

'''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 		__SCREAMING_SNAKE_CASE       ( nn.Module			):
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						0.0
				lowerCamelCase_							=						1
				lowerCamelCase_							=						1
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						jnp.floataa







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Any								=[]
										lowercase   :       Optional[int]								=[]

										for i in range(self.num_layers      ):
																lowercase   :       Optional[int]								=self.in_channels if i == 0 else self.out_channels

																lowercase   :       Union[str, Any]								=FlaxResnetBlockaD(
																    in_channels=UpperCAmelCase__		,				out_channels=self.out_channels		,				dropout_prob=self.dropout		,				dtype=self.dtype		,				)
																resnets.append(UpperCAmelCase__      )

																lowercase   :       Union[str, Any]								=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(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=resnets
										lowercase   :       List[str]								=attentions

										if self.add_downsample:
																lowercase   :       Optional[Any]								=FlaxDownsampleaD(self.out_channels		,				dtype=self.dtype      )







				def __call__(					self    :		Optional[int]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		str=True      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=()

										for resnet, attn in zip(self.resnets		,				self.attentions      ):
																lowercase   :       Tuple								=resnet(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )
																lowercase   :       List[str]								=attn(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )
																output_states += (hidden_states,)

										if self.add_downsample:
																lowercase   :       Union[str, Any]								=self.downsamplers_a(UpperCAmelCase__      )
																output_states += (hidden_states,)

										return hidden_states, output_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						0.0
				lowerCamelCase_							=						1
				lowerCamelCase_							=						True
				lowerCamelCase_							=						jnp.floataa







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[str]								=[]

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

																lowercase   :       Optional[Any]								=FlaxResnetBlockaD(
																    in_channels=UpperCAmelCase__		,				out_channels=self.out_channels		,				dropout_prob=self.dropout		,				dtype=self.dtype		,				)
																resnets.append(UpperCAmelCase__      )
										lowercase   :       int								=resnets

										if self.add_downsample:
																lowercase   :       Any								=FlaxDownsampleaD(self.out_channels		,				dtype=self.dtype      )







				def __call__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		List[str]=True      ):
										'''simple docstring'''
										lowercase   :       str								=()

										for resnet in self.resnets:
																lowercase   :       str								=resnet(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )
																output_states += (hidden_states,)

										if self.add_downsample:
																lowercase   :       str								=self.downsamplers_a(UpperCAmelCase__      )
																output_states += (hidden_states,)

										return hidden_states, output_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						0.0
				lowerCamelCase_							=						1
				lowerCamelCase_							=						1
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						jnp.floataa







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       str								=[]
										lowercase   :       Union[str, Any]								=[]

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

																lowercase   :       Union[str, Any]								=FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels		,				out_channels=self.out_channels		,				dropout_prob=self.dropout		,				dtype=self.dtype		,				)
																resnets.append(UpperCAmelCase__      )

																lowercase   :       Union[str, Any]								=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(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=resnets
										lowercase   :       Tuple								=attentions

										if self.add_upsample:
																lowercase   :       List[Any]								=FlaxUpsampleaD(self.out_channels		,				dtype=self.dtype      )







				def __call__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]=True      ):
										'''simple docstring'''
										for resnet, attn in zip(self.resnets		,				self.attentions      ):
																# pop res hidden states
																lowercase   :       str								=res_hidden_states_tuple[-1]
																lowercase   :       int								=res_hidden_states_tuple[:-1]
																lowercase   :       Optional[Any]								=jnp.concatenate((hidden_states, res_hidden_states)		,				axis=-1      )

																lowercase   :       Dict								=resnet(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )
																lowercase   :       List[Any]								=attn(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )

										if self.add_upsample:
																lowercase   :       str								=self.upsamplers_a(UpperCAmelCase__      )

										return hidden_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						42
				lowerCamelCase_							=						0.0
				lowerCamelCase_							=						1
				lowerCamelCase_							=						True
				lowerCamelCase_							=						jnp.floataa







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Dict								=[]

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

																lowercase   :       str								=FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels		,				out_channels=self.out_channels		,				dropout_prob=self.dropout		,				dtype=self.dtype		,				)
																resnets.append(UpperCAmelCase__      )

										lowercase   :       List[Any]								=resnets

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







				def __call__(					self    :		Optional[int]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		str=True      ):
										'''simple docstring'''
										for resnet in self.resnets:
																# pop res hidden states
																lowercase   :       Tuple								=res_hidden_states_tuple[-1]
																lowercase   :       Optional[int]								=res_hidden_states_tuple[:-1]
																lowercase   :       str								=jnp.concatenate((hidden_states, res_hidden_states)		,				axis=-1      )

																lowercase   :       List[Any]								=resnet(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )

										if self.add_upsample:
																lowercase   :       Tuple								=self.upsamplers_a(UpperCAmelCase__      )

										return hidden_states





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):
				lowerCamelCase_							=						42
				lowerCamelCase_							=						0.0
				lowerCamelCase_							=						1
				lowerCamelCase_							=						1
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						jnp.floataa







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										# there is always at least one resnet
										lowercase   :       Optional[int]								=[
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels		,				out_channels=self.in_channels		,				dropout_prob=self.dropout		,				dtype=self.dtype		,				)
										]

										lowercase   :       List[Any]								=[]

										for _ in range(self.num_layers      ):
																lowercase   :       Dict								=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(UpperCAmelCase__      )

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

										lowercase   :       Tuple								=resnets
										lowercase   :       Dict								=attentions







				def __call__(					self    :		List[str]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		int=True      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.resnets[0](UpperCAmelCase__		,				UpperCAmelCase__      )
										for attn, resnet in zip(self.attentions		,				self.resnets[1:]      ):
																lowercase   :       Any								=attn(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )
																lowercase   :       Tuple								=resnet(UpperCAmelCase__		,				UpperCAmelCase__		,				deterministic=UpperCAmelCase__      )

										return hidden_states








'''simple docstring'''





import math






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * power_factor






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * math.sqrt(1 - power_factor**2	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





