




'''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       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							0.0
							lowercase_							     =							1
							lowercase_							     =							1
							lowercase_							     =							True
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							jnp.floataa







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				str:


														"""simple docstring"""


														_a      	=							[]
														_a      	=							[]

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

																					_a      	=							FlaxResnetBlockaD(
																					    in_channels=lowerCAmelCase_				,     out_channels=self.out_channels				,     dropout_prob=self.dropout				,     dtype=self.dtype				,     )
																					resnets.append(lowerCAmelCase_   )

																					_a      	=							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(lowerCAmelCase_   )

														_a      	=							resnets
														_a      	=							attentions

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







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


														"""simple docstring"""


														_a      	=							()

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

														if self.add_downsample:
																					_a      	=							self.downsamplers_a(lowerCAmelCase_   )
																					output_states += (hidden_states,)

														return hidden_states, output_states



class      A       (						nn.Module       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							0.0
							lowercase_							     =							1
							lowercase_							     =							True
							lowercase_							     =							jnp.floataa







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


														"""simple docstring"""


														_a      	=							[]

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

																					_a      	=							FlaxResnetBlockaD(
																					    in_channels=lowerCAmelCase_				,     out_channels=self.out_channels				,     dropout_prob=self.dropout				,     dtype=self.dtype				,     )
																					resnets.append(lowerCAmelCase_   )
														_a      	=							resnets

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







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


														"""simple docstring"""


														_a      	=							()

														for resnet in self.resnets:
																					_a      	=							resnet(lowerCAmelCase_				,     lowerCAmelCase_				,     deterministic=lowerCAmelCase_   )
																					output_states += (hidden_states,)

														if self.add_downsample:
																					_a      	=							self.downsamplers_a(lowerCAmelCase_   )
																					output_states += (hidden_states,)

														return hidden_states, output_states



class      A       (						nn.Module       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							0.0
							lowercase_							     =							1
							lowercase_							     =							1
							lowercase_							     =							True
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							jnp.floataa







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				str:


														"""simple docstring"""


														_a      	=							[]
														_a      	=							[]

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

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

																					_a      	=							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(lowerCAmelCase_   )

														_a      	=							resnets
														_a      	=							attentions

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







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


														"""simple docstring"""


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

																					_a      	=							resnet(lowerCAmelCase_				,     lowerCAmelCase_				,     deterministic=lowerCAmelCase_   )
																					_a      	=							attn(lowerCAmelCase_				,     lowerCAmelCase_				,     deterministic=lowerCAmelCase_   )

														if self.add_upsample:
																					_a      	=							self.upsamplers_a(lowerCAmelCase_   )

														return hidden_states



class      A       (						nn.Module       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							0.0
							lowercase_							     =							1
							lowercase_							     =							True
							lowercase_							     =							jnp.floataa







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														_a      	=							[]

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

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

														_a      	=							resnets

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







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


														"""simple docstring"""


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

																					_a      	=							resnet(lowerCAmelCase_				,     lowerCAmelCase_				,     deterministic=lowerCAmelCase_   )

														if self.add_upsample:
																					_a      	=							self.upsamplers_a(lowerCAmelCase_   )

														return hidden_states



class      A       (						nn.Module       ):
							lowercase_							     =							42
							lowercase_							     =							0.0
							lowercase_							     =							1
							lowercase_							     =							1
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							jnp.floataa







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


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

														_a      	=							[]

														for _ in range(self.num_layers   ):
																					_a      	=							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(lowerCAmelCase_   )

																					_a      	=							FlaxResnetBlockaD(
																					    in_channels=self.in_channels				,     out_channels=self.in_channels				,     dropout_prob=self.dropout				,     dtype=self.dtype				,     )
																					resnets.append(lowerCAmelCase_   )

														_a      	=							resnets
														_a      	=							attentions







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


														"""simple docstring"""


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

														return hidden_states








'''simple docstring'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()