





'''simple docstring'''






from __future__ import annotations




class 					UpperCAmelCase	:




      def __init__(     self     :					int     ,     __snake_case     :					int    )							-> Optional[Any]:
            _lowerCAmelCase					=				data
            _lowerCAmelCase					=				None
            _lowerCAmelCase					=				None




def   UpperCamelCase__ (						lowerCAmelCase    ):  # In Order traversal of the tree





      """simple docstring"""


      if tree:
            display(tree.left    )
            print(tree.data    )
            display(tree.right    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      return 1 + max(depth_of_tree(tree.left    )				,      depth_of_tree(tree.right    )    ) if tree else 0
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if not tree:
            return True
      if tree.left and tree.right:
            return is_full_binary_tree(tree.left    ) and is_full_binary_tree(tree.right    )
      else:
            return not tree.left and not tree.right
def   UpperCamelCase__ (						):  # Main function for testing.





      """simple docstring"""


      _lowerCAmelCase					=				Node(1    )
      _lowerCAmelCase					=				Node(2    )
      _lowerCAmelCase					=				Node(3    )
      _lowerCAmelCase					=				Node(4    )
      _lowerCAmelCase					=				Node(5    )
      _lowerCAmelCase					=				Node(6    )
      _lowerCAmelCase					=				Node(7    )
      _lowerCAmelCase					=				Node(8    )
      _lowerCAmelCase					=				Node(9    )

      print(is_full_binary_tree(UpperCAmelCase_    )    )
      print(depth_of_tree(UpperCAmelCase_    )    )
      print("""Tree is: """    )
      display(UpperCAmelCase_    )


if __name__ == "__main__":
      main()











'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case_					:      Tuple    			=    False






@skip_mps
class 				lowercase__      (				lowercase		,   lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     StableDiffusionAttendAndExcitePipeline
	lowercase__    							=     False
	lowercase__    							=     TEXT_TO_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}  )
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS





	@classmethod
	def       UpperCamelCase_    (					cls   :							Any					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							List[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Union[str, Any]												=  UNetaDConditionModel(
							    block_out_channels=(32, 64)					,layers_per_block=1					,sample_size=32					,in_channels=4					,out_channels=4					,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,cross_attention_dim=32					,attention_head_dim=(2, 4)					,use_linear_projection=lowerCamelCase__					,)
							_UpperCamelCase   :						List[str]												=  DDIMScheduler(
							    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  AutoencoderKL(
							    block_out_channels=[32, 64]					,in_channels=3					,out_channels=3					,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,latent_channels=4					,sample_size=128					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  CLIPTextConfig(
							    bos_token_id=0					,eos_token_id=2					,hidden_size=32					,intermediate_size=37					,layer_norm_eps=1E-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1000					,hidden_act='gelu'					,projection_dim=512					,)
							_UpperCamelCase   :						List[str]												=  CLIPTextModel(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

							_UpperCamelCase   :						str												=  {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							str=0					):







							'''simple docstring'''
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						Union[str, Any]												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						int												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  'cpu'

							_UpperCamelCase   :						List[Any]												=  self.get_dummy_components()
							_UpperCamelCase   :						Union[str, Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  pipe(**lowerCamelCase__					).images
							_UpperCamelCase   :						Union[str, Any]												=  image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,(1, 64, 64, 3)					)
							_UpperCamelCase   :						Union[str, Any]												=  np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]					)
							_UpperCamelCase   :						Tuple												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							self._test_inference_batch_single_identical(batch_size=2					,expected_max_diff=7E-4					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							super().test_save_load_local(expected_max_difference=5E-4					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							super().test_save_load_optional_components(expected_max_difference=4E-4					)






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





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							Optional[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(51					)

							_UpperCamelCase   :						Optional[Any]												=  StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa					)
							pipe.to('cuda'					)

							_UpperCamelCase   :						Any												=  'a painting of an elephant with glasses'
							_UpperCamelCase   :						str												=  [5, 7]

							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,token_indices=lowerCamelCase__					,guidance_scale=7.5					,generator=lowerCamelCase__					,num_inference_steps=5					,max_iter_to_alter=5					,output_type='numpy'					,).images[0]

							_UpperCamelCase   :						Tuple												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'					)
							assert np.abs((expected_image - image).max()					) < 5E-1


