






"""simple docstring"""



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





class 					_UpperCAmelCase       ( nn.Module							):
					SCREAMING_SNAKE_CASE_		:				int
					SCREAMING_SNAKE_CASE_		:				jnp.dtype							    =   jnp.floataa




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






					def __call__(				self     :						str    ,						A     :						List[Any]   )  ->  Dict:
							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Any							       =   hidden_states.shape
							lowercase_ : List[str]							       =   jax.image.resize(
							    A    ,						shape=(batch, height * 2, width * 2, channels)    ,						method='''nearest'''    ,						)
							lowercase_ : Optional[Any]							       =   self.conv(A   )
							return hidden_states





class 					_UpperCAmelCase       ( nn.Module							):
					SCREAMING_SNAKE_CASE_		:				int
					SCREAMING_SNAKE_CASE_		:				jnp.dtype							    =   jnp.floataa




					def   A  (				self     :						str   )  ->  str:
							lowercase_ : Any							       =   nn.Conv(
							    self.out_channels    ,						kernel_size=(3, 3)    ,						strides=(2, 2)    ,						padding=((1, 1), (1, 1))    ,						dtype=self.dtype    ,						)






					def __call__(				self     :						Any    ,						A     :						Tuple   )  ->  Optional[Any]:
							# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
							# hidden_states = jnp.pad(hidden_states, pad_width=pad)
							lowercase_ : Tuple							       =   self.conv(A   )
							return hidden_states





class 					_UpperCAmelCase       ( nn.Module							):
					SCREAMING_SNAKE_CASE_		:				int
					SCREAMING_SNAKE_CASE_		:				int							    =   None
					SCREAMING_SNAKE_CASE_		:				float							    =   0.0
					SCREAMING_SNAKE_CASE_		:				bool							    =   None
					SCREAMING_SNAKE_CASE_		:				jnp.dtype							    =   jnp.floataa




					def   A  (				self     :						int   )  ->  str:
							lowercase_ : Optional[Any]							       =   self.in_channels if self.out_channels is None else self.out_channels

							lowercase_ : Union[str, Any]							       =   nn.GroupNorm(num_groups=32    ,						epsilon=1e-5   )
							lowercase_ : List[str]							       =   nn.Conv(
							    A    ,						kernel_size=(3, 3)    ,						strides=(1, 1)    ,						padding=((1, 1), (1, 1))    ,						dtype=self.dtype    ,						)

							lowercase_ : int							       =   nn.Dense(A    ,						dtype=self.dtype   )

							lowercase_ : Optional[int]							       =   nn.GroupNorm(num_groups=32    ,						epsilon=1e-5   )
							lowercase_ : str							       =   nn.Dropout(self.dropout_prob   )
							lowercase_ : Any							       =   nn.Conv(
							    A    ,						kernel_size=(3, 3)    ,						strides=(1, 1)    ,						padding=((1, 1), (1, 1))    ,						dtype=self.dtype    ,						)

							lowercase_ : int							       =   self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

							lowercase_ : Union[str, Any]							       =   None
							if use_nin_shortcut:
									lowercase_ : Union[str, Any]							       =   nn.Conv(
									    A    ,						kernel_size=(1, 1)    ,						strides=(1, 1)    ,						padding='''VALID'''    ,						dtype=self.dtype    ,						)






					def __call__(				self     :						Optional[int]    ,						A     :						str    ,						A     :						List[str]    ,						A     :						str=True   )  ->  Optional[Any]:
							lowercase_ : List[str]							       =   hidden_states
							lowercase_ : Dict							       =   self.norma(A   )
							lowercase_ : List[str]							       =   nn.swish(A   )
							lowercase_ : Dict							       =   self.conva(A   )

							lowercase_ : Optional[int]							       =   self.time_emb_proj(nn.swish(A   )   )
							lowercase_ : Optional[int]							       =   jnp.expand_dims(jnp.expand_dims(A    ,						1   )    ,						1   )
							lowercase_ : List[str]							       =   hidden_states + temb

							lowercase_ : List[str]							       =   self.norma(A   )
							lowercase_ : int							       =   nn.swish(A   )
							lowercase_ : Tuple							       =   self.dropout(A    ,						A   )
							lowercase_ : List[Any]							       =   self.conva(A   )

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

							return hidden_states + residual










"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


