






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Tuple       						=  KandinskyInpaintPipeline
		_snake_case    :    Union[str, Any]       						=  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
		_snake_case    :    Any       						=  [
		    '''prompt''',
		    '''negative_prompt''',
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		    '''mask_image''',
		]
		_snake_case    :    List[str]       						=  [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''latents''',
		    '''guidance_scale''',
		    '''negative_prompt''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		_snake_case    :    Optional[Any]       						=  False



		@property
		def 					__UpperCAmelCase			(							self   )	->							Any:
							return 3_2



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							return 3_2



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.time_input_dim



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return self.time_input_dim * 4



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return 1_0_0



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Tuple            =						XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
							return tokenizer



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							torch.manual_seed(0   )
							UpperCAmelCase_    :      int            =						MCLIPConfig(
							    numDims=self.cross_attention_dim				,		transformerDimensions=self.text_embedder_hidden_size				,		hidden_size=self.text_embedder_hidden_size				,		intermediate_size=3_7				,		num_attention_heads=4				,		num_hidden_layers=5				,		vocab_size=1_0_0_5				,		)

							UpperCAmelCase_    :      Union[str, Any]            =						MultilingualCLIP(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						text_encoder.eval()

							return text_encoder



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							torch.manual_seed(0   )

							UpperCAmelCase_    :      Optional[int]            =						{
							    'in_channels': 9,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'text_image',
							    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
							    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
							    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
							    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    'layers_per_block': 1,
							    'encoder_hid_dim': self.text_embedder_hidden_size,
							    'encoder_hid_dim_type': 'text_image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							UpperCAmelCase_    :      Tuple            =						UNetaDConditionModel(**_UpperCamelCase   )
							return model



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							return {
							    "block_out_channels": [3_2, 6_4],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 1_2,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							torch.manual_seed(0   )
							UpperCAmelCase_    :      Tuple            =						VQModel(**self.dummy_movq_kwargs   )
							return model



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      Optional[Any]            =						self.dummy_text_encoder
							UpperCAmelCase_    :      Optional[Any]            =						self.dummy_tokenizer
							UpperCAmelCase_    :      Dict            =						self.dummy_unet
							UpperCAmelCase_    :      Dict            =						self.dummy_movq

							UpperCAmelCase_    :      List[Any]            =						DDIMScheduler(
							    num_train_timesteps=1_0_0_0				,		beta_schedule='linear'				,		beta_start=0.0_00_85				,		beta_end=0.0_12				,		clip_sample=_UpperCamelCase				,		set_alpha_to_one=_UpperCamelCase				,		steps_offset=1				,		prediction_type='epsilon'				,		thresholding=_UpperCamelCase				,		)

							UpperCAmelCase_    :      Optional[int]            =						{
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=0   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[str]            =						floats_tensor((1, self.cross_attention_dim)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						floats_tensor((1, self.cross_attention_dim)				,		rng=random.Random(seed + 1   )   ).to(_UpperCamelCase   )
							# create init_image
							UpperCAmelCase_    :      List[str]            =						floats_tensor((1, 3, 6_4, 6_4)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						image.cpu().permute(0				,		2				,		3				,		1   )[0]
							UpperCAmelCase_    :      Dict            =						Image.fromarray(np.uinta(_UpperCamelCase   )   ).convert('RGB'   ).resize((2_5_6, 2_5_6)   )
							# create mask
							UpperCAmelCase_    :      Union[str, Any]            =						np.ones((6_4, 6_4)				,		dtype=np.floataa   )
							UpperCAmelCase_    :      Optional[int]            =						0

							if str(_UpperCamelCase   ).startswith('mps'   ):
												UpperCAmelCase_    :      List[Any]            =						torch.manual_seed(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Dict            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						{
							    'prompt': 'horse',
							    'image': init_image,
							    'mask_image': mask,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 6_4,
							    'width': 6_4,
							    'num_inference_steps': 2,
							    'guidance_scale': 4.0,
							    'output_type': 'np',
							}
							return inputs



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      int            =						'cpu'

							UpperCAmelCase_    :      int            =						self.get_dummy_components()

							UpperCAmelCase_    :      Tuple            =						self.pipeline_class(**_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						pipe.to(_UpperCamelCase   )

							pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						pipe(**self.get_dummy_inputs(_UpperCamelCase   )   )
							UpperCAmelCase_    :      Tuple            =						output.images

							UpperCAmelCase_    :      int            =						pipe(
							    **self.get_dummy_inputs(_UpperCamelCase   )				,		return_dict=_UpperCamelCase				,		)[0]

							UpperCAmelCase_    :      Tuple            =						image[0, -3:, -3:, -1]
							UpperCAmelCase_    :      Any            =						image_from_tuple[0, -3:, -3:, -1]

							print(f"image.shape {image.shape}"   )

							assert image.shape == (1, 6_4, 6_4, 3)

							UpperCAmelCase_    :      int            =						np.array(
							    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

							assert (
							    np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
							), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
							), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





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







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

							UpperCAmelCase_    :      int            =						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
							UpperCAmelCase_    :      Dict            =						np.ones((7_6_8, 7_6_8)				,		dtype=np.floataa   )
							UpperCAmelCase_    :      str            =						0

							UpperCAmelCase_    :      int            =						'a hat'

							UpperCAmelCase_    :      int            =						KandinskyPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-prior'				,		torch_dtype=torch.floataa   )
							pipe_prior.to(_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						KandinskyInpaintPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-inpaint'				,		torch_dtype=torch.floataa   )
							UpperCAmelCase_    :      Optional[int]            =						pipeline.to(_UpperCamelCase   )
							pipeline.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						torch.Generator(device='cpu'   ).manual_seed(0   )
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						pipe_prior(
							    _UpperCamelCase				,		generator=_UpperCamelCase				,		num_inference_steps=5				,		negative_prompt=''				,		).to_tuple()

							UpperCAmelCase_    :      List[Any]            =						pipeline(
							    _UpperCamelCase				,		image=_UpperCamelCase				,		mask_image=_UpperCamelCase				,		image_embeds=_UpperCamelCase				,		negative_image_embeds=_UpperCamelCase				,		generator=_UpperCamelCase				,		num_inference_steps=1_0_0				,		height=7_6_8				,		width=7_6_8				,		output_type='np'				,		)

							UpperCAmelCase_    :      Tuple            =						output.images[0]

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(_UpperCamelCase				,		_UpperCamelCase   )









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


