






from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							Optional[int]       ):
	snake_case__			:				List[Any]       =  k_size // 2
	snake_case__			:				Any       =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case__			:				Optional[Any]       =  1 / (2 * pi * sigma) * exp(-(square(__a       ) + square(__a       )) / (2 * square(__a       ))       )
	return g







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Optional[Any]					,  snake_case_ :							int       ):
	snake_case__			:				Union[str, Any]       =  image.shape[0], image.shape[1]
	# dst image height and width
	snake_case__			:				int       =  height - k_size + 1
	snake_case__			:				Optional[int]       =  width - k_size + 1

	# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
	snake_case__			:				Optional[Any]       =  zeros((dst_height * dst_width, k_size * k_size)       )
	snake_case__			:				Tuple       =  0
	for i, j in product(range(__a       )					,  range(__a       )       ):
		snake_case__			:				Optional[int]       =  ravel(image[i : i + k_size, j : j + k_size]       )
		snake_case__			:				str       =  window
		row += 1

	#  turn the kernel into shape(k*k, 1)
	snake_case__			:				List[Any]       =  gen_gaussian_kernel(__a					,  __a       )
	snake_case__			:				str       =  ravel(__a       )

	# reshape and get the dst image
	snake_case__			:				Optional[int]       =  dot(__a					,  __a       ).reshape(__a					,  __a       ).astype(__a       )

	return dst


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

	# get values with two different mask size
	__lowerCamelCase :      int										=				gaussian_filter(gray, 3, sigma=1)
	__lowerCamelCase :      int										=				gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
	imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
	waitKey()











import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		StableDiffusionInstructPixaPixPipeline
					a_     =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_     =		IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_     =		IMAGE_TO_IMAGE_IMAGE_PARAMS



					def 	_lowercase					( self					:  List[str]	):
						torch.manual_seed(0	)
						snake_case__			:				Any       =  UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)							,		layers_per_block=2							,		sample_size=3_2							,		in_channels=8							,		out_channels=4							,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,		cross_attention_dim=3_2							,		)
						snake_case__			:				int       =  PNDMScheduler(skip_prk_steps=__A	)
						torch.manual_seed(0	)
						snake_case__			:				Union[str, Any]       =  AutoencoderKL(
						    block_out_channels=[3_2, 6_4]							,		in_channels=3							,		out_channels=3							,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,		latent_channels=4							,		)
						torch.manual_seed(0	)
						snake_case__			:				int       =  CLIPTextConfig(
						    bos_token_id=0							,		eos_token_id=2							,		hidden_size=3_2							,		intermediate_size=3_7							,		layer_norm_eps=1e-0_5							,		num_attention_heads=4							,		num_hidden_layers=5							,		pad_token_id=1							,		vocab_size=1_0_0_0							,		)
						snake_case__			:				Union[str, Any]       =  CLIPTextModel(__A	)
						snake_case__			:				List[Any]       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

						snake_case__			:				str       =  {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components



					def 	_lowercase					( self					:  List[Any]							,		__A					:  int							,		__A					:  Any=0	):
						snake_case__			:				Optional[int]       =  floats_tensor((1, 3, 3_2, 3_2)							,		rng=random.Random(__A	)	).to(__A	)
						snake_case__			:				int       =  image.cpu().permute(0							,		2							,		3							,		1	)[0]
						snake_case__			:				Union[str, Any]       =  Image.fromarray(np.uinta(__A	)	).convert("RGB"	)
						if str(__A	).startswith("mps"	):
							snake_case__			:				List[Any]       =  torch.manual_seed(__A	)
						else:
							snake_case__			:				Optional[int]       =  torch.Generator(device=__A	).manual_seed(__A	)
						snake_case__			:				Optional[int]       =  {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "image_guidance_scale": 1,
						    "output_type": "numpy",
						}
						return inputs



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Tuple       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				int       =  self.get_dummy_components()
						snake_case__			:				List[Any]       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				List[Any]       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Tuple       =  self.get_dummy_inputs(__A	)
						snake_case__			:				List[str]       =  sd_pipe(**__A	).images
						snake_case__			:				str       =  image[0, -3:, -3:, -1]
						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				List[Any]       =  np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Dict       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				List[Any]       =  self.get_dummy_components()
						snake_case__			:				List[Any]       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				str       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)
						snake_case__			:				List[Any]       =  "french fries"
						snake_case__			:				str       =  sd_pipe(**__A							,		negative_prompt=__A	)
						snake_case__			:				Any       =  output.images
						snake_case__			:				Any       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				Union[str, Any]       =  np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Tuple       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				List[Any]       =  self.get_dummy_components()
						snake_case__			:				str       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				List[str]       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Any       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Tuple       =  [inputs["prompt"]] * 2

						snake_case__			:				Any       =  np.array(inputs["image"]	).astype(np.floataa	) / 2_5_5.0
						snake_case__			:				List[str]       =  torch.from_numpy(__A	).unsqueeze(0	).to(__A	)
						snake_case__			:				Union[str, Any]       =  image / 2 + 0.5
						snake_case__			:				str       =  image.permute(0							,		3							,		1							,		2	)
						snake_case__			:				int       =  image.repeat(2							,		1							,		1							,		1	)

