


"""simple docstring"""







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       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =KandinskyVaaControlnetPipeline
       a  :				Optional[Any]   =["image_embeds", "negative_image_embeds", "hint"]
       a  :				Tuple   =["image_embeds", "negative_image_embeds", "hint"]
       a  :				Dict   =[
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       a  :				int   =False

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim * 4

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 100

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)

         lowerCAmelCase     :     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,
         }

         lowerCAmelCase     :     Optional[Any]								=							UNetaDConditionModel(**snake_case__						)
         return model

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     Optional[Any]								=							VQModel(**self.dummy_movq_kwargs						)
         return model

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.dummy_unet
         lowerCAmelCase     :     Optional[Any]								=							self.dummy_movq

         lowerCAmelCase     :     str								=							DDIMScheduler(
             num_train_timesteps=1_000		,     beta_schedule="linear"		,     beta_start=0.00085		,     beta_end=0.012		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     steps_offset=1		,     prediction_type="epsilon"		,     thresholding=snake_case__		,     )

         lowerCAmelCase     :     Optional[Any]								=							{
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     Dict								=							floats_tensor((1, self.text_embedder_hidden_size)		,     rng=random.Random(seed + 1						)						).to(
             snake_case__						)

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

         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Union[str, Any]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     str								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase     :     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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"cpu"

         lowerCAmelCase     :     Optional[int]								=							self.get_dummy_components()

         lowerCAmelCase     :     int								=							self.pipeline_class(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							pipe.to(snake_case__						)

         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Dict								=							pipe(**self.get_dummy_inputs(snake_case__						)						)
         lowerCAmelCase     :     List[Any]								=							output.images

         lowerCAmelCase     :     Tuple								=							pipe(
             **self.get_dummy_inputs(snake_case__						)		,     return_dict=snake_case__		,     )[0]

         lowerCAmelCase     :     Optional[int]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     str								=							image_from_tuple[0, -3:, -3:, -1]

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

         lowerCAmelCase     :     Any								=							np.array(
             [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]						)

         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       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"						)

         lowerCAmelCase     :     Optional[int]								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png"						)
         lowerCAmelCase     :     Any								=							torch.from_numpy(np.array(snake_case__						)						).float() / 255.0
         lowerCAmelCase     :     Optional[Any]								=							hint.permute(2		,     0		,     1						).unsqueeze(0						)

         lowerCAmelCase     :     Optional[Any]								=							KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"		,     torch_dtype=torch.floataa						)
         pipe_prior.to(snake_case__						)

         lowerCAmelCase     :     Dict								=							KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"		,     torch_dtype=torch.floataa						)
         lowerCAmelCase     :     Union[str, Any]								=							pipeline.to(snake_case__						)
         pipeline.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Dict								=							"A robot, 4k photo"

         lowerCAmelCase     :     Union[str, Any]								=							torch.Generator(device="cuda"						).manual_seed(0						)
         lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							pipe_prior(
             snake_case__		,     generator=snake_case__		,     num_inference_steps=5		,     negative_prompt=""		,     ).to_tuple()

         lowerCAmelCase     :     Dict								=							torch.Generator(device="cuda"						).manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							pipeline(
             image_embeds=snake_case__		,     negative_image_embeds=snake_case__		,     hint=snake_case__		,     generator=snake_case__		,     num_inference_steps=100		,     output_type="np"		,     )

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

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

         assert_mean_pixel_difference(snake_case__		,     snake_case__						)









"""simple docstring"""







class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__ = ""		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     dict[str, RadixNode]								=							{}

         # A node will be a leaf if the tree contains its word
         lowerCAmelCase     :     str								=							is_leaf

         lowerCAmelCase     :     str								=							prefix

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							0
         for q, w in zip(self.prefix		,     snake_case__						):
           if q != w:
             break

           x += 1

         return self.prefix[:x], self.prefix[x:], word[x:]

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         for word in words:
           self.insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.prefix == word:
           lowerCAmelCase     :     Union[str, Any]								=							True

         # Case 2: The node has no edges that have a prefix to the word
         # Solution: We create an edge from the current node to a new one
         # containing the word
         elif word[0] not in self.nodes:
           lowerCAmelCase     :     Optional[Any]								=							RadixNode(prefix=snake_case__		,     is_leaf=snake_case__						)

         else:
           lowerCAmelCase     :     Tuple								=							self.nodes[word[0]]
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							incoming_node.match(
               snake_case__						)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
             self.nodes[matching_string[0]].insert(snake_case__						)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
             lowerCAmelCase     :     Optional[Any]								=							remaining_prefix

             lowerCAmelCase     :     int								=							self.nodes[matching_string[0]]
             lowerCAmelCase     :     List[Any]								=							RadixNode(snake_case__		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							aux_node

             if remaining_word == "":
               lowerCAmelCase     :     Optional[int]								=							True
             else:
               self.nodes[matching_string[0]].insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							incoming_node.match(
               snake_case__						)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
             return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
             return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
             return incoming_node.find(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							incoming_node.match(
               snake_case__						)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
             return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
             return incoming_node.delete(snake_case__						)
           else:
             # If it is not a leaf, we don't have to delete
             if not incoming_node.is_leaf:
               return False
             else:
               # We delete the nodes if no edges go from it
               if len(incoming_node.nodes						) == 0:
                 del self.nodes[word[0]]
                 # We merge the current node with its only child
                 if len(self.nodes						) == 1 and not self.is_leaf:
                   lowerCAmelCase     :     List[str]								=							list(self.nodes.values()						)[0]
                   lowerCAmelCase     :     List[str]								=							merging_node.is_leaf
                   self.prefix += merging_node.prefix
                   lowerCAmelCase     :     Optional[int]								=							merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
               elif len(incoming_node.nodes						) > 1:
                 lowerCAmelCase     :     Optional[int]								=							False
               # If there is 1 edge, we merge it with its child
               else:
                 lowerCAmelCase     :     Optional[Any]								=							list(incoming_node.nodes.values()						)[0]
                 lowerCAmelCase     :     int								=							merging_node.is_leaf
                 incoming_node.prefix += merging_node.prefix
                 lowerCAmelCase     :     Tuple								=							merging_node.nodes

               return True







       def        lowercase__							(						self		,     snake_case__ = 0						):






         """simple docstring"""





         if self.prefix != "":
           print("-" * height		,     self.prefix		,     "  (leaf)" if self.is_leaf else ""						)

         for value in self.nodes.values():
           value.print_tree(height + 1						)

def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							"banana bananas bandana band apple all beast".split()
  lowerCAmelCase     :     List[str]								=							RadixNode()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  assert all(root.find(SCREAMING_SNAKE_CASE				) for word in words				)
  assert not root.find("bandanas"				)
  assert not root.find("apps"				)
  root.delete("all"				)
  assert not root.find("all"				)
  root.delete("banana"				)
  assert not root.find("banana"				)
  assert root.find("bananas"				)

  return True
def 					a__			(				):


  '''simple docstring'''







  assert test_trie()
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							RadixNode()
  lowerCAmelCase     :     Optional[Any]								=							"banana bananas bandanas bandana band apple all beast".split()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  print("Words:"				,      SCREAMING_SNAKE_CASE				)
  print("Tree:"				)
  root.print_tree()


if __name__ == "__main__":
     main()





