

def      _lowerCAmelCase(	__A   ,       __A			):

  if not (isinstance(__A   ,       __A			) and isinstance(__A   ,       __A			)):
    raise ValueError("longest_common_substring() takes two strings for inputs"			)

  UpperCAmelCase					      =  len(__A			)
  UpperCAmelCase					      =  len(__A			)

  UpperCAmelCase					      =  [[0] * (texta_length + 1) for _ in range(texta_length + 1			)]
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0

  for i in range(1   ,       texta_length + 1			):
    for j in range(1   ,       texta_length + 1			):
      if texta[i - 1] == texta[j - 1]:
        UpperCAmelCase					      =  1 + dp[i - 1][j - 1]
        if dp[i][j] > ans_length:
          UpperCAmelCase					      =  i
          UpperCAmelCase					      =  dp[i][j]

  return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






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 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						KandinskyInpaintPipeline
 UpperCAmelCase							    =						["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 UpperCAmelCase							    =						[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 UpperCAmelCase							    =						[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase							    =						False


 @property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Tuple:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[Any]:
   return self.time_input_dim


 @property
 def _UpperCamelCase	(						self		: Tuple    )    ->      Tuple:
   return self.time_input_dim * 4


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


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[int]:
   UpperCAmelCase					      =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"    )
   return tokenizer


 @property
 def _UpperCamelCase	(						self		: int    )    ->      Dict:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  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					      =  MultilingualCLIP(lowerCAmelCase__    )
   UpperCAmelCase					      =  text_encoder.eval()

   return text_encoder


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   torch.manual_seed(0    )

   UpperCAmelCase					      =  {
       "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					      =  UNetaDConditionModel(**lowerCAmelCase__    )
   return model


 @property
 def _UpperCamelCase	(						self		: str    )    ->      Optional[Any]:
   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		: Dict    )    ->      List[Any]:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  VQModel(**self.dummy_movq_kwargs    )
   return model


 def _UpperCamelCase	(						self		: Tuple    )    ->      Any:
   UpperCAmelCase					      =  self.dummy_text_encoder
   UpperCAmelCase					      =  self.dummy_tokenizer
   UpperCAmelCase					      =  self.dummy_unet
   UpperCAmelCase					      =  self.dummy_movq

   UpperCAmelCase					      =  DDIMScheduler(
       num_train_timesteps=1_0_0_0			,						beta_schedule="linear"			,						beta_start=0.00_085			,						beta_end=0.012			,						clip_sample=lowerCAmelCase__			,						set_alpha_to_one=lowerCAmelCase__			,						steps_offset=1			,						prediction_type="epsilon"			,						thresholding=lowerCAmelCase__			,						)

   UpperCAmelCase					      =  {
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components


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

   if str(lowerCAmelCase__    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(lowerCAmelCase__    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=lowerCAmelCase__    ).manual_seed(lowerCAmelCase__    )
   UpperCAmelCase					      =  {
       "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		: Dict    )    ->      List[str]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**lowerCAmelCase__    )
   UpperCAmelCase					      =  pipe.to(lowerCAmelCase__    )

   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  pipe(**self.get_dummy_inputs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  pipe(
       **self.get_dummy_inputs(lowerCAmelCase__    )			,						return_dict=lowerCAmelCase__			,						)[0]

   UpperCAmelCase					      =  image[0, -3:, -3:, -1]
   UpperCAmelCase					      =  image_from_tuple[0, -3:, -3:, -1]

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

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

   UpperCAmelCase					      =  np.array(
       [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]    )

   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		: str    )    ->      Tuple:
   super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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


 def _UpperCamelCase	(						self		: str    )    ->      str:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _UpperCamelCase	(						self		: Tuple    )    ->      int:
   UpperCAmelCase					      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"    )

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

   UpperCAmelCase					      =  "a hat"

   UpperCAmelCase					      =  KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,						torch_dtype=torch.floataa    )
   pipe_prior.to(lowerCAmelCase__    )

   UpperCAmelCase					      =  KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,						torch_dtype=torch.floataa    )
   UpperCAmelCase					      =  pipeline.to(lowerCAmelCase__    )
   pipeline.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  torch.Generator(device="cpu"    ).manual_seed(0    )
   UpperCAmelCase      ,	UpperCAmelCase					      =  pipe_prior(
       lowerCAmelCase__			,						generator=lowerCAmelCase__			,						num_inference_steps=5			,						negative_prompt=""			,						).to_tuple()

   UpperCAmelCase					      =  pipeline(
       lowerCAmelCase__			,						image=lowerCAmelCase__			,						mask_image=lowerCAmelCase__			,						image_embeds=lowerCAmelCase__			,						negative_image_embeds=lowerCAmelCase__			,						generator=lowerCAmelCase__			,						num_inference_steps=1_0_0			,						height=7_6_8			,						width=7_6_8			,						output_type="np"			,						)

   UpperCAmelCase					      =  output.images[0]

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

   assert_mean_pixel_difference(lowerCAmelCase__			,						lowerCAmelCase__    )



