




'''simple docstring'''

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






class 						a				(   nn.Module     ):
				snake_case_      =		42
				snake_case_      =		jnp.floataa


				def   A_ (		self    :					int					):
									snake_case_									=				nn.Conv(
									    self.out_channels       ,				kernel_size=(3, 3)       ,				strides=(1, 1)       ,				padding=((1, 1), (1, 1))       ,				dtype=self.dtype       ,				)


				def __call__(		self    :					Optional[int]       ,				lowercase_    :					Tuple					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				hidden_states.shape
									snake_case_									=				jax.image.resize(
									    lowercase_       ,				shape=(batch, height * 2, width * 2, channels)       ,				method='''nearest'''       ,				)
									snake_case_									=				self.conv(lowercase_					)
									return hidden_states







class 						a				(   nn.Module     ):
				snake_case_      =		42
				snake_case_      =		jnp.floataa


				def   A_ (		self    :					str					):
									snake_case_									=				nn.Conv(
									    self.out_channels       ,				kernel_size=(3, 3)       ,				strides=(2, 2)       ,				padding=((1, 1), (1, 1))       ,				dtype=self.dtype       ,				)


				def __call__(		self    :					List[Any]       ,				lowercase_    :					List[Any]					):
									# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
									# hidden_states = jnp.pad(hidden_states, pad_width=pad)
									snake_case_									=				self.conv(lowercase_					)
									return hidden_states







class 						a				(   nn.Module     ):
				snake_case_      =		42
				snake_case_      =		None
				snake_case_      =		0.0
				snake_case_      =		None
				snake_case_      =		jnp.floataa


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				self.in_channels if self.out_channels is None else self.out_channels

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

									snake_case_									=				nn.Dense(lowercase_       ,				dtype=self.dtype					)

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

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

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


				def __call__(		self    :					Any       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Any=True					):
									snake_case_									=				hidden_states
									snake_case_									=				self.norma(lowercase_					)
									snake_case_									=				nn.swish(lowercase_					)
									snake_case_									=				self.conva(lowercase_					)

									snake_case_									=				self.time_emb_proj(nn.swish(lowercase_					)					)
									snake_case_									=				jnp.expand_dims(jnp.expand_dims(lowercase_       ,				1					)       ,				1					)
									snake_case_									=				hidden_states + temb

									snake_case_									=				self.norma(lowercase_					)
									snake_case_									=				nn.swish(lowercase_					)
									snake_case_									=				self.dropout(lowercase_       ,				lowercase_					)
									snake_case_									=				self.conva(lowercase_					)

									if self.conv_shortcut is not None:
														snake_case_									=				self.conv_shortcut(lowercase_					)

									return hidden_states + residual







'''simple docstring'''

from __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

