



from __future__ import annotations

import math
import random
from typing import Any





class 		UpperCAmelCase						:





   def __init__(	self			):
         _lowerCAmelCase 			=  []
         _lowerCAmelCase 			=  0
         _lowerCAmelCase 			=  0





   def 	__lowerCAmelCase							(	self			):
         return self.head == self.tail





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         self.data.append(_lowerCAmelCase			)
         _lowerCAmelCase 			=  self.tail + 1





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.data[self.head]
         _lowerCAmelCase 			=  self.head + 1
         return ret





   def 	__lowerCAmelCase							(	self			):
         return self.tail - self.head





   def 	__lowerCAmelCase							(	self			):
         print(self.data			)
         print('''**************'''			)
         print(self.data[self.head : self.tail]			)






class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  data
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  1





   def 	__lowerCAmelCase							(	self			):
         return self.data





   def 	__lowerCAmelCase							(	self			):
         return self.left





   def 	__lowerCAmelCase							(	self			):
         return self.right





   def 	__lowerCAmelCase							(	self			):
         return self.height





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  data





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  node





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  node





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  height






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode | None				)->int:
      if node is None:
            return 0
      return node.get_height()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	int				)->int:
      if a > b:
            return a
      return b
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->MyNode:
      print('''left rotation node:'''     ,				node.get_data()				)
      _lowerCAmelCase 			=  node.get_left()
      assert ret is not None
      node.set_left(ret.get_right()				)
      ret.set_right(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  my_max(get_height(node.get_right()				)     ,				get_height(node.get_left()				)				) + 1
      node.set_height(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  my_max(get_height(ret.get_right()				)     ,				get_height(ret.get_left()				)				) + 1
      ret.set_height(_SCREAMING_SNAKE_CASE				)
      return ret
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->MyNode:
      print('''right rotation node:'''     ,				node.get_data()				)
      _lowerCAmelCase 			=  node.get_right()
      assert ret is not None
      node.set_right(ret.get_left()				)
      ret.set_left(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  my_max(get_height(node.get_right()				)     ,				get_height(node.get_left()				)				) + 1
      node.set_height(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  my_max(get_height(ret.get_right()				)     ,				get_height(ret.get_left()				)				) + 1
      ret.set_height(_SCREAMING_SNAKE_CASE				)
      return ret
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->MyNode:
      _lowerCAmelCase 			=  node.get_left()
      assert left_child is not None
      node.set_left(left_rotation(_SCREAMING_SNAKE_CASE				)				)
      return right_rotation(_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->MyNode:
      _lowerCAmelCase 			=  node.get_right()
      assert right_child is not None
      node.set_right(right_rotation(_SCREAMING_SNAKE_CASE				)				)
      return left_rotation(_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode | None     ,				_SCREAMING_SNAKE_CASE   :	Any				)->MyNode | None:
      if node is None:
            return MyNode(_SCREAMING_SNAKE_CASE				)
      if data < node.get_data():
            node.set_left(insert_node(node.get_left()     ,				_SCREAMING_SNAKE_CASE				)				)
            if (
                get_height(node.get_left()				) - get_height(node.get_right()				) == 2
            ):  # an unbalance detected
                  _lowerCAmelCase 			=  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
                        _lowerCAmelCase 			=  right_rotation(_SCREAMING_SNAKE_CASE				)
                  else:
                        _lowerCAmelCase 			=  lr_rotation(_SCREAMING_SNAKE_CASE				)
      else:
            node.set_right(insert_node(node.get_right()     ,				_SCREAMING_SNAKE_CASE				)				)
            if get_height(node.get_right()				) - get_height(node.get_left()				) == 2:
                  _lowerCAmelCase 			=  node.get_right()
                  assert right_child is not None
                  if data < right_child.get_data():
                        _lowerCAmelCase 			=  rl_rotation(_SCREAMING_SNAKE_CASE				)
                  else:
                        _lowerCAmelCase 			=  left_rotation(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  my_max(get_height(node.get_right()				)     ,				get_height(node.get_left()				)				) + 1
      node.set_height(_SCREAMING_SNAKE_CASE				)
      return node
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->Any:
      while True:
            _lowerCAmelCase 			=  root.get_right()
            if right_child is None:
                  break
            _lowerCAmelCase 			=  right_child
      return root.get_data()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode				)->Any:
      while True:
            _lowerCAmelCase 			=  root.get_left()
            if left_child is None:
                  break
            _lowerCAmelCase 			=  left_child
      return root.get_data()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	MyNode     ,				_SCREAMING_SNAKE_CASE   :	Any				)->MyNode | None:
      _lowerCAmelCase 			=  root.get_left()
      _lowerCAmelCase 			=  root.get_right()
      if root.get_data() == data:
            if left_child is not None and right_child is not None:
                  _lowerCAmelCase 			=  get_left_most(_SCREAMING_SNAKE_CASE				)
                  root.set_data(_SCREAMING_SNAKE_CASE				)
                  root.set_right(del_node(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)				)
            elif left_child is not None:
                  _lowerCAmelCase 			=  left_child
            elif right_child is not None:
                  _lowerCAmelCase 			=  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(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)				)
      else:  # root.get_data() < data
            if right_child is None:
                  return root
            else:
                  root.set_right(del_node(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)				)