						snake_case__			:				str       =  sd_pipe(**__A	).images
						snake_case__			:				Any       =  image[-1, -3:, -3:, -1]

						assert image.shape == (2, 3_2, 3_2, 3)
						snake_case__			:				int       =  np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Union[str, Any]       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				int       =  self.get_dummy_components()
						snake_case__			:				Dict       =  EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5							,		beta_end=0.0_1_2							,		beta_schedule="scaled_linear"	)
						snake_case__			:				Tuple       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				str       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Optional[Any]       =  sd_pipe(**__A	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1]

						snake_case__			:				Union[str, Any]       =  [round(__A							,		4	) for x in image_slice.flatten().tolist()]
						print(",".join([str(__A	) for x in slice]	)	)

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				str       =  np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



					def 	_lowercase					( self					:  List[str]	):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Tuple       =  self.get_dummy_components()
						snake_case__			:				Tuple       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				int       =  VaeImageProcessor(do_resize=__A							,		do_normalize=__A	)
						snake_case__			:				Any       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Dict       =  pipe(**self.get_dummy_inputs_by_type(__A							,		input_image_type="pt"	)	)[0]

						snake_case__			:				int       =  components["vae"]
						snake_case__			:				Union[str, Any]       =  self.get_dummy_inputs_by_type(__A							,		input_image_type="pt"	)

						for image_param in self.image_latents_params:
							if image_param in inputs.keys():
								snake_case__			:				Optional[int]       =  vae.encode(inputs[image_param]	).latent_dist.mode()

						snake_case__			:				str       =  pipe(**__A	)[0]

						snake_case__			:				Dict       =  np.abs(out - out_latents_inputs	).max()
						self.assertLess(__A							,		1e-4							,		"passing latents as image input generate different result from passing image"	)



@slow
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[int]	):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def 	_lowercase					( self					:  str							,		__A					:  Dict=0	):
						snake_case__			:				Optional[int]       =  torch.manual_seed(__A	)
						snake_case__			:				Tuple       =  load_image(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
						snake_case__			:				Optional[Any]       =  {
						    "prompt": "turn him into a cyborg",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 7.5,
						    "image_guidance_scale": 1.0,
						    "output_type": "numpy",
						}
						return inputs



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Union[str, Any]       =  self.get_inputs()
						snake_case__			:				Union[str, Any]       =  pipe(**__A	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Any       =  np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

						assert np.abs(expected_slice - image_slice	).max() < 1e-3



					def 	_lowercase					( self					:  str	):
						snake_case__			:				int       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						snake_case__			:				Dict       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				List[str]       =  self.get_inputs()
						snake_case__			:				Any       =  pipe(**__A	).images
						snake_case__			:				Union[str, Any]       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Optional[Any]       =  np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

						assert np.abs(expected_slice - image_slice	).max() < 1e-3



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				List[str]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						snake_case__			:				List[str]       =  DDIMScheduler.from_config(pipe.scheduler.config	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				int       =  self.get_inputs()
						snake_case__			:				Union[str, Any]       =  pipe(**__A	).images
						snake_case__			:				Tuple       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Union[str, Any]       =  np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

						assert np.abs(expected_slice - image_slice	).max() < 1e-3



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Optional[Any]       =  0

						def callback_fn(__A					:  int							,		__A					:  int							,		__A					:  torch.FloatTensor	) -> None:
							snake_case__			:				Union[str, Any]       =  True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
								snake_case__			:				Optional[Any]       =  latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__			:				int       =  latents[0, -3:, -3:, -1]
								snake_case__			:				Optional[int]       =  np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

								assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
							elif step == 2:
								snake_case__			:				int       =  latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__			:				Any       =  latents[0, -3:, -3:, -1]
								snake_case__			:				Dict       =  np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

								assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

						snake_case__			:				Any       =  False

						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A							,		torch_dtype=torch.floataa	)
						snake_case__			:				int       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Optional[Any]       =  self.get_inputs()
						pipe(**__A							,		callback=__A							,		callback_steps=1	)
						assert callback_fn.has_been_called
						assert number_of_steps == 3



					def 	_lowercase					( self					:  List[Any]	):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						snake_case__			:				Dict       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A							,		torch_dtype=torch.floataa	)
						snake_case__			:				Tuple       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing(1	)
						pipe.enable_sequential_cpu_offload()

						snake_case__			:				Dict       =  self.get_inputs()
						snake_case__			:				List[Any]       =  pipe(**__A	)

						snake_case__			:				List[str]       =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 1_0**9






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						snake_case__			:				Union[str, Any]       =  inputs["image"].resize((5_0_4, 5_0_4)	)

						snake_case__			:				Optional[Any]       =  "timbrooks/instruct-pix2pix"
						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    __A							,		safety_checker=__A							,		)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Union[str, Any]       =  pipe(**__A	)
						snake_case__			:				Tuple       =  output.images[0]

						snake_case__			:				List[Any]       =  image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert image.shape == (5_0_4, 5_0_4, 3)
						snake_case__			:				int       =  np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3



