

'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
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   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					KandinskyVaaPipeline
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""image_embeds""", """negative_image_embeds"""]
 __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					:List[str]      	=					False



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 32



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.time_input_dim



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.time_input_dim * 4



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 100



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      torch.manual_seed(0      )

      __magic_name__             =    {
          '''in_channels''': 4,
          # 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,
      }

      __magic_name__             =    UNetaDConditionModel(**a__      )
      return model



 @property
 def 			snake_case__  (   self		:   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 			snake_case__  (   self		:   str      ):
      torch.manual_seed(0      )
      __magic_name__             =    VQModel(**self.dummy_movq_kwargs      )
      return model



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__             =    self.dummy_unet
      __magic_name__             =    self.dummy_movq

      __magic_name__             =    DDIMScheduler(
          num_train_timesteps=1000			,				beta_schedule='''linear'''			,				beta_start=0.00_085			,				beta_end=0.012			,				clip_sample=a__			,				set_alpha_to_one=a__			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=a__			,				)

      __magic_name__             =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Union[str, Any]			,				a__		:   Any=0      ):
      __magic_name__             =    floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__             =    floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1      )      ).to(
          a__      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__             =    torch.manual_seed(a__      )
      else:
           __magic_name__             =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__             =    {
          '''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 			snake_case__  (   self		:   List[str]      ):
      __magic_name__             =    '''cpu'''

      __magic_name__             =    self.get_dummy_components()

      __magic_name__             =    self.pipeline_class(**a__      )
      __magic_name__             =    pipe.to(a__      )

      pipe.set_progress_bar_config(disable=a__      )

      __magic_name__             =    pipe(**self.get_dummy_inputs(a__      )      )
      __magic_name__             =    output.images

      __magic_name__             =    pipe(
          **self.get_dummy_inputs(a__      )			,				return_dict=a__			,				)[0]

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

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

      __magic_name__             =    np.array(
          [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]      )

      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     ):



 def 			snake_case__  (   self		:   Any      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__             =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''      )

      __magic_name__             =    KandinskyVaaPriorPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-prior'''			,				torch_dtype=torch.floataa      )
      pipe_prior.to(a__      )

      __magic_name__             =    KandinskyVaaPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-decoder'''			,				torch_dtype=torch.floataa      )
      __magic_name__             =    pipeline.to(a__      )
      pipeline.set_progress_bar_config(disable=a__      )

      __magic_name__             =    '''red cat, 4k photo'''

      __magic_name__             =    torch.Generator(device='''cuda'''      ).manual_seed(0      )
      __magic_name__       ,  __magic_name__             =    pipe_prior(
          a__			,				generator=a__			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

      __magic_name__             =    torch.Generator(device='''cuda'''      ).manual_seed(0      )
      __magic_name__             =    pipeline(
          image_embeds=a__			,				negative_image_embeds=a__			,				generator=a__			,				num_inference_steps=100			,				output_type='''np'''			,				)

      __magic_name__             =    output.images[0]

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

      assert_mean_pixel_difference(a__			,				a__      )









'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    0
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   int      ):
      return self.head == self.tail



 def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
      self.data.append(a__      )
      __magic_name__						       =    self.tail + 1



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.data[self.head]
      __magic_name__						       =    self.head + 1
      return ret



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.tail - self.head



 def 			snake_case__  (   self		:   List[Any]      ):
      print(self.data      )
      print('''**************'''      )
      print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   Any      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    1



 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.data



 def 			snake_case__  (   self		:   List[Any]      ):
      return self.left



 def 			snake_case__  (   self		:   Tuple      ):
      return self.right



 def 			snake_case__  (   self		:   Any      ):
      return self.height



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
      __magic_name__						       =    data



 def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
      __magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     if node is None:
          return 0
     return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     if a > b:
          return a
     return b




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''left rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()			)
     ret.set_right(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''right rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()			)
     ret.set_left(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(a			)			)
     return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(a			)			)
     return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     if node is None:
          return MyNode(a			)
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()		,						a			)			)
          if (
              get_height(node.get_left()			) - get_height(node.get_right()			) == 2
          ):  # an unbalance detected
               __magic_name__						       =    node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    __magic_name__						       =    right_rotation(a			)
               else:
                    __magic_name__						       =    lr_rotation(a			)
     else:
          node.set_right(insert_node(node.get_right()		,						a			)			)
          if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
               __magic_name__						       =    node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    __magic_name__						       =    rl_rotation(a			)
               else:
                    __magic_name__						       =    left_rotation(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     return node




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_right()
          if right_child is None:
               break
          __magic_name__						       =    right_child
     return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_left()
          if left_child is None:
               break
          __magic_name__						       =    left_child
     return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     __magic_name__						       =    root.get_left()
     __magic_name__						       =    root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               __magic_name__						       =    get_left_most(a			)
               root.set_data(a			)
               root.set_right(del_node(a		,						a			)			)
          elif left_child is not None:
               __magic_name__						       =    left_child
          elif right_child is not None:
               __magic_name__						       =    right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print('''No such data'''			)
               return root
          else:
               root.set_left(del_node(a		,						a			)			)
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(a		,						a			)			)

     if get_height(a			) - get_height(a			) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
               __magic_name__						       =    left_rotation(a			)
          else:
               __magic_name__						       =    rl_rotation(a			)
     elif get_height(a			) - get_height(a			) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
               __magic_name__						       =    right_rotation(a			)
          else:
               __magic_name__						       =    lr_rotation(a			)
     __magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
     root.set_height(a			)
     return root






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]      ):
      __magic_name__						       =    None



 def 			snake_case__  (   self		:   List[Any]      ):
      return get_height(self.root      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
      print('''insert:''' + str(a__      )      )
      __magic_name__						       =    insert_node(self.root			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
      print('''delete:''' + str(a__      )      )
      if self.root is None:
           print('''Tree is empty!'''      )
           return
      __magic_name__						       =    del_node(self.root			,				a__      )



 def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
      __magic_name__						       =    ''''''
      __magic_name__						       =    MyQueue()
      q.push(self.root      )
      __magic_name__						       =    self.get_height()
      if layer == 0:
           return output
      __magic_name__						       =    0
      while not q.is_empty():
           __magic_name__						       =    q.pop()
           __magic_name__						       =    ''' ''' * int(math.pow(2			,				layer - 1      )      )
           output += space
           if node is None:
                output += "*"
                q.push(a__      )
                q.push(a__      )
           else:
                output += str(node.get_data()      )
                q.push(node.get_left()      )
                q.push(node.get_right()      )
           output += space
           __magic_name__						       =    cnt + 1
           for i in range(100      ):
                if cnt == math.pow(2			,				a__      ) - 1:
                     __magic_name__						       =    layer - 1
                     if layer == 0:
                          output += "\n*************************************"
                          return output
                     output += "\n"
                     break
      output += "\n*************************************"
      return output




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     import doctest

     doctest.testmod()


if __name__ == "__main__":
      _test()
      _lowerCAmelCase         =						AVLtree()
      _lowerCAmelCase         =						list(range(10))
      random.shuffle(lst)
      for i in lst:
            t.insert(i)
            print(str(t))
      random.shuffle(lst)
      for i in lst:
            t.del_node(i)
            print(str(t))






