
"""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 __lowerCAmelCase					:




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								torch.manual_seed(0						)
								A_								=    TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"						)

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

								torch.manual_seed(0						)
								A_								=    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_								=    DDPMScheduler(
								    num_train_timesteps=1_000							, beta_schedule="squaredcos_cap_v2"							, beta_start=0.0_0_0_1							, beta_end=0.0_2							, thresholding=lowercase_							, dynamic_thresholding_ratio=0.9_5							, sample_max_value=1.0							, prediction_type="epsilon"							, variance_type="learned_range"							, )

								torch.manual_seed(0						)
								A_								=    IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								torch.manual_seed(0						)
								A_								=    TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"						)

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

								torch.manual_seed(0						)
								A_								=    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=1.4_1_4							, time_embedding_act_fn="gelu"							, time_embedding_dim=32							, )
								unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

								torch.manual_seed(0						)
								A_								=    DDPMScheduler(
								    num_train_timesteps=1_000							, beta_schedule="squaredcos_cap_v2"							, beta_start=0.0_0_0_1							, beta_end=0.0_2							, thresholding=lowercase_							, dynamic_thresholding_ratio=0.9_5							, sample_max_value=1.0							, prediction_type="epsilon"							, variance_type="learned_range"							, )

								torch.manual_seed(0						)
								A_								=    DDPMScheduler(
								    num_train_timesteps=1_000							, beta_schedule="squaredcos_cap_v2"							, beta_start=0.0_0_0_1							, beta_end=0.0_2							, )

								torch.manual_seed(0						)
								A_								=    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   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								A_								=    self.get_dummy_components()
								A_								=    self.pipeline_class(**lowercase_						)
								pipe.to(lowercase_						)
								pipe.set_progress_bar_config(disable=lowercase_						)

								A_								=    self.get_dummy_inputs(lowercase_						)

								A_								=    inputs["prompt"]
								A_								=    inputs["generator"]
								A_								=    inputs["num_inference_steps"]
								A_								=    inputs["output_type"]

								if "image" in inputs:
													A_								=    inputs["image"]
								else:
													A_								=    None

								if "mask_image" in inputs:
													A_								=    inputs["mask_image"]
								else:
													A_								=    None

								if "original_image" in inputs:
													A_								=    inputs["original_image"]
								else:
													A_								=    None

								A_			,     A_								=    pipe.encode_prompt(lowercase_						)

								# inputs with prompt converted to embeddings
								A_								=    {
								    "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_								=    image

								if mask_image is not None:
													A_								=    mask_image

								if original_image is not None:
													A_								=    original_image

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

								A_								=    pipe(**lowercase_						)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowercase_						)
													A_								=    self.pipeline_class.from_pretrained(lowercase_						)
													pipe_loaded.to(lowercase_						)
													pipe_loaded.set_progress_bar_config(disable=lowercase_						)

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

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

								A_								=    self.get_dummy_inputs(lowercase_						)

								A_								=    inputs["generator"]
								A_								=    inputs["num_inference_steps"]
								A_								=    inputs["output_type"]

								# inputs with prompt converted to embeddings
								A_								=    {
								    "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_								=    image

								if mask_image is not None:
													A_								=    mask_image

								if original_image is not None:
													A_								=    original_image

								A_								=    pipe_loaded(**lowercase_						)[0]

								A_								=    np.abs(to_np(lowercase_						) - to_np(lowercase_						)						).max()
								self.assertLess(lowercase_							, 1e-4						)






			def   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								A_								=    self.get_dummy_components()
								A_								=    self.pipeline_class(**lowercase_						)
								pipe.to(lowercase_						)
								pipe.set_progress_bar_config(disable=lowercase_						)

								A_								=    self.get_dummy_inputs(lowercase_						)
								A_								=    pipe(**lowercase_						)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowercase_						)
													A_								=    self.pipeline_class.from_pretrained(lowercase_						)
													pipe_loaded.to(lowercase_						)
													pipe_loaded.set_progress_bar_config(disable=lowercase_						)

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

								A_								=    self.get_dummy_inputs(lowercase_						)
								A_								=    pipe_loaded(**lowercase_						)[0]

								A_								=    np.abs(to_np(lowercase_						) - to_np(lowercase_						)						).max()
								self.assertLess(lowercase_							, 1e-4						)










'''simple docstring'''

from collections.abc import Sequence
from queue import Queue






class 		a							:


				def __init__(						self       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int=None			,							lowercase_       :   List[Any]=None   ):
									snake_case_		     =							start
									snake_case_		     =							end
									snake_case_		     =							val
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							left
									snake_case_		     =							right


				def __repr__(						self       :   Any   ):
									return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   Sequence			,							lowercase_       :   List[str]   ):
									snake_case_		     =							collection
									snake_case_		     =							function
									if self.collection:
														snake_case_		     =							self._build_tree(0			,							len(lowercase_   ) - 1   )


				def     A_					(						self       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									self._update_tree(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]   ):
									return self._query_range(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   int			,							lowercase_       :   int   ):
									if start == end:
														return SegmentTreeNode(lowercase_			,							lowercase_			,							self.collection[start]   )
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							self._build_tree(lowercase_			,							lowercase_   )
									snake_case_		     =							self._build_tree(mid + 1			,							lowercase_   )
									return SegmentTreeNode(lowercase_			,							lowercase_			,							self.fn(left.val			,							right.val   )			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									if node.start == i and node.end == i:
														snake_case_		     =							val
														return
									if i <= node.mid:
														self._update_tree(node.left			,							lowercase_			,							lowercase_   )
									else:
														self._update_tree(node.right			,							lowercase_			,							lowercase_   )
									snake_case_		     =							self.fn(node.left.val			,							node.right.val   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									if node.start == i and node.end == j:
														return node.val

									if i <= node.mid:
														if j <= node.mid:
																			# range in left child tree
																			return self._query_range(node.left			,							lowercase_			,							lowercase_   )
														else:
																			# range in left child tree and right child tree
																			return self.fn(
																			    self._query_range(node.left			,							lowercase_			,							node.mid   )			,							self._query_range(node.right			,							node.mid + 1			,							lowercase_   )			,							)
									else:
														# range in right child tree
														return self._query_range(node.right			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any   ):
									if self.root is not None:
														snake_case_		     =							Queue()
														queue.put(self.root   )
														while not queue.empty():
																			snake_case_		     =							queue.get()
																			yield node

																			if node.left is not None:
																								queue.put(node.left   )

																			if node.right is not None:
																								queue.put(node.right   )


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('*' * 50)
														a    :							List[Any]										=    SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()

