





'''simple docstring'''







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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class 					__a      (nn.Module     ):
							__a					:   int
							__a					:   int
							__a					:   float									=       0.0
							__a					:   int									=       1
							__a					:   int									=       1
							__a					:   bool									=       True
							__a					:   bool									=       False
							__a					:   bool									=       False
							__a					:   bool									=       False
							__a					:   jnp.dtype									=       jnp.floataa

							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  []
													UpperCAmelCase_			:      List[Any]     						=  []

													for i in range(self.num_layers   ):
																			UpperCAmelCase_			:      Union[str, Any]     						=  self.in_channels if i == 0 else self.out_channels

																			UpperCAmelCase_			:      List[Any]     						=  FlaxResnetBlockaD(
																			    in_channels=__magic_name__       ,     out_channels=self.out_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
																			resnets.append(__magic_name__   )

																			UpperCAmelCase_			:      List[str]     						=  FlaxTransformeraDModel(
																			    in_channels=self.out_channels       ,     n_heads=self.num_attention_heads       ,     d_head=self.out_channels // self.num_attention_heads       ,     depth=1       ,     use_linear_projection=self.use_linear_projection       ,     only_cross_attention=self.only_cross_attention       ,     use_memory_efficient_attention=self.use_memory_efficient_attention       ,     dtype=self.dtype       ,     )
																			attentions.append(__magic_name__   )

													UpperCAmelCase_			:      int     						=  resnets
													UpperCAmelCase_			:      Optional[Any]     						=  attentions

													if self.add_downsample:
																			UpperCAmelCase_			:      Tuple     						=  FlaxDownsampleaD(self.out_channels       ,     dtype=self.dtype   )



							def __call__(							self							:     Tuple       ,     __magic_name__							:     str       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict=True   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  ()

													for resnet, attn in zip(self.resnets       ,     self.attentions   ):
																			UpperCAmelCase_			:      Union[str, Any]     						=  resnet(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )
																			UpperCAmelCase_			:      Tuple     						=  attn(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )
																			output_states += (hidden_states,)

													if self.add_downsample:
																			UpperCAmelCase_			:      int     						=  self.downsamplers_a(__magic_name__   )
																			output_states += (hidden_states,)

													return hidden_states, output_states





class 					__a      (nn.Module     ):
							__a					:   int
							__a					:   int
							__a					:   float									=       0.0
							__a					:   int									=       1
							__a					:   bool									=       True
							__a					:   jnp.dtype									=       jnp.floataa

							def     UpperCAmelCase__	(							self							:     str   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  []

													for i in range(self.num_layers   ):
																			UpperCAmelCase_			:      List[Any]     						=  self.in_channels if i == 0 else self.out_channels

																			UpperCAmelCase_			:      Dict     						=  FlaxResnetBlockaD(
																			    in_channels=__magic_name__       ,     out_channels=self.out_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
																			resnets.append(__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  resnets

													if self.add_downsample:
																			UpperCAmelCase_			:      Union[str, Any]     						=  FlaxDownsampleaD(self.out_channels       ,     dtype=self.dtype   )



							def __call__(							self							:     int       ,     __magic_name__							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[Any]=True   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  ()

													for resnet in self.resnets:
																			UpperCAmelCase_			:      Optional[Any]     						=  resnet(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )
																			output_states += (hidden_states,)

													if self.add_downsample:
																			UpperCAmelCase_			:      Dict     						=  self.downsamplers_a(__magic_name__   )
																			output_states += (hidden_states,)

													return hidden_states, output_states





class 					__a      (nn.Module     ):
							__a					:   int
							__a					:   int
							__a					:   int
							__a					:   float									=       0.0
							__a					:   int									=       1
							__a					:   int									=       1
							__a					:   bool									=       True
							__a					:   bool									=       False
							__a					:   bool									=       False
							__a					:   bool									=       False
							__a					:   jnp.dtype									=       jnp.floataa

							def     UpperCAmelCase__	(							self							:     int   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  []
													UpperCAmelCase_			:      Dict     						=  []

													for i in range(self.num_layers   ):
																			UpperCAmelCase_			:      List[Any]     						=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
																			UpperCAmelCase_			:      str     						=  self.prev_output_channel if i == 0 else self.out_channels

																			UpperCAmelCase_			:      Any     						=  FlaxResnetBlockaD(
																			    in_channels=resnet_in_channels + res_skip_channels       ,     out_channels=self.out_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
																			resnets.append(__magic_name__   )

																			UpperCAmelCase_			:      str     						=  FlaxTransformeraDModel(
																			    in_channels=self.out_channels       ,     n_heads=self.num_attention_heads       ,     d_head=self.out_channels // self.num_attention_heads       ,     depth=1       ,     use_linear_projection=self.use_linear_projection       ,     only_cross_attention=self.only_cross_attention       ,     use_memory_efficient_attention=self.use_memory_efficient_attention       ,     dtype=self.dtype       ,     )
																			attentions.append(__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  resnets
													UpperCAmelCase_			:      Any     						=  attentions

													if self.add_upsample:
																			UpperCAmelCase_			:      Tuple     						=  FlaxUpsampleaD(self.out_channels       ,     dtype=self.dtype   )



							def __call__(							self							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     str       ,     __magic_name__							:     List[Any]=True   )					->       List[Any]:







													"""simple docstring"""
													for resnet, attn in zip(self.resnets       ,     self.attentions   ):
																			# pop res hidden states
																			UpperCAmelCase_			:      List[str]     						=  res_hidden_states_tuple[-1]
																			UpperCAmelCase_			:      int     						=  res_hidden_states_tuple[:-1]
																			UpperCAmelCase_			:      Optional[int]     						=  jnp.concatenate((hidden_states, res_hidden_states)       ,     axis=-1   )

