






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

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






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		float					=0.0
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		bool					=True
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		jnp.dtype					=jnp.floataa





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =[]
          _lowercase              =[]

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

             _lowercase              =FlaxResnetBlockaD(
                 in_channels=snake_case,      out_channels=self.out_channels,      dropout_prob=self.dropout,      dtype=self.dtype,      )
             resnets.append(snake_case)

             _lowercase              =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(snake_case)

          _lowercase              =resnets
          _lowercase              =attentions

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





       def __call__(				self	:Optional[Any],      snake_case	:Optional[int],      snake_case	:Optional[Any],      snake_case	:Any,      snake_case	:Optional[int]=True):



          """simple docstring"""

          _lowercase              =()

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

          if self.add_downsample:
             _lowercase              =self.downsamplers_a(snake_case)
             output_states += (hidden_states,)

          return hidden_states, output_states






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		float					=0.0
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		bool					=True
       __lowerCAmelCase    :		jnp.dtype					=jnp.floataa





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =[]

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

             _lowercase              =FlaxResnetBlockaD(
                 in_channels=snake_case,      out_channels=self.out_channels,      dropout_prob=self.dropout,      dtype=self.dtype,      )
             resnets.append(snake_case)
          _lowercase              =resnets

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





       def __call__(				self	:Tuple,      snake_case	:Tuple,      snake_case	:Any,      snake_case	:Union[str, Any]=True):



          """simple docstring"""

          _lowercase              =()

          for resnet in self.resnets:
             _lowercase              =resnet(snake_case,      snake_case,      deterministic=snake_case)
             output_states += (hidden_states,)

          if self.add_downsample:
             _lowercase              =self.downsamplers_a(snake_case)
             output_states += (hidden_states,)

          return hidden_states, output_states






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		float					=0.0
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		bool					=True
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		jnp.dtype					=jnp.floataa





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =[]
          _lowercase              =[]

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

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

             _lowercase              =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(snake_case)

          _lowercase              =resnets
          _lowercase              =attentions

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





       def __call__(				self	:List[str],      snake_case	:str,      snake_case	:Dict,      snake_case	:Tuple,      snake_case	:Tuple,      snake_case	:Dict=True):



          """simple docstring"""

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

             _lowercase              =resnet(snake_case,      snake_case,      deterministic=snake_case)
             _lowercase              =attn(snake_case,      snake_case,      deterministic=snake_case)

          if self.add_upsample:
             _lowercase              =self.upsamplers_a(snake_case)

          return hidden_states






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		float					=0.0
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		bool					=True
       __lowerCAmelCase    :		jnp.dtype					=jnp.floataa





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =[]

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

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

          _lowercase              =resnets

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





       def __call__(				self	:str,      snake_case	:Tuple,      snake_case	:Any,      snake_case	:str,      snake_case	:List[Any]=True):



          """simple docstring"""

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

             _lowercase              =resnet(snake_case,      snake_case,      deterministic=snake_case)

          if self.add_upsample:
             _lowercase              =self.upsamplers_a(snake_case)

          return hidden_states






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       __lowerCAmelCase    :		int
       __lowerCAmelCase    :		float					=0.0
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		int					=1
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		bool					=False
       __lowerCAmelCase    :		jnp.dtype					=jnp.floataa





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

          _lowercase              =[]

          for _ in range(self.num_layers):
             _lowercase              =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(snake_case)

             _lowercase              =FlaxResnetBlockaD(
                 in_channels=self.in_channels,      out_channels=self.in_channels,      dropout_prob=self.dropout,      dtype=self.dtype,      )
             resnets.append(snake_case)

          _lowercase              =resnets
          _lowercase              =attentions





       def __call__(				self	:int,      snake_case	:Optional[int],      snake_case	:Optional[int],      snake_case	:Optional[int],      snake_case	:Tuple=True):



          """simple docstring"""

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

          return hidden_states










# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          if not self.directed:  # For undirected graphs
             # if both source vertex and destination vertex are both present in the
             # adjacency list, add destination vertex to source vertex list of adjacent
             # vertices and add source vertex to destination vertex list of adjacent
             # vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination vertex
             # to source vertex list of adjacent vertices, then create a new vertex with
             # destination vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[source_vertex]
             # if only destination vertex is present in adjacency list, add source vertex
             # to destination vertex list of adjacent vertices, then create a new vertex
             # with source vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(snake_case)
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and assign a list
             # containing the destination vertex as it's first adjacent vertex also
             # create a new vertex with destination vertex as key and assign a list
             # containing the source vertex as it's first adjacent vertex.
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[source_vertex]
          else:  # For directed graphs
             # if both source vertex and destination vertex are present in adjacency
             # list, add destination vertex to source vertex list of adjacent vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination
             # vertex to source vertex list of adjacent vertices and create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[]
             # if only destination vertex is present in adjacency list, create a new
             # vertex with source vertex as key and assign a list containing destination
             # vertex as first adjacent vertex
             elif destination_vertex in self.adj_list:
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and a list containing
             # destination vertex as it's first adjacent vertex. Then create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[]

          return self





       def __repr__(				self	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


