"""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      ):
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						float      =     0.0
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						bool      =     True
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = []
							lowerCAmelCase__   = []

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

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

									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(snake_case__  )

							lowerCAmelCase__   = resnets
							lowerCAmelCase__   = attentions

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




					def __call__(						self    :  Any	,    snake_case__    :  List[str]	,    snake_case__    :  str	,    snake_case__    :  Any	,    snake_case__    :  Dict=True  ):
							lowerCAmelCase__   = ()

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

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

							return hidden_states, output_states

class    a_   (  nn.Module      ):
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						float      =     0.0
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						bool      =     True
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = []

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

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

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




					def __call__(						self    :  Optional[Any]	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  List[str]=True  ):
							lowerCAmelCase__   = ()

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

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

							return hidden_states, output_states

class    a_   (  nn.Module      ):
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						float      =     0.0
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						bool      =     True
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							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(snake_case__  )

									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(snake_case__  )

							lowerCAmelCase__   = resnets
							lowerCAmelCase__   = attentions

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




					def __call__(						self    :  str	,    snake_case__    :  str	,    snake_case__    :  Optional[Any]	,    snake_case__    :  List[str]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=True  ):
							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(snake_case__	,    snake_case__	,    deterministic=snake_case__  )
									lowerCAmelCase__   = attn(snake_case__	,    snake_case__	,    deterministic=snake_case__  )

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

							return hidden_states

class    a_   (  nn.Module      ):
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						int
					UpperCamelCase_			:						float      =     0.0
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						bool      =     True
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							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(snake_case__  )

							lowerCAmelCase__   = resnets

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




					def __call__(						self    :  Dict	,    snake_case__    :  Any	,    snake_case__    :  List[str]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=True  ):
							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(snake_case__	,    snake_case__	,    deterministic=snake_case__  )

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

							return hidden_states

class    a_   (  nn.Module      ):
					UpperCamelCase_			:						int
					UpperCamelCase_			:						float      =     0.0
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						int      =     1
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						bool      =     False
					UpperCamelCase_			:						jnp.dtype      =     jnp.floataa




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# there is always at least one resnet
							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(snake_case__  )

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

							lowerCAmelCase__   = resnets
							lowerCAmelCase__   = attentions




					def __call__(						self    :  str	,    snake_case__    :  List[str]	,    snake_case__    :  int	,    snake_case__    :  List[Any]	,    snake_case__    :  Dict=True  ):
							lowerCAmelCase__   = self.resnets[0](snake_case__	,    snake_case__  )
							for attn, resnet in zip(self.attentions	,    self.resnets[1:]  ):
									lowerCAmelCase__   = attn(snake_case__	,    snake_case__	,    deterministic=snake_case__  )
									lowerCAmelCase__   = resnet(snake_case__	,    snake_case__	,    deterministic=snake_case__  )

							return hidden_states





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




