





'''simple docstring'''







import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =ShapEPipeline
				__a            =['prompt']
				__a            =['prompt']
				__a            =[
				    'num_images_per_prompt',
				    'num_inference_steps',
				    'generator',
				    'latents',
				    'guidance_scale',
				    'frame_size',
				    'output_type',
				    'return_dict',
				]
				__a            =False




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return 32




				@property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								return 32




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								return self.time_input_dim * 4




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return 8




				@property
				def   UpperCamelCase__   (  self     :					Any					):
								_a								= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
								return tokenizer




				@property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								torch.manual_seed(0					)
								_a								= CLIPTextConfig(
								    bos_token_id=0					,				eos_token_id=2					,				hidden_size=self.text_embedder_hidden_size					,				projection_dim=self.text_embedder_hidden_size					,				intermediate_size=37					,				layer_norm_eps=1e-0_5					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=10_00					,				)
								return CLIPTextModelWithProjection(__a					)




				@property
				def   UpperCamelCase__   (  self     :					int					):
								torch.manual_seed(0					)

								_a								= {
								    "num_attention_heads": 2,
								    "attention_head_dim": 16,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 32,
								    "embedding_proj_dim": self.text_embedder_hidden_size,
								    "time_embed_dim": self.time_embed_dim,
								    "num_layers": 1,
								    "clip_embed_dim": self.time_input_dim * 2,
								    "additional_embeddings": 0,
								    "time_embed_act_fn": "gelu",
								    "norm_in_type": "layer",
								    "encoder_hid_proj_type": None,
								    "added_emb_type": None,
								}

								_a								= PriorTransformer(**__a					)
								return model




				@property
				def   UpperCamelCase__   (  self     :					Any					):
								torch.manual_seed(0					)

								_a								= {
								    "param_shapes": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    "d_latent": self.time_input_dim,
								    "d_hidden": self.renderer_dim,
								    "n_output": 12,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								_a								= ShapERenderer(**__a					)
								return model




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.dummy_prior
								_a								= self.dummy_text_encoder
								_a								= self.dummy_tokenizer
								_a								= self.dummy_renderer

								_a								= HeunDiscreteScheduler(
								    beta_schedule="exp"					,				num_train_timesteps=10_24					,				prediction_type="sample"					,				use_karras_sigmas=__a					,				clip_sample=__a					,				clip_sample_range=1.0					,				)
								_a								= {
								    "prior": prior,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components




				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[str]					,				__a     :					str=0					):
								if str(__a					).startswith("mps"					):
												_a								= torch.manual_seed(__a					)
								else:
												_a								= torch.Generator(device=__a					).manual_seed(__a					)
								_a								= {
								    "prompt": "horse",
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 32,
								    "output_type": "np",
								}
								return inputs




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= "cpu"

								_a								= self.get_dummy_components()

								_a								= self.pipeline_class(**__a					)
								_a								= pipe.to(__a					)

								pipe.set_progress_bar_config(disable=__a					)

								_a								= pipe(**self.get_dummy_inputs(__a					)					)
								_a								= output.images[0]
								_a								= image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								_a								= np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




				def   UpperCamelCase__   (  self     :					List[str]					):
								# 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     :					str					):
								_a								= torch_device == "cpu"
								_a								= True

								self._test_inference_batch_single_identical(
								    batch_size=2					,				test_max_difference=__a					,				relax_max_difference=__a					,				)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.get_dummy_components()
								_a								= self.pipeline_class(**__a					)
								_a								= pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)

								_a								= 1
								_a								= 2

								_a								= self.get_dummy_inputs(__a					)

								for key in inputs.keys():
												if key in self.batch_params:
																_a								= batch_size * [inputs[key]]

								_a								= pipe(**__a					,				num_images_per_prompt=__a					)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt


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






				"""simple docstring"""







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




				def   UpperCamelCase__   (  self     :					int					):
								_a								= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/shap_e/test_shap_e_np_out.npy"					)
								_a								= ShapEPipeline.from_pretrained("openai/shap-e"					)
								_a								= pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)

								_a								= torch.Generator(device=__a					).manual_seed(0					)

								_a								= pipe(
								    "a shark"					,				generator=__a					,				guidance_scale=15.0					,				num_inference_steps=64					,				frame_size=64					,				output_type="np"					,				).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__a					,				__a					)





'''simple docstring'''




from __future__ import annotations

import math

class 			UpperCamelCase_ :





	def __init__(  self		,		A    )			->			None:
								UpperCAmelCase     :		Optional[int]							     =		size
								# approximate the overall size of segment tree with given value
								UpperCAmelCase     :		Optional[int]							     =		[0 for i in range(0		,		4 * size    )]
								# create array to store lazy update
								UpperCAmelCase     :		Any							     =		[0 for i in range(0		,		4 * size    )]
								UpperCAmelCase     :		Tuple							     =		[0 for i in range(0		,		4 * size    )]  # flag for lazy update





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2 + 1





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			None:
								if left_element == right_element:
															UpperCAmelCase     :		str							     =		a[left_element - 1]
								else:
															UpperCAmelCase     :		Tuple							     =		(left_element + right_element) // 2
															self.build(self.left(A    )		,		A		,		A		,		A    )
															self.build(self.right(A    )		,		mid + 1		,		A		,		A    )
															UpperCAmelCase     :		str							     =		max(
															    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			bool:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
															UpperCAmelCase     :		int							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		List[str]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		int							     =		True

								if right_element < a or left_element > b:
															return True
								if left_element >= a and right_element <= b:
															UpperCAmelCase     :		Optional[Any]							     =		val
															if left_element != right_element:
																						UpperCAmelCase     :		Tuple							     =		val
																						UpperCAmelCase     :		int							     =		val
																						UpperCAmelCase     :		Any							     =		True
																						UpperCAmelCase     :		str							     =		True
															return True
								UpperCAmelCase     :		str							     =		(left_element + right_element) // 2
								self.update(self.left(A    )		,		A		,		A		,		A		,		A		,		A    )
								self.update(self.right(A    )		,		mid + 1		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		List[str]							     =		max(
								    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )
								return True





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A    )			->			int | float:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Any							     =		self.lazy[idx]
															UpperCAmelCase     :		Any							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Tuple							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		Tuple							     =		True
								if right_element < a or left_element > b:
															return -math.inf
								if left_element >= a and right_element <= b:
															return self.segment_tree[idx]
								UpperCAmelCase     :		Dict							     =		(left_element + right_element) // 2
								UpperCAmelCase     :		List[Any]							     =		self.query(self.left(A    )		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		str							     =		self.query(self.right(A    )		,		mid + 1		,		A		,		A		,		A    )
								return max(A		,		A    )






	def __str__(  self    )			->			str:
								return str([self.query(1		,		1		,		self.size		,		A		,		A    ) for i in range(1		,		self.size + 1    )]    )


if __name__ == "__main__":
							a   :			Optional[int]					  =    [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
							a   :			Optional[Any]					  =    1_5
							a   :			Union[str, Any]					  =    SegmentTree(size)
							segt.build(1, 1, size, A)
							print(segt.query(1, 1, size, 4, 6))
							print(segt.query(1, 1, size, 7, 1_1))
							print(segt.query(1, 1, size, 7, 1_2))
							segt.update(1, 1, size, 1, 3, 1_1_1)
							print(segt.query(1, 1, size, 1, 1_5))
							segt.update(1, 1, size, 7, 8, 2_3_5)
							print(segt)

