from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)


@flax.struct.dataclass
class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						jnp.ndarray


@flax_register_to_config
class      lowerCamelCase_					(       nn.Module						,     UpperCAmelCase_						,     UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						int      =				3_2
    a__     :						int      =				4
    a__     :						int      =				4
    a__     :						Tuple[str]      =				(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    a__     :						Tuple[str]      =				("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    a__     :						Union[bool, Tuple[bool]]      =				False
    a__     :						Tuple[int]      =				(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
    a__     :						int      =				2
    a__     :						Union[int, Tuple[int]]      =				8
    a__     :						Optional[Union[int, Tuple[int]]]      =				None
    a__     :						int      =				1_2_8_0
    a__     :						float      =				0.0
    a__     :						bool      =				False
    a__     :						jnp.dtype      =				jnp.floataa
    a__     :						bool      =				True
    a__     :						int      =				0
    a__     :						bool      =				False







    def       UpperCamelCase__       (     self			,			__lowercase)	->     FrozenDict:
        # init input tensors
        __UpperCamelCase					:int        =		(1, self.in_channels, self.sample_size, self.sample_size)
        __UpperCamelCase					:Tuple        =		jnp.zeros(__lowercase			,			dtype=jnp.floataa)
        __UpperCamelCase					:int        =		jnp.ones((1,)			,			dtype=jnp.intaa)
        __UpperCamelCase					:Tuple        =		jnp.zeros((1, 1, self.cross_attention_dim)			,			dtype=jnp.floataa)

        __UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		jax.random.split(__lowercase)
        __UpperCamelCase					:List[str]        =		{'''params''': params_rng, '''dropout''': dropout_rng}

        return self.init(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)["params"]







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:List[str]        =		self.block_out_channels
        __UpperCamelCase					:Dict        =		block_out_channels[0] * 4

        if self.num_attention_heads is not None:
            raise ValueError(
                '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''')

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        __UpperCamelCase					:str        =		self.num_attention_heads or self.attention_head_dim

        # input
        __UpperCamelCase					:int        =		nn.Conv(
            block_out_channels[0]			,			kernel_size=(3, 3)			,			strides=(1, 1)			,			padding=((1, 1), (1, 1))			,			dtype=self.dtype			,			)

        # time
        __UpperCamelCase					:Union[str, Any]        =		FlaxTimesteps(
            block_out_channels[0]			,			flip_sin_to_cos=self.flip_sin_to_cos			,			freq_shift=self.config.freq_shift)
        __UpperCamelCase					:List[str]        =		FlaxTimestepEmbedding(__lowercase			,			dtype=self.dtype)

        __UpperCamelCase					:str        =		self.only_cross_attention
        if isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Tuple        =		(only_cross_attention,) * len(self.down_block_types)

        if isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Any        =		(num_attention_heads,) * len(self.down_block_types)

        # down
        __UpperCamelCase					:List[Any]        =		[]
        __UpperCamelCase					:Dict        =		block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types):
            __UpperCamelCase					:Dict        =		output_channel
            __UpperCamelCase					:int        =		block_out_channels[i]
            __UpperCamelCase					:List[Any]        =		i == len(__lowercase) - 1

            if down_block_type == "CrossAttnDownBlock2D":
                __UpperCamelCase					:Tuple        =		FlaxCrossAttnDownBlockaD(
                    in_channels=__lowercase			,			out_channels=__lowercase			,			dropout=self.dropout			,			num_layers=self.layers_per_block			,			num_attention_heads=num_attention_heads[i]			,			add_downsample=not is_final_block			,			use_linear_projection=self.use_linear_projection			,			only_cross_attention=only_cross_attention[i]			,			use_memory_efficient_attention=self.use_memory_efficient_attention			,			dtype=self.dtype			,			)
            else:
                __UpperCamelCase					:List[str]        =		FlaxDownBlockaD(
                    in_channels=__lowercase			,			out_channels=__lowercase			,			dropout=self.dropout			,			num_layers=self.layers_per_block			,			add_downsample=not is_final_block			,			dtype=self.dtype			,			)

            down_blocks.append(__lowercase)
        __UpperCamelCase					:List[str]        =		down_blocks

        # mid
        __UpperCamelCase					:str        =		FlaxUNetMidBlockaDCrossAttn(
            in_channels=block_out_channels[-1]			,			dropout=self.dropout			,			num_attention_heads=num_attention_heads[-1]			,			use_linear_projection=self.use_linear_projection			,			use_memory_efficient_attention=self.use_memory_efficient_attention			,			dtype=self.dtype			,			)

        # up
        __UpperCamelCase					:Tuple        =		[]
        __UpperCamelCase					:List[Any]        =		list(reversed(__lowercase))
        __UpperCamelCase					:int        =		list(reversed(__lowercase))
        __UpperCamelCase					:Tuple        =		list(reversed(__lowercase))
        __UpperCamelCase					:List[Any]        =		reversed_block_out_channels[0]
        for i, up_block_type in enumerate(self.up_block_types):
            __UpperCamelCase					:Any        =		output_channel
            __UpperCamelCase					:Any        =		reversed_block_out_channels[i]
            __UpperCamelCase					:Tuple        =		reversed_block_out_channels[min(i + 1			,			len(__lowercase) - 1)]

