




from __future__ import annotations

from queue import Queue


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		tuple[int, int]:





  """simple docstring"""





  a ^= b
  b ^= a
  a ^= b
  return a, b


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		list[list[int]]:





  """simple docstring"""





  _lowercase:			List[str]      =     1
  while (1 << j) < max_node:
    for i in range(1       ,  max_node + 1				):
      _lowercase:			List[Any]      =     parent[j - 1][parent[j - 1][i]]
    j += 1
  return parent


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  if level[u] < level[v]:
    _lowercase  ,       _lowercase:			List[Any]      =     swap(UpperCamelCase       ,  UpperCamelCase				)
  # making depth of u same as depth of v
  for i in range(18       ,  -1       ,  -1				):
    if level[u] - (1 << i) >= level[v]:
      _lowercase:			List[str]      =     parent[i][u]
    # at the same depth if u==v that mean lca is found
  if u == v:
    return u
  # moving both nodes upwards till lca in found
  for i in range(18       ,  -1       ,  -1				):
    if parent[i][u] not in [0, parent[i][v]]:
      _lowercase  ,       _lowercase:			Tuple      =     parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
  return parent[0][u]


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = 1       ,  )      ->		tuple[list[int], list[list[int]]]:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     0
  _lowercase:			Queue[int]      =     Queue(maxsize=UpperCamelCase				)
  q.put(UpperCamelCase				)
  while q.qsize() != 0:
    _lowercase:			List[str]      =     q.get()
    for v in graph[u]:
      if level[v] == -1:
        _lowercase:			Optional[Any]      =     level[u] + 1
        q.put(UpperCamelCase				)
        _lowercase:			Tuple      =     u
  return level, parent


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Dict      =     13
  # initializing with 0
  _lowercase:			Union[str, Any]      =     [[0 for _ in range(max_node + 10				)] for _ in range(20				)]
  # initializing with -1 which means every node is unvisited
  _lowercase:			Optional[int]      =     [-1 for _ in range(max_node + 10				)]
  _lowercase:			dict[int, list[int]]      =     {
      1: [2, 3, 4],
      2: [5],
      3: [6, 7],
      4: [8],
      5: [9, 10],
      6: [11],
      7: [],
      8: [12, 13],
      9: [],
      10: [],
      11: [],
      12: [],
      13: [],
  }
  _lowercase  ,       _lowercase:			str      =     breadth_first_search(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  1				)
  _lowercase:			Tuple      =     create_sparse(UpperCamelCase       ,  UpperCamelCase				)
  print('LCA of node 1 and 3 is: '       ,  lowest_common_ancestor(1       ,  3       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  print('LCA of node 5 and 6 is: '       ,  lowest_common_ancestor(5       ,  6       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  print('LCA of node 7 and 11 is: '       ,  lowest_common_ancestor(7       ,  11       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  print('LCA of node 6 and 7 is: '       ,  lowest_common_ancestor(6       ,  7       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  print('LCA of node 4 and 12 is: '       ,  lowest_common_ancestor(4       ,  12       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  print('LCA of node 8 and 8 is: '       ,  lowest_common_ancestor(8       ,  8       ,  UpperCamelCase       ,  UpperCamelCase				)				)


if __name__ == "__main__":
   main()






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






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






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






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			int      =     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    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






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

     _lowercase:			Optional[Any]      =     {
         'in_channels': 4,
         # 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,
     }

     _lowercase:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      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 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			List[str]      =     self.dummy_text_encoder
     _lowercase:			List[Any]      =     self.dummy_tokenizer
     _lowercase:			Optional[Any]      =     self.dummy_unet
     _lowercase:			List[str]      =     self.dummy_movq

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

     _lowercase:			Union[str, Any]      =     pipe(
         **self.get_dummy_inputs(UpperCamelCase_  )    ,       return_dict=UpperCamelCase_    ,       )[0]

     _lowercase:			int      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     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()}'

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






   def 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
