


'''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    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		0.0
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		jnp.floataa
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							[]
      _a      =							[]

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

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

           _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(__magic_name__  )

      _a      =							resnets
      _a      =							attentions

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




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

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

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

      return hidden_states, output_states





class 	a       (       nn.Module    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		0.0
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		jnp.floataa
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							[]

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

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

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




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

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

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

      return hidden_states, output_states





class 	a       (       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 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _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(__magic_name__  )

           _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(__magic_name__  )

      _a      =							resnets
      _a      =							attentions

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




 def __call__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=True  )   ->							List[str]:
      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(__magic_name__ ,			__magic_name__ ,			deterministic=__magic_name__  )
           _a      =							attn(__magic_name__ ,			__magic_name__ ,			deterministic=__magic_name__  )

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

      return hidden_states





class 	a       (       nn.Module    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		0.0
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		jnp.floataa
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _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(__magic_name__  )

      _a      =							resnets

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




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

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

      return hidden_states





class 	a       (       nn.Module    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		0.0
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		1
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		jnp.floataa
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # there is always at least one resnet
      _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(__magic_name__  )

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

      _a      =							resnets
      _a      =							attentions




 def __call__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=True  )   ->							Dict:
      _a      =							self.resnets[0](__magic_name__ ,			__magic_name__  )
      for attn, resnet in zip(self.attentions ,			self.resnets[1:]  ):
           _a      =							attn(__magic_name__ ,			__magic_name__ ,			deterministic=__magic_name__  )
           _a      =							resnet(__magic_name__ ,			__magic_name__ ,			deterministic=__magic_name__  )

      return hidden_states





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # If this activity has start time greater than
          # or equal to the finish time of previously
          # selected activity, then select it
          if start[j] >= finish[i]:
               print(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