            __UpperCamelCase					:Any        =		i == len(__lowercase) - 1

            if up_block_type == "CrossAttnUpBlock2D":
                __UpperCamelCase					:Tuple        =		FlaxCrossAttnUpBlockaD(
                    in_channels=__lowercase			,			out_channels=__lowercase			,			prev_output_channel=__lowercase			,			num_layers=self.layers_per_block + 1			,			num_attention_heads=reversed_num_attention_heads[i]			,			add_upsample=not is_final_block			,			dropout=self.dropout			,			use_linear_projection=self.use_linear_projection			,			only_cross_attention=only_cross_attention[i]			,			use_memory_efficient_attention=self.use_memory_efficient_attention			,			dtype=self.dtype			,			)
            else:
                __UpperCamelCase					:int        =		FlaxUpBlockaD(
                    in_channels=__lowercase			,			out_channels=__lowercase			,			prev_output_channel=__lowercase			,			num_layers=self.layers_per_block + 1			,			add_upsample=not is_final_block			,			dropout=self.dropout			,			dtype=self.dtype			,			)

            up_blocks.append(__lowercase)
            __UpperCamelCase					:Optional[int]        =		output_channel
        __UpperCamelCase					:Optional[int]        =		up_blocks

        # out
        __UpperCamelCase					:str        =		nn.GroupNorm(num_groups=32			,			epsilon=1E-5)
        __UpperCamelCase					:Tuple        =		nn.Conv(
            self.out_channels			,			kernel_size=(3, 3)			,			strides=(1, 1)			,			padding=((1, 1), (1, 1))			,			dtype=self.dtype			,			)







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase=None			,			__lowercase=None			,			__lowercase = True			,			__lowercase = False			,			)	->     Union[FlaxUNetaDConditionOutput, Tuple]:
        # 1. time
        if not isinstance(__lowercase			,			jnp.ndarray):
            __UpperCamelCase					:List[Any]        =		jnp.array([timesteps]			,			dtype=jnp.intaa)
        elif isinstance(__lowercase			,			jnp.ndarray) and len(timesteps.shape) == 0:
            __UpperCamelCase					:List[str]        =		timesteps.astype(dtype=jnp.floataa)
            __UpperCamelCase					:Any        =		jnp.expand_dims(__lowercase			,			0)

        __UpperCamelCase					:Dict        =		self.time_proj(__lowercase)
        __UpperCamelCase					:List[str]        =		self.time_embedding(__lowercase)

        # 2. pre-process
        __UpperCamelCase					:Optional[Any]        =		jnp.transpose(__lowercase			,			(0, 2, 3, 1))
        __UpperCamelCase					:Optional[int]        =		self.conv_in(__lowercase)

        # 3. down
        __UpperCamelCase					:int        =		(sample,)
        for down_block in self.down_blocks:
            if isinstance(__lowercase			,			__lowercase):
                __UpperCamelCase  ,	__UpperCamelCase					:str        =		down_block(__lowercase			,			__lowercase			,			__lowercase			,			deterministic=not train)
            else:
                __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		down_block(__lowercase			,			__lowercase			,			deterministic=not train)
            down_block_res_samples += res_samples

        if down_block_additional_residuals is not None:
            __UpperCamelCase					:Optional[int]        =		()

            for down_block_res_sample, down_block_additional_residual in zip(
                __lowercase			,			__lowercase):
                down_block_res_sample += down_block_additional_residual
                new_down_block_res_samples += (down_block_res_sample,)

            __UpperCamelCase					:int        =		new_down_block_res_samples

        # 4. mid
        __UpperCamelCase					:List[str]        =		self.mid_block(__lowercase			,			__lowercase			,			__lowercase			,			deterministic=not train)

        if mid_block_additional_residual is not None:
            sample += mid_block_additional_residual

        # 5. up
        for up_block in self.up_blocks:
            __UpperCamelCase					:Optional[int]        =		down_block_res_samples[-(self.layers_per_block + 1) :]
            __UpperCamelCase					:Union[str, Any]        =		down_block_res_samples[: -(self.layers_per_block + 1)]
            if isinstance(__lowercase			,			__lowercase):
                __UpperCamelCase					:List[Any]        =		up_block(
                    __lowercase			,			temb=__lowercase			,			encoder_hidden_states=__lowercase			,			res_hidden_states_tuple=__lowercase			,			deterministic=not train			,			)
            else:
                __UpperCamelCase					:List[str]        =		up_block(__lowercase			,			temb=__lowercase			,			res_hidden_states_tuple=__lowercase			,			deterministic=not train)

        # 6. post-process
        __UpperCamelCase					:int        =		self.conv_norm_out(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		nn.silu(__lowercase)
        __UpperCamelCase					:Tuple        =		self.conv_out(__lowercase)
        __UpperCamelCase					:Optional[int]        =		jnp.transpose(__lowercase			,			(0, 3, 1, 2))

        if not return_dict:
            return (sample,)

        return FlaxUNetaDConditionOutput(sample=__lowercase)






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''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()





