


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[int]							     =     KandinskyVaaInpaintPipeline
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     ["image_embeds", "negative_image_embeds", "image", "mask_image"]
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     [
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     SCREAMING_SNAKE_CASE :				Tuple							     =     [
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     False

     @property
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Dict:
          return 32

     @property
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[Any]:
          return 32

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

     @property
     def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
          return self.time_input_dim * 4

     @property
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Dict:
          return 100

     @property
     def 				lowerCamelCase__				(       self  :	int    )		->							List[Any]:
          torch.manual_seed(0    )

          __UpperCAmelCase			:				Dict													=    {
              '''in_channels''': 9,
              # Out channels is double in channels because predicts mean and variance
              '''out_channels''': 8,
              '''addition_embed_type''': '''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''': '''image_proj''',
              '''cross_attention_dim''': self.cross_attention_dim,
              '''attention_head_dim''': 4,
              '''resnet_time_scale_shift''': '''scale_shift''',
              '''class_embed_type''': None,
          }

          __UpperCAmelCase			:				Any													=    UNetaDConditionModel(**snake_case    )
          return model

     @property
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[Any]:
          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  :	Union[str, Any]    )		->							List[str]:
          torch.manual_seed(0    )
          __UpperCAmelCase			:				int													=    VQModel(**self.dummy_movq_kwargs    )
          return model

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				Dict													=    self.dummy_unet
          __UpperCAmelCase			:				str													=    self.dummy_movq

          __UpperCAmelCase			:				Union[str, Any]													=    DDIMScheduler(
              num_train_timesteps=1000							,				beta_schedule='''linear'''							,				beta_start=0.00_085							,				beta_end=0.012							,				clip_sample=snake_case							,				set_alpha_to_one=snake_case							,				steps_offset=1							,				prediction_type='''epsilon'''							,				thresholding=snake_case							,				)

          __UpperCAmelCase			:				int													=    {
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	List[str]							,				snake_case  :	List[str]=0    )		->							int:
          __UpperCAmelCase			:				List[str]													=    floats_tensor((1, self.text_embedder_hidden_size)							,				rng=random.Random(snake_case    )    ).to(snake_case    )
          __UpperCAmelCase			:				int													=    floats_tensor((1, self.text_embedder_hidden_size)							,				rng=random.Random(seed + 1    )    ).to(
              snake_case    )
          # create init_image
          __UpperCAmelCase			:				List[str]													=    floats_tensor((1, 3, 64, 64)							,				rng=random.Random(snake_case    )    ).to(snake_case    )
          __UpperCAmelCase			:				List[Any]													=    image.cpu().permute(0							,				2							,				3							,				1    )[0]
          __UpperCAmelCase			:				List[str]													=    Image.fromarray(np.uinta(snake_case    )    ).convert('''RGB'''    ).resize((256, 256)    )
          # create mask
          __UpperCAmelCase			:				Any													=    np.ones((64, 64)							,				dtype=np.floataa    )
          __UpperCAmelCase			:				Union[str, Any]													=    0

          if str(snake_case    ).startswith('''mps'''    ):
               __UpperCAmelCase			:				Union[str, Any]													=    torch.manual_seed(snake_case    )
          else:
               __UpperCAmelCase			:				Union[str, Any]													=    torch.Generator(device=snake_case    ).manual_seed(snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    {
              '''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  :	int    )		->							Dict:
          __UpperCAmelCase			:				Optional[Any]													=    '''cpu'''

          __UpperCAmelCase			:				str													=    self.get_dummy_components()

          __UpperCAmelCase			:				Union[str, Any]													=    self.pipeline_class(**snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    pipe.to(snake_case    )

          pipe.set_progress_bar_config(disable=snake_case    )

          __UpperCAmelCase			:				int													=    pipe(**self.get_dummy_inputs(snake_case    )    )
          __UpperCAmelCase			:				List[Any]													=    output.images

          __UpperCAmelCase			:				Tuple													=    pipe(
              **self.get_dummy_inputs(snake_case    )							,				return_dict=snake_case							,				)[0]

          __UpperCAmelCase			:				Optional[Any]													=    image[0, -3:, -3:, -1]
          __UpperCAmelCase			:				Optional[int]													=    image_from_tuple[0, -3:, -3:, -1]

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

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

          __UpperCAmelCase			:				int													=    np.array(
              [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]    )

          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  :	int    )		->							Union[str, Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3    )



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


     """simple docstring"""






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






     def 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          __UpperCAmelCase			:				Optional[int]													=    load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''    )

          __UpperCAmelCase			:				Tuple													=    load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
          __UpperCAmelCase			:				int													=    np.ones((768, 768)							,				dtype=np.floataa    )
          __UpperCAmelCase			:				List[str]													=    0

          __UpperCAmelCase			:				str													=    '''a hat'''

          __UpperCAmelCase			:				Union[str, Any]													=    KandinskyVaaPriorPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-prior'''							,				torch_dtype=torch.floataa    )
          pipe_prior.to(snake_case    )

          __UpperCAmelCase			:				Dict													=    KandinskyVaaInpaintPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''							,				torch_dtype=torch.floataa    )
          __UpperCAmelCase			:				str													=    pipeline.to(snake_case    )
          pipeline.set_progress_bar_config(disable=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    torch.Generator(device='''cpu'''    ).manual_seed(0    )
          __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    pipe_prior(
              snake_case							,				generator=snake_case							,				num_inference_steps=5							,				negative_prompt=''''''							,				).to_tuple()

          __UpperCAmelCase			:				Dict													=    pipeline(
              image=snake_case							,				mask_image=snake_case							,				image_embeds=snake_case							,				negative_image_embeds=snake_case							,				generator=snake_case							,				num_inference_steps=100							,				height=768							,				width=768							,				output_type='''np'''							,				)

          __UpperCAmelCase			:				Any													=    output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(snake_case							,				snake_case    )



'''simple docstring'''



def 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()