






from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__A      =				TypeVar('T')





class 						SCREAMING_SNAKE_CASE				( Generic[T]					):





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   list[T]		,     __A:   Callable[[T, T], T]   )    ->				str:
												_A								= None

												_A								= len(__SCREAMING_SNAKE_CASE   )
												_A								= [any_type for _ in range(self.N   )] + arr
												_A								= fnc
												self.build()

							def       __A (  self:   Optional[int]   )    ->				int:

												for p in range(self.N - 1		,     0		,     -1   ):
																	_A								= self.fn(self.st[p * 2]		,     self.st[p * 2 + 1]   )

							def       __A (  self:   List[Any]		,     __A:   int		,     __A:   T   )    ->				int:
												p += self.N
												_A								= v
												while p > 1:
																	_A								= p // 2
																	_A								= self.fn(self.st[p * 2]		,     self.st[p * 2 + 1]   )



							def       __A (  self:   List[Any]		,     __A:   int		,     __A:   int   )    ->				List[str]:  # noqa: E741
												_A							,_A								= l + self.N, r + self.N

												_A								= None
												while l <= r:
																	if l % 2 == 1:
																						_A								= self.st[l] if res is None else self.fn(__SCREAMING_SNAKE_CASE		,     self.st[l]   )
																	if r % 2 == 0:
																						_A								= self.st[r] if res is None else self.fn(__SCREAMING_SNAKE_CASE		,     self.st[r]   )
																	_A							,_A								= (l + 1) // 2, (r - 1) // 2
												return res


if __name__ == "__main__":
				from functools import reduce

				__A      =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

				__A      =				{
				    0: 7,
				    1: 2,
				    2: 6,
				    3: -14,
				    4: 5,
				    5: 4,
				    6: 7,
				    7: -10,
				    8: 9,
				    9: 10,
				    10: 12,
				    11: 1,
				}

				__A      =				SegmentTree(test_array, min)
				__A      =				SegmentTree(test_array, max)
				__A      =				SegmentTree(test_array, lambda a, b: a + b)




				def      __A					(     ):

									'''simple docstring'''







									for i in range(len(_UpperCAmelCase ) ):
														for j in range(_UpperCAmelCase					,			len(_UpperCAmelCase ) ):
																			_A								= reduce(_UpperCAmelCase					,			test_array[i : j + 1] )
																			_A								= reduce(_UpperCAmelCase					,			test_array[i : j + 1] )
																			_A								= reduce(lambda _lowercase					,			_lowercase : a + b					,			test_array[i : j + 1] )
																			assert min_range == min_segment_tree.query(_UpperCAmelCase					,			_UpperCAmelCase )
																			assert max_range == max_segment_tree.query(_UpperCAmelCase					,			_UpperCAmelCase )
																			assert sum_range == sum_segment_tree.query(_UpperCAmelCase					,			_UpperCAmelCase )

				test_all_segments()

				for index, value in test_updates.items():
								__A      =				value
								min_segment_tree.update(index, value)
								max_segment_tree.update(index, value)
								sum_segment_tree.update(index, value)
								test_all_segments()














import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



