






import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 							_A (    UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       int   =   KandinskyVaaPriorEmbaEmbPipeline
    lowerCamelCase  :       Tuple   =   ['prompt', 'image']
    lowerCamelCase  :       Union[str, Any]   =   ['prompt', 'image']
    lowerCamelCase  :       List[Any]   =   [
        'num_images_per_prompt',
        'strength',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase  :       Optional[Any]   =   False



    @property
    def _a       (							self			:    Dict			)			->       Optional[Any]:
           return 32



    @property
    def _a       (							self			:    Optional[Any]			)			->       Dict:
           return 32



    @property
    def _a       (							self			:    List[str]			)			->       Tuple:
           return self.time_input_dim



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



    @property
    def _a       (							self			:    Optional[int]			)			->       Any:
           return 100



    @property
    def _a       (							self			:    int			)			->       Union[str, Any]:
           __UpperCAmelCase    							=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
           return tokenizer



    @property
    def _a       (							self			:    Optional[int]			)			->       Tuple:
           torch.manual_seed(0			)
           __UpperCAmelCase    							=CLIPTextConfig(
               bos_token_id=0   ,    eos_token_id=2   ,    hidden_size=self.text_embedder_hidden_size   ,    projection_dim=self.text_embedder_hidden_size   ,    intermediate_size=37   ,    layer_norm_eps=1e-05   ,    num_attention_heads=4   ,    num_hidden_layers=5   ,    pad_token_id=1   ,    vocab_size=1000   ,    )
           return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE			)



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

           __UpperCAmelCase    							={
               """num_attention_heads""": 2,
               """attention_head_dim""": 12,
               """embedding_dim""": self.text_embedder_hidden_size,
               """num_layers""": 1,
           }

           __UpperCAmelCase    							=PriorTransformer(**__SCREAMING_SNAKE_CASE			)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           __UpperCAmelCase    							=nn.Parameter(torch.ones(model.clip_std.shape			)			)
           return model



    @property
    def _a       (							self			:    Tuple			)			->       int:
           torch.manual_seed(0			)
           __UpperCAmelCase    							=CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size   ,    image_size=224   ,    projection_dim=self.text_embedder_hidden_size   ,    intermediate_size=37   ,    num_attention_heads=4   ,    num_channels=3   ,    num_hidden_layers=5   ,    patch_size=14   ,    )

           __UpperCAmelCase    							=CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE			)
           return model



    @property
    def _a       (							self			:    str			)			->       str:
           __UpperCAmelCase    							=CLIPImageProcessor(
               crop_size=224   ,    do_center_crop=__SCREAMING_SNAKE_CASE   ,    do_normalize=__SCREAMING_SNAKE_CASE   ,    do_resize=__SCREAMING_SNAKE_CASE   ,    image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,    image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,    resample=3   ,    size=224   ,    )

           return image_processor



    def _a       (							self			:    str			)			->       str:
           __UpperCAmelCase    							=self.dummy_prior
           __UpperCAmelCase    							=self.dummy_image_encoder
           __UpperCAmelCase    							=self.dummy_text_encoder
           __UpperCAmelCase    							=self.dummy_tokenizer
           __UpperCAmelCase    							=self.dummy_image_processor

           __UpperCAmelCase    							=UnCLIPScheduler(
               variance_type="""fixed_small_log"""   ,    prediction_type="""sample"""   ,    num_train_timesteps=1000   ,    clip_sample=__SCREAMING_SNAKE_CASE   ,    clip_sample_range=10.0   ,    )

           __UpperCAmelCase    							={
               """prior""": prior,
               """image_encoder""": image_encoder,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """scheduler""": scheduler,
               """image_processor""": image_processor,
           }

           return components



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=0			)			->       Union[str, Any]:
           if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
                  __UpperCAmelCase    							=torch.manual_seed(__SCREAMING_SNAKE_CASE			)
           else:
                  __UpperCAmelCase    							=torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=image.cpu().permute(0   ,    2   ,    3   ,    1			)[0]
           __UpperCAmelCase    							=Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((256, 256)			)

           __UpperCAmelCase    							={
               """prompt""": """horse""",
               """image""": init_image,
               """strength""": 0.5,
               """generator""": generator,
               """guidance_scale""": 4.0,
               """num_inference_steps""": 2,
               """output_type""": """np""",
           }
           return inputs



    def _a       (							self			:    Union[str, Any]			)			->       Tuple:
           __UpperCAmelCase    							="""cpu"""

           __UpperCAmelCase    							=self.get_dummy_components()

           __UpperCAmelCase    							=self.pipeline_class(**__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=pipe.to(__SCREAMING_SNAKE_CASE			)

           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)			)
           __UpperCAmelCase    							=output.image_embeds

           __UpperCAmelCase    							=pipe(
               **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)   ,    return_dict=__SCREAMING_SNAKE_CASE   ,    )[0]

           __UpperCAmelCase    							=image[0, -10:]
           __UpperCAmelCase    							=image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           __UpperCAmelCase    							=np.array(
               [
                   0.1_071_284,
                   1.3_330_271,
                   0.61_260_223,
                   -0.6_691_065,
                   -0.3_846_852,
                   -1.0_303_661,
                   0.22_716_111,
                   0.03_348_901,
                   0.30_040_675,
                   -0.24_805_029,
               ]			)

           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



    @skip_mps
    def _a       (							self			:    Optional[int]			)			->       Optional[Any]:
           __UpperCAmelCase    							=torch_device == """cpu"""
           __UpperCAmelCase    							=True
           __UpperCAmelCase    							=False

           self._test_inference_batch_single_identical(
               test_max_difference=__SCREAMING_SNAKE_CASE   ,    relax_max_difference=__SCREAMING_SNAKE_CASE   ,    test_mean_pixel_difference=__SCREAMING_SNAKE_CASE   ,    )



    @skip_mps
    def _a       (							self			:    str			)			->       List[str]:
           __UpperCAmelCase    							=torch_device == """cpu"""
           __UpperCAmelCase    							=False

           self._test_attention_slicing_forward_pass(
               test_max_difference=__SCREAMING_SNAKE_CASE   ,    test_mean_pixel_difference=__SCREAMING_SNAKE_CASE   ,    )












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







def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:







       """simple docstring"""






       __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  							=  imread("../image_data/lena.jpg")
   # turn image in gray scale value
   __A  							=  cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   __A  							=  median_filter(gray, 3)
   __A  							=  median_filter(gray, 5)

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




