



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  KandinskyVaaControlnetPipeline
       UpperCAmelCase__				:					Any        =  ["image_embeds", "negative_image_embeds", "hint"]
       UpperCAmelCase__				:					Any        =  ["image_embeds", "negative_image_embeds", "hint"]
       UpperCAmelCase__				:					str        =  [
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase__				:					Any        =  False
       @property
       def 					snake_case_       (			self       )     ->		str:
          return 32
       @property
       def 					snake_case_       (			self       )     ->		Dict:
          return 32
       @property
       def 					snake_case_       (			self       )     ->		Dict:
          return self.time_input_dim
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self.time_input_dim * 4
       @property
       def 					snake_case_       (			self       )     ->		Any:
          return 100
       @property
       def 					snake_case_       (			self       )     ->		str:
          torch.manual_seed(0       )

          UpperCamelCase    :  List[Any]							=	{
              'in_channels': 8,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'image_hint',
              '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    :  int							=	UNetaDConditionModel(**SCREAMING_SNAKE_CASE_       )
          return model
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return {
              "block_out_channels": [32, 32, 64, 64],
              "down_block_types": [
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          torch.manual_seed(0       )
          UpperCamelCase    :  Dict							=	VQModel(**self.dummy_movq_kwargs       )
          return model
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Optional[int]							=	self.dummy_unet
          UpperCamelCase    :  int							=	self.dummy_movq

          UpperCamelCase    :  Optional[int]							=	DDIMScheduler(
              num_train_timesteps=1000,			beta_schedule='linear',			beta_start=0.0_00_85,			beta_end=0.0_12,			clip_sample=SCREAMING_SNAKE_CASE_,			set_alpha_to_one=SCREAMING_SNAKE_CASE_,			steps_offset=1,			prediction_type='epsilon',			thresholding=SCREAMING_SNAKE_CASE_,			)

          UpperCamelCase    :  int							=	{
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }
          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		str:
          UpperCamelCase    :  Dict							=	floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(seed + 1       )       ).to(
              SCREAMING_SNAKE_CASE_       )

          # create hint
          UpperCamelCase    :  Any							=	floats_tensor((1, 3, 64, 64),			rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  Union[str, Any]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  List[str]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	{
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'hint': hint,
              'generator': generator,
              'height': 64,
              'width': 64,
              'guidance_scale': 4.0,
              'num_inference_steps': 2,
              'output_type': 'np',
          }
          return inputs






       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Dict							=	'cpu'

          UpperCamelCase    :  Any							=	self.get_dummy_components()

          UpperCamelCase    :  List[Any]							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	pipe.to(SCREAMING_SNAKE_CASE_       )

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )       )
          UpperCamelCase    :  Tuple							=	output.images

          UpperCamelCase    :  str							=	pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ),			return_dict=SCREAMING_SNAKE_CASE_,			)[0]

          UpperCamelCase    :  Tuple							=	image[0, -3:, -3:, -1]
          UpperCamelCase    :  List[str]							=	image_from_tuple[0, -3:, -3:, -1]

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

          UpperCamelCase    :  List[Any]							=	np.array(
              [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]       )

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






       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Any							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'       )

          UpperCamelCase    :  str							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinskyv22/hint_image_cat.png'       )
          UpperCamelCase    :  Dict							=	torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_       )       ).float() / 2_55.0
          UpperCamelCase    :  Union[str, Any]							=	hint.permute(2,			0,			1       ).unsqueeze(0       )

          UpperCamelCase    :  int							=	KandinskyVaaPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-2-prior',			torch_dtype=torch.floataa       )
          pipe_prior.to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	KandinskyVaaControlnetPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-2-controlnet-depth',			torch_dtype=torch.floataa       )
          UpperCamelCase    :  Optional[Any]							=	pipeline.to(SCREAMING_SNAKE_CASE_       )
          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	'A robot, 4k photo'

          UpperCamelCase    :  Tuple							=	torch.Generator(device='cuda'       ).manual_seed(0       )
          UpperCamelCase	,     UpperCamelCase    :  List[str]							=	pipe_prior(
              SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=5,			negative_prompt='',			).to_tuple()

          UpperCamelCase    :  List[str]							=	torch.Generator(device='cuda'       ).manual_seed(0       )
          UpperCamelCase    :  Any							=	pipeline(
              image_embeds=SCREAMING_SNAKE_CASE_,			negative_image_embeds=SCREAMING_SNAKE_CASE_,			hint=SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=100,			output_type='np',			)

          UpperCamelCase    :  str							=	output.images[0]

          assert image.shape == (512, 512, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )








import functools
from typing import Any
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    list[str]	)	->     bool:

   # Validation
   if not isinstance(snake_case__       ,     snake_case__	) or len(snake_case__	) == 0:
      raise ValueError('the string should be not empty string'	)

   if not isinstance(snake_case__       ,     snake_case__	) or not all(
       isinstance(snake_case__       ,     snake_case__	) and len(snake_case__	) > 0 for item in words	):
      raise ValueError('the words should be a list of non-empty strings'	)

   # Build trie
   UpperCamelCase    :  dict[str, Any]							=	{}
   UpperCamelCase    :  List[str]							=	'WORD_KEEPER'

   for word in words:
      UpperCamelCase    :  List[str]							=	trie
      for c in word:
         if c not in trie_node:
            UpperCamelCase    :  int							=	{}

         UpperCamelCase    :  str							=	trie_node[c]

      UpperCamelCase    :  Tuple							=	True

   UpperCamelCase    :  List[Any]							=	len(snake_case__	)

   # Dynamic programming method
   @functools.cache
   def is_breakable(snake_case__       :    int	) -> bool:
      if index == len_string:
         return True

      UpperCamelCase    :  Dict							=	trie
      for i in range(snake_case__       ,     snake_case__	):
         UpperCamelCase    :  List[Any]							=	trie_node.get(string[i]       ,     snake_case__	)

         if trie_node is None:
            return False

         if trie_node.get(snake_case__       ,     snake_case__	) and is_breakable(i + 1	):
            return True

      return False

   return is_breakable(0	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