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





class 		UpperCAmelCase						:





   def __init__(	self			):
         _lowerCAmelCase 			=  None





   def 	__lowerCAmelCase							(	self			):
         return get_height(self.root			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         print('''insert:''' + str(_lowerCAmelCase			)			)
         _lowerCAmelCase 			=  insert_node(self.root       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         print('''delete:''' + str(_lowerCAmelCase			)			)
         if self.root is None:
               print('''Tree is empty!'''			)
               return
         _lowerCAmelCase 			=  del_node(self.root       ,     _lowerCAmelCase			)





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






def 							UpperCAmelCase__		(				)->None:
      import doctest

      doctest.testmod()


if __name__ == "__main__":
    _test()
    UpperCAmelCase_									=							AVLtree()
    UpperCAmelCase_									=							list(range(1_0))
    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))











import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     DiTPipeline
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   SCREAMING_SNAKE_CASE__							=     PipelineTesterMixin.required_optional_params - {
       '''latents''',
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(0			)
         _lowerCAmelCase 			=  TransformeraDModel(
             sample_size=16       ,     num_layers=2       ,     patch_size=4       ,     attention_head_dim=8       ,     num_attention_heads=2       ,     in_channels=4       ,     out_channels=8       ,     attention_bias=_lowerCAmelCase       ,     activation_fn='''gelu-approximate'''       ,     num_embeds_ada_norm=1_000       ,     norm_type='''ada_norm_zero'''       ,     norm_elementwise_affine=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  AutoencoderKL()
         _lowerCAmelCase 			=  DDIMScheduler()
         _lowerCAmelCase 			=  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
         if str(_lowerCAmelCase			).startswith('''mps'''			):
               _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
         _lowerCAmelCase 			=  {
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''cpu'''

         _lowerCAmelCase 			=  self.get_dummy_components()
         _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)
         pipe.to(_lowerCAmelCase			)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
         _lowerCAmelCase 			=  pipe(**_lowerCAmelCase			).images
         _lowerCAmelCase 			=  image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,     (1, 16, 16, 3)			)
         _lowerCAmelCase 			=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]			)
         _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
         self.assertLessEqual(_lowerCAmelCase       ,     1E-3			)





   def 	__lowerCAmelCase							(	self			):
         self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase       ,     expected_max_diff=1E-3			)





   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()       ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,     )
   def 	__lowerCAmelCase							(	self			):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)






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





   def 	__lowerCAmelCase							(	self			):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  torch.manual_seed(0			)

         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=40       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''			)
               assert np.abs((expected_image - image).max()			) < 1E-2





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''			)
         _lowerCAmelCase 			=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=25       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   F'''/dit/{word}_512.npy'''			)

               assert np.abs((expected_image - image).max()			) < 1E-1