





"""simple docstring"""






from __future__ import annotations

from collections.abc import Iterator



class 					__UpperCAmelCase							:
				def __init__(		self    :			Optional[Any]	, a_    :			int    )	->	None:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			value
							a__  :	Node | None		 =			None
							a__  :	Node | None		 =			None



class 					__UpperCAmelCase							:
				def __init__(		self    :			List[str]	, a_    :			Node    )	->	None:



							'''simple docstring'''



							a__  :	Dict		 =			tree
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			Node | None    )	->	int:



							'''simple docstring'''



							if node is None:
										return 0
							return node.value + (
							    self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
							)






				def __iter__(		self    :			Union[str, Any]    )	->	Iterator[int]:



							'''simple docstring'''



							yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class 					__UpperCAmelCase							:
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	List[str]		 =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	int		 =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	Any		 =			UNetaDConditionModel(
							    sample_size=32	, layers_per_block=1	, block_out_channels=[32, 64]	, down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]	, mid_block_type="UNetMidBlock2DSimpleCrossAttn"	, up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	, in_channels=3	, out_channels=6	, cross_attention_dim=32	, encoder_hid_dim=32	, attention_head_dim=8	, addition_embed_type="text"	, addition_embed_type_num_heads=2	, cross_attention_norm="group_norm"	, resnet_time_scale_shift="scale_shift"	, act_fn="gelu"	, )
							unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							torch.manual_seed(0    )
							a__  :	str		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, thresholding=a_	, dynamic_thresholding_ratio=0.95	, sample_max_value=1.0	, prediction_type="epsilon"	, variance_type="learned_range"	, )

							torch.manual_seed(0    )
							a__  :	Optional[int]		 =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	List[Any]		 =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	List[str]		 =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

							torch.manual_seed(0    )
							a__  :	Dict		 =			UNetaDConditionModel(
							    sample_size=32	, layers_per_block=[1, 2]	, block_out_channels=[32, 64]	, down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]	, mid_block_type="UNetMidBlock2DSimpleCrossAttn"	, up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	, in_channels=6	, out_channels=6	, cross_attention_dim=32	, encoder_hid_dim=32	, attention_head_dim=8	, addition_embed_type="text"	, addition_embed_type_num_heads=2	, cross_attention_norm="group_norm"	, resnet_time_scale_shift="scale_shift"	, act_fn="gelu"	, class_embed_type="timestep"	, mid_block_scale_factor=1.414	, time_embedding_act_fn="gelu"	, time_embedding_dim=32	, )
							unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							torch.manual_seed(0    )
							a__  :	Optional[int]		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, thresholding=a_	, dynamic_thresholding_ratio=0.95	, sample_max_value=1.0	, prediction_type="epsilon"	, variance_type="learned_range"	, )

							torch.manual_seed(0    )
							a__  :	Tuple		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_schedule="squaredcos_cap_v2"	, beta_start=0.0001	, beta_end=0.02	, )

							torch.manual_seed(0    )
							a__  :	Optional[Any]		 =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Any		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs(a_    )

							a__  :	Optional[int]		 =			inputs["prompt"]
							a__  :	List[Any]		 =			inputs["generator"]
							a__  :	Optional[int]		 =			inputs["num_inference_steps"]
							a__  :	Any		 =			inputs["output_type"]

							if "image" in inputs:
										a__  :	Any		 =			inputs["image"]
							else:
										a__  :	Dict		 =			None

							if "mask_image" in inputs:
										a__  :	Optional[int]		 =			inputs["mask_image"]
							else:
										a__  :	Any		 =			None

							if "original_image" in inputs:
										a__  :	List[Any]		 =			inputs["original_image"]
							else:
										a__  :	str		 =			None

							a__ ,	a__  :	Optional[int]		 =			pipe.encode_prompt(a_    )

							# inputs with prompt converted to embeddings
							a__  :	Union[str, Any]		 =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Optional[int]		 =			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
										setattr(a_	, a_	, a_    )

							a__  :	int		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	List[str]		 =			self.pipeline_class.from_pretrained(a_    )
										pipe_loaded.to(a_    )
										pipe_loaded.set_progress_bar_config(disable=a_    )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(a_	, a_    ) is None	, F"`{optional_component}` did not stay set to None after loading."	, )

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs(a_    )

							a__  :	str		 =			inputs["generator"]
							a__  :	Dict		 =			inputs["num_inference_steps"]
							a__  :	Optional[int]		 =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							a__  :	List[Any]		 =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										a__  :	Dict		 =			image

							if mask_image is not None:
										a__  :	Any		 =			mask_image

							if original_image is not None:
										a__  :	Dict		 =			original_image

							a__  :	Optional[Any]		 =			pipe_loaded(**a_    )[0]

							a__  :	int		 =			np.abs(to_np(a_    ) - to_np(a_    )    ).max()
							self.assertLess(a_	, 1E-4    )






				def       UpperCAmelCase      (		self    :			int    )	->	Any:



							'''simple docstring'''



							a__  :	Dict		 =			self.get_dummy_components()
							a__  :	Dict		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[str]		 =			self.get_dummy_inputs(a_    )
							a__  :	Dict		 =			pipe(**a_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_    )
										a__  :	str		 =			self.pipeline_class.from_pretrained(a_    )
										pipe_loaded.to(a_    )
										pipe_loaded.set_progress_bar_config(disable=a_    )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

							a__  :	Optional[int]		 =			self.get_dummy_inputs(a_    )
							a__  :	Optional[int]		 =			pipe_loaded(**a_    )[0]

							a__  :	List[Any]		 =			np.abs(to_np(a_    ) - to_np(a_    )    ).max()
							self.assertLess(a_	, 1E-4    )