																			UpperCAmelCase_			:      str     						=  resnet(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )
																			UpperCAmelCase_			:      Optional[int]     						=  attn(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )

													if self.add_upsample:
																			UpperCAmelCase_			:      int     						=  self.upsamplers_a(__magic_name__   )

													return hidden_states





class 					__a      (nn.Module     ):
							__a					:   int
							__a					:   int
							__a					:   int
							__a					:   float									=       0.0
							__a					:   int									=       1
							__a					:   bool									=       True
							__a					:   jnp.dtype									=       jnp.floataa

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  []

													for i in range(self.num_layers   ):
																			UpperCAmelCase_			:      int     						=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
																			UpperCAmelCase_			:      Any     						=  self.prev_output_channel if i == 0 else self.out_channels

																			UpperCAmelCase_			:      List[Any]     						=  FlaxResnetBlockaD(
																			    in_channels=resnet_in_channels + res_skip_channels       ,     out_channels=self.out_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
																			resnets.append(__magic_name__   )

													UpperCAmelCase_			:      Optional[int]     						=  resnets

													if self.add_upsample:
																			UpperCAmelCase_			:      int     						=  FlaxUpsampleaD(self.out_channels       ,     dtype=self.dtype   )



							def __call__(							self							:     List[Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     int       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     str=True   )					->       Optional[Any]:







													"""simple docstring"""
													for resnet in self.resnets:
																			# pop res hidden states
																			UpperCAmelCase_			:      Tuple     						=  res_hidden_states_tuple[-1]
																			UpperCAmelCase_			:      Optional[Any]     						=  res_hidden_states_tuple[:-1]
																			UpperCAmelCase_			:      Tuple     						=  jnp.concatenate((hidden_states, res_hidden_states)       ,     axis=-1   )

																			UpperCAmelCase_			:      Dict     						=  resnet(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )

													if self.add_upsample:
																			UpperCAmelCase_			:      Union[str, Any]     						=  self.upsamplers_a(__magic_name__   )

													return hidden_states





class 					__a      (nn.Module     ):
							__a					:   int
							__a					:   float									=       0.0
							__a					:   int									=       1
							__a					:   int									=       1
							__a					:   bool									=       False
							__a					:   bool									=       False
							__a					:   jnp.dtype									=       jnp.floataa

							def     UpperCAmelCase__	(							self							:     str   )					->       Tuple:







													"""simple docstring"""
													# there is always at least one resnet
													UpperCAmelCase_			:      int     						=  [
													    FlaxResnetBlockaD(
													        in_channels=self.in_channels       ,     out_channels=self.in_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
													]

													UpperCAmelCase_			:      List[Any]     						=  []

													for _ in range(self.num_layers   ):
																			UpperCAmelCase_			:      Optional[Any]     						=  FlaxTransformeraDModel(
																			    in_channels=self.in_channels       ,     n_heads=self.num_attention_heads       ,     d_head=self.in_channels // self.num_attention_heads       ,     depth=1       ,     use_linear_projection=self.use_linear_projection       ,     use_memory_efficient_attention=self.use_memory_efficient_attention       ,     dtype=self.dtype       ,     )
																			attentions.append(__magic_name__   )

																			UpperCAmelCase_			:      List[str]     						=  FlaxResnetBlockaD(
																			    in_channels=self.in_channels       ,     out_channels=self.in_channels       ,     dropout_prob=self.dropout       ,     dtype=self.dtype       ,     )
																			resnets.append(__magic_name__   )

													UpperCAmelCase_			:      Any     						=  resnets
													UpperCAmelCase_			:      Optional[int]     						=  attentions



							def __call__(							self							:     str       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     List[Any]=True   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.resnets[0](__magic_name__       ,     __magic_name__   )
													for attn, resnet in zip(self.attentions       ,     self.resnets[1:]   ):
																			UpperCAmelCase_			:      Any     						=  attn(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )
																			UpperCAmelCase_			:      Optional[Any]     						=  resnet(__magic_name__       ,     __magic_name__       ,     deterministic=__magic_name__   )

													return hidden_states












'''simple docstring'''







class 					__a      :

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  size
													UpperCAmelCase_			:      Tuple     						=  [0] * size
													UpperCAmelCase_			:      Optional[Any]     						=  [0] * size

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return index | (index + 1)

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return (index & (index + 1)) - 1

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  value
													while index < self.size:
																			UpperCAmelCase_			:      str     						=  self.get_prev(__magic_name__   ) + 1
																			if current_left_border == index:
																									UpperCAmelCase_			:      List[str]     						=  value
																			else:
																									UpperCAmelCase_			:      Optional[int]     						=  max(__magic_name__       ,     __magic_name__       ,     __magic_name__   )
																			UpperCAmelCase_			:      Tuple     						=  self.get_next(__magic_name__   )



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       int:







													"""simple docstring"""
													right -= 1  # Because of right is exclusive
													UpperCAmelCase_			:      List[str]     						=  0
													while left <= right:
																			UpperCAmelCase_			:      Optional[Any]     						=  self.get_prev(__magic_name__   )
																			if left <= current_left:
																									UpperCAmelCase_			:      Dict     						=  max(__magic_name__       ,     self.tree[right]   )
																									UpperCAmelCase_			:      Optional[Any]     						=  current_left
																			else:
																									UpperCAmelCase_			:      str     						=  max(__magic_name__       ,     self.arr[right]   )
																									right -= 1
													return result


if __name__ == "__main__":
					import doctest

					doctest.testmod()





