





'''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 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	str									=       KandinskyVaaPipeline
       _snake_case      :	Union[str, Any]									=       [
           'image_embeds',
           'negative_image_embeds',
       ]
       _snake_case      :	Optional[Any]									=       ['image_embeds', 'negative_image_embeds']
       _snake_case      :	Union[str, Any]									=       [
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       _snake_case      :	List[Any]									=       False





       @property
       def snake_case__		(				self  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            return 32





       @property
       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            return 32





       @property
       def snake_case__		(				self  :						List[str]	)       ->	List[Any]:

            '''simple docstring'''




            return self.time_input_dim





       @property
       def snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            return self.time_input_dim * 4





       @property
       def snake_case__		(				self  :						Tuple	)       ->	List[str]:

            '''simple docstring'''




            return 100





       @property
       def snake_case__		(				self  :						Optional[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            torch.manual_seed(0	)

            _UpperCamelCase							       =			{
                '''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,
            }

            _UpperCamelCase							       =			UNetaDConditionModel(**lowerCAmelCase__	)
            return model





       @property
       def snake_case__		(				self  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            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  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            torch.manual_seed(0	)
            _UpperCamelCase							       =			VQModel(**self.dummy_movq_kwargs	)
            return model





       def snake_case__		(				self  :						Dict	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.dummy_unet
            _UpperCamelCase							       =			self.dummy_movq

            _UpperCamelCase							       =			DDIMScheduler(
                num_train_timesteps=1000			,					beta_schedule='''linear'''			,					beta_start=0.00085			,					beta_end=0.012			,					clip_sample=lowerCAmelCase__			,					set_alpha_to_one=lowerCAmelCase__			,					steps_offset=1			,					prediction_type='''epsilon'''			,					thresholding=lowerCAmelCase__			,					)

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





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Tuple=0	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(lowerCAmelCase__	)	).to(lowerCAmelCase__	)
            _UpperCamelCase							       =			floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(seed + 1	)	).to(
                lowerCAmelCase__	)
            if str(lowerCAmelCase__	).startswith('''mps'''	):
                 _UpperCamelCase							       =			torch.manual_seed(lowerCAmelCase__	)
            else:
                 _UpperCamelCase							       =			torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''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  :						Any	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			'''cpu'''

            _UpperCamelCase							       =			self.get_dummy_components()

            _UpperCamelCase							       =			self.pipeline_class(**lowerCAmelCase__	)
            _UpperCamelCase							       =			pipe.to(lowerCAmelCase__	)

            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			pipe(**self.get_dummy_inputs(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			output.images

            _UpperCamelCase							       =			pipe(
                **self.get_dummy_inputs(lowerCAmelCase__	)			,					return_dict=lowerCAmelCase__			,					)[0]

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

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

            _UpperCamelCase							       =			np.array(
                [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]	)

            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					):






       """simple docstring"""





       def snake_case__		(				self  :						Any	)       ->	Any:

            '''simple docstring'''




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







       def snake_case__		(				self  :						List[Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''	)

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

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

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

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

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

            _UpperCamelCase							       =			output.images[0]

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

            assert_mean_pixel_difference(lowerCAmelCase__			,					lowerCAmelCase__	)










'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



