

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

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







def _a (       )    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   Optional[int]   	=      cn.convert_to_negative(_SCREAMING_SNAKE_CASE		)
 # assert negative_img array for at least one True
 assert negative_img.any()







def _a (       )    ->    List[str]:

 """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(_SCREAMING_SNAKE_CASE    ,					1_10		)		).startswith(
      '<PIL.Image.Image image mode=RGB size=100x100 at'		)







def _a (       )    ->    Optional[int]:

 """simple docstring"""






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







def _a (       )    ->    int:

 """simple docstring"""






 __lowerCAmelCase:   Tuple   	=      imread('digital_image_processing/image_data/lena_small.jpg'    ,					0		)
 # assert ambiguous array for all == True
 assert canny_img.all()
 __lowerCAmelCase:   Optional[Any]   	=      canny.canny(_SCREAMING_SNAKE_CASE		)
 # assert canny array for at least one True
 assert canny_array.any()







def _a (       )    ->    List[Any]:

 """simple docstring"""






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







def _a (       )    ->    Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:   int   	=      array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]]		)
 __lowerCAmelCase:   Union[str, Any]   	=      conv.img_convolve(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		).astype(_SCREAMING_SNAKE_CASE		)
 assert res.any()







def _a (       )    ->    List[str]:

 """simple docstring"""






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







def _a (       )    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   int   	=      sob.sobel_filter(_SCREAMING_SNAKE_CASE		)
 assert grad.any() and theta.any()







def _a (       )    ->    Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:   int   	=      sp.make_sepia(_SCREAMING_SNAKE_CASE    ,					20		)
 assert sepia.all()







def _a (       SCREAMING_SNAKE_CASE		:			Optional[Any] = "digital_image_processing/image_data/lena_small.jpg"		)    ->    List[Any]:

 """simple docstring"""






 __lowerCAmelCase:   Tuple   	=      bs.Burkes(imread(_SCREAMING_SNAKE_CASE    ,					1		)    ,					1_20		)
 burkes.process()
 assert burkes.output_img.any()







def _a (       SCREAMING_SNAKE_CASE		:			Optional[int] = "digital_image_processing/image_data/lena_small.jpg"    ,					)    ->    Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:   Any   	=      rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE    ,					1		)    ,					4_00    ,					2_00		)
 nn.process()
 assert nn.output.any()







def _a (       )    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   Union[str, Any]   	=      'digital_image_processing/image_data/lena.jpg'

 # Reading the image and converting it to grayscale.
 __lowerCAmelCase:   Any   	=      imread(_SCREAMING_SNAKE_CASE    ,					0		)

 # Test for get_neighbors_pixel function() return not None
 __lowerCAmelCase:   List[str]   	=      0
 __lowerCAmelCase:   Optional[int]   	=      0
 __lowerCAmelCase:   str   	=      image[x_coordinate][y_coordinate]

 __lowerCAmelCase:   Any   	=      lbp.get_neighbors_pixel(
     _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

 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
 __lowerCAmelCase:   Dict   	=      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]		):
   __lowerCAmelCase:   List[str]   	=      lbp.local_binary_value(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

 assert lbp_image.any()






"""simple docstring"""
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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	MCLIPConfig(
            numDims=self.cross_attention_dim    ,			transformerDimensions=self.text_embedder_hidden_size    ,			hidden_size=self.text_embedder_hidden_size    ,			intermediate_size=37    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			vocab_size=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            '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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return {
            "block_out_channels": [32, 64],
            "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": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

        assert image.shape == (1, 64, 64, 3)

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

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

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowercase    ,			lowercase)


