

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

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



class 							_UpperCAmelCase			( nn.Module      ):
  '''simple docstring'''







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







  def 						UpperCamelCase							(			self						:			List[str]			):
        A 			=					[]
        A 			=					[]

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

              A 			=					FlaxResnetBlockaD(
                  in_channels=a__		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
              resnets.append(a__			)

              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(a__			)

        A 			=					resnets
        A 			=					attentions

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







  def __call__(			self						:			List[str]		,    UpperCamelCase__						:			List[str]		,    UpperCamelCase__						:			Optional[Any]		,    UpperCamelCase__						:			str		,    UpperCamelCase__						:			str=True			):
        A 			=					()

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

        if self.add_downsample:
              A 			=					self.downsamplers_a(a__			)
              output_states += (hidden_states,)

        return hidden_states, output_states



class 							_UpperCAmelCase			( nn.Module      ):
  '''simple docstring'''







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







  def 						UpperCamelCase							(			self						:			Union[str, Any]			):
        A 			=					[]

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

              A 			=					FlaxResnetBlockaD(
                  in_channels=a__		,    out_channels=self.out_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
              resnets.append(a__			)
        A 			=					resnets

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







  def __call__(			self						:			List[Any]		,    UpperCamelCase__						:			Optional[int]		,    UpperCamelCase__						:			Union[str, Any]		,    UpperCamelCase__						:			List[Any]=True			):
        A 			=					()

        for resnet in self.resnets:
              A 			=					resnet(a__		,    a__		,    deterministic=a__			)
              output_states += (hidden_states,)

        if self.add_downsample:
              A 			=					self.downsamplers_a(a__			)
              output_states += (hidden_states,)

        return hidden_states, output_states



class 							_UpperCAmelCase			( nn.Module      ):
  '''simple docstring'''







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







  def 						UpperCamelCase							(			self						:			Optional[int]			):
        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(a__			)

              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(a__			)

        A 			=					resnets
        A 			=					attentions

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







  def __call__(			self						:			Union[str, Any]		,    UpperCamelCase__						:			Dict		,    UpperCamelCase__						:			Any		,    UpperCamelCase__						:			Tuple		,    UpperCamelCase__						:			str		,    UpperCamelCase__						:			int=True			):

        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(a__		,    a__		,    deterministic=a__			)
              A 			=					attn(a__		,    a__		,    deterministic=a__			)

        if self.add_upsample:
              A 			=					self.upsamplers_a(a__			)

        return hidden_states



class 							_UpperCAmelCase			( nn.Module      ):
  '''simple docstring'''







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







  def 						UpperCamelCase							(			self						:			Optional[Any]			):
        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(a__			)

        A 			=					resnets

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







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

        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(a__		,    a__		,    deterministic=a__			)

        if self.add_upsample:
              A 			=					self.upsamplers_a(a__			)

        return hidden_states



class 							_UpperCAmelCase			( nn.Module      ):
  '''simple docstring'''







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







  def 						UpperCamelCase							(			self						:			int			):
        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(a__			)

              A 			=					FlaxResnetBlockaD(
                  in_channels=self.in_channels		,    out_channels=self.in_channels		,    dropout_prob=self.dropout		,    dtype=self.dtype		,    )
              resnets.append(a__			)

        A 			=					resnets
        A 			=					attentions







  def __call__(			self						:			List[str]		,    UpperCamelCase__						:			Dict		,    UpperCamelCase__						:			Optional[Any]		,    UpperCamelCase__						:			Dict		,    UpperCamelCase__						:			Dict=True			):
        A 			=					self.resnets[0](a__		,    a__			)
        for attn, resnet in zip(self.attentions		,    self.resnets[1:]			):
              A 			=					attn(a__		,    a__		,    deterministic=a__			)
              A 			=					resnet(a__		,    a__		,    deterministic=a__			)

        return hidden_states





'''simple docstring'''

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case									=    logging.get_logger(__name__)  # pylint: disable=invalid-name




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

  # uncondition for scaling
  lowerCAmelCase__     :   Dict     		=	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





