




'''simple docstring'''

from __future__ import annotations

import queue






class 		a							:


				def __init__(						self       :   Optional[int]			,							lowercase_       :   Optional[Any]   ):
									snake_case_		     =							data
									snake_case_		     =							None
									snake_case_		     =							None







def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					if not isinstance(__a, __a      ) or not node:
										return
					snake_case_		     =							queue.Queue()
					q.put(__a      )
					while not q.empty():
										snake_case_		     =							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 						__magic_name__       (      __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					if not isinstance(__a, __a      ) or not node:
										return
					snake_case_		     =							queue.Queue()
					q.put(__a      )
					while not q.empty():
										snake_case_		     =							[]
										while not q.empty():
															snake_case_		     =							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(__a      )


def 						__magic_name__       (      __UpperCAmelCase      )						->   Union[str, Any]:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase = "", __UpperCAmelCase=50, __UpperCAmelCase="*"      )						->   Optional[Any]:

					'''simple docstring'''

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


if __name__ == "__main__":
							import doctest

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

							a    :							Any										=    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())






"""simple docstring"""







import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







@skip_mps
class 					A_(SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			Dict												=			TextToVideoSDPipeline
			a_       :			Dict												=			TEXT_TO_IMAGE_PARAMS
			a_       :			Dict												=			TEXT_TO_IMAGE_BATCH_PARAMS
			# No `output_type`.
			a_       :			str												=			frozenset(
			    [
			        """num_inference_steps""",
			        """generator""",
			        """latents""",
			        """return_dict""",
			        """callback""",
			        """callback_steps""",
			    ]      )
			def    _lowerCAmelCase		(	self							):
						torch.manual_seed(0							)
						_lowerCamelCase					:					List[Any]	       =    UNetaDConditionModel(
						    block_out_channels=(32, 64, 64, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')				,   up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')				,   cross_attention_dim=32				,   attention_head_dim=4				,   )
						_lowerCamelCase					:					Optional[int]	       =    DDIMScheduler(
						    beta_start=0.0_0_0_8_5				,   beta_end=0.0_1_2				,   beta_schedule='scaled_linear'				,   clip_sample=A				,   set_alpha_to_one=A				,   )
						torch.manual_seed(0							)
						_lowerCamelCase					:					Dict	       =    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							)
						_lowerCamelCase					:					Optional[int]	       =    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				,   )
						_lowerCamelCase					:					str	       =    CLIPTextModel(A							)
						_lowerCamelCase					:					List[str]	       =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						_lowerCamelCase					:					Any	       =    {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						}
						return components
			def    _lowerCAmelCase		(	self				,   A				,   A=0							):
						if str(A							).startswith('mps'							):
									_lowerCamelCase					:					Tuple	       =    torch.manual_seed(A							)
						else:
									_lowerCamelCase					:					Optional[int]	       =    torch.Generator(device=A							).manual_seed(A							)
						_lowerCamelCase					:					Optional[int]	       =    {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'pt',
						}
						return inputs
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    'cpu'  # ensure determinism for the device-dependent torch.Generator
						_lowerCamelCase					:					Tuple	       =    self.get_dummy_components()
						_lowerCamelCase					:					Dict	       =    TextToVideoSDPipeline(**A							)
						_lowerCamelCase					:					Optional[int]	       =    sd_pipe.to(A							)
						sd_pipe.set_progress_bar_config(disable=A							)

						_lowerCamelCase					:					Union[str, Any]	       =    self.get_dummy_inputs(A							)
						_lowerCamelCase					:					Union[str, Any]	       =    'np'
						_lowerCamelCase					:					Optional[int]	       =    sd_pipe(**A							).frames
						_lowerCamelCase					:					Dict	       =    frames[0][-3:, -3:, -1]

						assert frames[0].shape == (64, 64, 3)
						_lowerCamelCase					:					Tuple	       =    np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
			def    _lowerCAmelCase		(	self							):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A				,   expected_max_diff=3E-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(test_mean_pixel_difference=A				,   expected_max_diff=1E-2							)
			@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'							)
			def    _lowerCAmelCase		(	self							):
						pass
			@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'							)
			def    _lowerCAmelCase		(	self							):
						pass
			@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'							)
			def    _lowerCAmelCase		(	self							):
						pass

			def    _lowerCAmelCase		(	self							):
						return super().test_progress_bar()







@slow
@skip_mps
class 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'							)

						_lowerCamelCase					:					Dict	       =    TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'							)
						_lowerCamelCase					:					Dict	       =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
						_lowerCamelCase					:					Tuple	       =    pipe.to('cuda'							)

						_lowerCamelCase					:					str	       =    'Spiderman is surfing'
						_lowerCamelCase					:					Any	       =    torch.Generator(device='cpu'							).manual_seed(0							)

						_lowerCamelCase					:					Union[str, Any]	       =    pipe(A				,   generator=A				,   num_inference_steps=25				,   output_type='pt'							).frames
						_lowerCamelCase					:					Any	       =    video_frames.cpu().numpy()

						assert np.abs(expected_video - video							).mean() < 5E-2

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'							)

						_lowerCamelCase					:					int	       =    TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'							)
						_lowerCamelCase					:					Optional[Any]	       =    pipe.to('cuda'							)

						_lowerCamelCase					:					Tuple	       =    'Spiderman is surfing'
						_lowerCamelCase					:					Union[str, Any]	       =    torch.Generator(device='cpu'							).manual_seed(0							)

						_lowerCamelCase					:					Tuple	       =    pipe(A				,   generator=A				,   num_inference_steps=2				,   output_type='pt'							).frames
						_lowerCamelCase					:					Optional[int]	       =    video_frames.cpu().numpy()

						assert np.abs(expected_video - video							).mean() < 5E-2





