






"""simple docstring"""
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class 			UpperCamelCase_       (__A				,						__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				StableUnCLIPPipeline
	__magic_name__	      =				TEXT_TO_IMAGE_PARAMS
	__magic_name__	      =				TEXT_TO_IMAGE_BATCH_PARAMS
	__magic_name__	      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	__magic_name__	      =				TEXT_TO_IMAGE_IMAGE_PARAMS

	# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				UpperCAmelCase_     : Tuple			     =					32
				UpperCAmelCase_     : int			     =					embedder_hidden_size

				# prior components

				torch.manual_seed(0						)
				UpperCAmelCase_     : Any			     =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Optional[int]			     =					CLIPTextModelWithProjection(
				    CLIPTextConfig(
				        bos_token_id=0	,	eos_token_id=2	,	hidden_size=lowerCAmelCase_	,	projection_dim=lowerCAmelCase_	,	intermediate_size=37	,	layer_norm_eps=1e-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1_000	,	)						)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Tuple			     =					PriorTransformer(
				    num_attention_heads=2	,	attention_head_dim=12	,	embedding_dim=lowerCAmelCase_	,	num_layers=1	,	)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Optional[int]			     =					DDPMScheduler(
				    variance_type="fixed_small_log"	,	prediction_type="sample"	,	num_train_timesteps=1_000	,	clip_sample=lowerCAmelCase_	,	clip_sample_range=5.0	,	beta_schedule="squaredcos_cap_v2"	,	)

				# regular denoising components

				torch.manual_seed(0						)
				UpperCAmelCase_     : List[str]			     =					StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					DDPMScheduler(beta_schedule="squaredcos_cap_v2"						)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Optional[int]			     =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Union[str, Any]			     =					CLIPTextModel(
				    CLIPTextConfig(
				        bos_token_id=0	,	eos_token_id=2	,	hidden_size=lowerCAmelCase_	,	projection_dim=32	,	intermediate_size=37	,	layer_norm_eps=1e-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1_000	,	)						)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Any			     =					UNetaDConditionModel(
				    sample_size=32	,	in_channels=4	,	out_channels=4	,	down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")	,	up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")	,	block_out_channels=(32, 64)	,	attention_head_dim=(2, 4)	,	class_embed_type="projection"	,	projection_class_embeddings_input_dim=embedder_projection_dim * 2	,	cross_attention_dim=lowerCAmelCase_	,	layers_per_block=1	,	upcast_attention=lowerCAmelCase_	,	use_linear_projection=lowerCAmelCase_	,	)

				torch.manual_seed(0						)
				UpperCAmelCase_     : List[Any]			     =					DDIMScheduler(
				    beta_schedule="scaled_linear"	,	beta_start=0.0_0_0_8_5	,	beta_end=0.0_1_2	,	prediction_type="v_prediction"	,	set_alpha_to_one=lowerCAmelCase_	,	steps_offset=1	,	)

				torch.manual_seed(0						)
				UpperCAmelCase_     : Any			     =					AutoencoderKL()

				UpperCAmelCase_     : List[str]			     =					{
				    # prior components
				    "prior_tokenizer": prior_tokenizer,
				    "prior_text_encoder": prior_text_encoder,
				    "prior": prior,
				    "prior_scheduler": prior_scheduler,
				    # image noising components
				    "image_normalizer": image_normalizer,
				    "image_noising_scheduler": image_noising_scheduler,
				    # regular denoising components
				    "tokenizer": tokenizer,
				    "text_encoder": text_encoder,
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				}

				return components





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[int]=0						)						->			Tuple:
				if str(lowerCAmelCase_						).startswith("mps"						):
							UpperCAmelCase_     : Any			     =					torch.manual_seed(lowerCAmelCase_						)
				else:
							UpperCAmelCase_     : Union[str, Any]			     =					torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "prior_num_inference_steps": 2,
				    "output_type": "numpy",
				}
				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			int:
				UpperCAmelCase_     : Union[str, Any]			     =					torch_device == "cpu"

				self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Optional[int]:
				UpperCAmelCase_     : List[str]			     =					torch_device in ["cpu", "mps"]

				self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase_						)







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





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





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Any:
				UpperCAmelCase_     : List[Any]			     =					load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"						)

				UpperCAmelCase_     : Union[str, Any]			     =					StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"	,	torch_dtype=torch.floataa						)
				pipe.to(lowerCAmelCase_						)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
				# stable unclip will oom when integration tests are run on a V100,
				# so turn on memory savings
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				UpperCAmelCase_     : Optional[Any]			     =					torch.Generator(device="cpu"						).manual_seed(0						)
				UpperCAmelCase_     : Optional[Any]			     =					pipe("anime turle"	,	generator=lowerCAmelCase_	,	output_type="np"						)

				UpperCAmelCase_     : Optional[Any]			     =					output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(lowerCAmelCase_	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Tuple:
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				UpperCAmelCase_     : List[str]			     =					StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"	,	torch_dtype=torch.floataa						)
				UpperCAmelCase_     : Optional[int]			     =					pipe.to(lowerCAmelCase_						)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				UpperCAmelCase_     : str			     =					pipe(
				    "anime turtle"	,	prior_num_inference_steps=2	,	num_inference_steps=2	,	output_type="np"	,	)

				UpperCAmelCase_     : Dict			     =					torch.cuda.max_memory_allocated()
				# make sure that less than 7 GB is allocated
				assert mem_bytes < 7 * 10**9











"""simple docstring"""
from __future__ import annotations







class 			UpperCamelCase_       :





	def __init__(      self					: Any	,	lowerCAmelCase_					: int						)						->			None:
				UpperCAmelCase_     : Any			     =					data
				UpperCAmelCase_     : Node | None			     =					None
				UpperCAmelCase_     : Node | None			     =					None





def 				snake_case      (   A__						):  # In Order traversal of the tree
			if tree:
						display(tree.left						)
						print(tree.data						)
						display(tree.right						)

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

def 				snake_case      (   A__						):
			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 				snake_case      (   ):  # Main function for testing.
			UpperCAmelCase_     : List[str]			     =					Node(1						)
			UpperCAmelCase_     : Any			     =					Node(2						)
			UpperCAmelCase_     : Optional[Any]			     =					Node(3						)
			UpperCAmelCase_     : Union[str, Any]			     =					Node(4						)
			UpperCAmelCase_     : int			     =					Node(5						)
			UpperCAmelCase_     : Optional[int]			     =					Node(6						)
			UpperCAmelCase_     : Any			     =					Node(7						)
			UpperCAmelCase_     : List[str]			     =					Node(8						)
			UpperCAmelCase_     : List[Any]			     =					Node(9						)

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


if __name__ == "__main__":
			main()



