






from collections import namedtuple

a_							=    namedtuple('''from_to''', '''from_ to''')

a_							=    {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.001, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.00454, 264.172),
    '''cubicyard''': from_to(0.76455, 1.30795),
    '''cubicfoot''': from_to(0.028, 35.3147),
    '''cup''': from_to(0.000236588, 4226.75),
}


def        _a  (     UpperCamelCase_					:				float    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				str   ) ->   float:
      """simple docstring"""







      if from_type not in METRIC_CONVERSION:
            raise ValueError(
                F"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
                + ", ".join(UpperCamelCase_   )   )
      if to_type not in METRIC_CONVERSION:
            raise ValueError(
                F"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
                + ", ".join(UpperCamelCase_   )   )
      return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
 import doctest

 doctest.testmod()














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

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


class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =42
	a_         =0.0
	a_         =1
	a_         =1
	a_         =True
	a_         =False
	a_         =False
	a_         =False
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						[]
							lowerCAmelCase__       =						[]

							for i in range(self.num_layers       ):
													lowerCAmelCase__       =						self.in_channels if i == 0 else self.out_channels

													lowerCAmelCase__       =						FlaxResnetBlockaD(
													    in_channels=__UpperCAmelCase		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
													resnets.append(__UpperCAmelCase       )

													lowerCAmelCase__       =						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       )

							lowerCAmelCase__       =						resnets
							lowerCAmelCase__       =						attentions

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


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=True       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						()

							for resnet, attn in zip(self.resnets		,    self.attentions       ):
													lowerCAmelCase__       =						resnet(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )
													lowerCAmelCase__       =						attn(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )
													output_states += (hidden_states,)

							if self.add_downsample:
													lowerCAmelCase__       =						self.downsamplers_a(__UpperCAmelCase       )
													output_states += (hidden_states,)

							return hidden_states, output_states





class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =42
	a_         =0.0
	a_         =1
	a_         =True
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						[]

							for i in range(self.num_layers       ):
													lowerCAmelCase__       =						self.in_channels if i == 0 else self.out_channels

													lowerCAmelCase__       =						FlaxResnetBlockaD(
													    in_channels=__UpperCAmelCase		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
													resnets.append(__UpperCAmelCase       )
							lowerCAmelCase__       =						resnets

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


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=True       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						()

							for resnet in self.resnets:
													lowerCAmelCase__       =						resnet(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )
													output_states += (hidden_states,)

							if self.add_downsample:
													lowerCAmelCase__       =						self.downsamplers_a(__UpperCAmelCase       )
													output_states += (hidden_states,)

							return hidden_states, output_states





class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =42
	a_         =42
	a_         =0.0
	a_         =1
	a_         =1
	a_         =True
	a_         =False
	a_         =False
	a_         =False
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						[]
							lowerCAmelCase__       =						[]

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

													lowerCAmelCase__       =						FlaxResnetBlockaD(
													    in_channels=resnet_in_channels + res_skip_channels		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
													resnets.append(__UpperCAmelCase       )

													lowerCAmelCase__       =						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       )

							lowerCAmelCase__       =						resnets
							lowerCAmelCase__       =						attentions

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


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=True       )->					Dict:







							'''simple docstring'''





							for resnet, attn in zip(self.resnets		,    self.attentions       ):
													# pop res hidden states
													lowerCAmelCase__       =						res_hidden_states_tuple[-1]
													lowerCAmelCase__       =						res_hidden_states_tuple[:-1]
													lowerCAmelCase__       =						jnp.concatenate((hidden_states, res_hidden_states)		,    axis=-1       )

													lowerCAmelCase__       =						resnet(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )
													lowerCAmelCase__       =						attn(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )

							if self.add_upsample:
													lowerCAmelCase__       =						self.upsamplers_a(__UpperCAmelCase       )

							return hidden_states





class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =42
	a_         =42
	a_         =0.0
	a_         =1
	a_         =True
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						[]

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

													lowerCAmelCase__       =						FlaxResnetBlockaD(
													    in_channels=resnet_in_channels + res_skip_channels		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
													resnets.append(__UpperCAmelCase       )

							lowerCAmelCase__       =						resnets

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


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=True       )->					int:







							'''simple docstring'''





							for resnet in self.resnets:
													# pop res hidden states
													lowerCAmelCase__       =						res_hidden_states_tuple[-1]
													lowerCAmelCase__       =						res_hidden_states_tuple[:-1]
													lowerCAmelCase__       =						jnp.concatenate((hidden_states, res_hidden_states)		,    axis=-1       )

													lowerCAmelCase__       =						resnet(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )

							if self.add_upsample:
													lowerCAmelCase__       =						self.upsamplers_a(__UpperCAmelCase       )

							return hidden_states





class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =0.0
	a_         =1
	a_         =1
	a_         =False
	a_         =False
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						[
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels		,    out_channels=self.in_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
							]

							lowerCAmelCase__       =						[]

							for _ in range(self.num_layers       ):
													lowerCAmelCase__       =						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       )

													lowerCAmelCase__       =						FlaxResnetBlockaD(
													    in_channels=self.in_channels		,    out_channels=self.in_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
													resnets.append(__UpperCAmelCase       )

							lowerCAmelCase__       =						resnets
							lowerCAmelCase__       =						attentions


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=True       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.resnets[0](__UpperCAmelCase		,    __UpperCAmelCase       )
							for attn, resnet in zip(self.attentions		,    self.resnets[1:]       ):
													lowerCAmelCase__       =						attn(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )
													lowerCAmelCase__       =						resnet(__UpperCAmelCase		,    __UpperCAmelCase		,    deterministic=__UpperCAmelCase       )

							return hidden_states






