ):
			# get mask according with mask
			UpperCAmelCase_    			=     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			UpperCAmelCase_    			=     sort(A      )[inta(divide((multiply(A    ,       A      ))    ,       2      ) + 1      )]
			UpperCAmelCase_    			=     median
	return median_img


if __name__ == "__main__":
				# read original image
				_a:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				_a:      Tuple								=     median_filter(gray, 3)
				_a:      Dict								=     median_filter(gray, 5)

				# show result images
				imshow("""median filter with 3x3 mask""", medianaxa)
				imshow("""median filter with 5x5 mask""", medianaxa)
				waitKey(0)