


'''simple docstring'''






def lowerCamelCase__      (						__lowerCamelCase							):







   '''simple docstring'''




   if not all(char in '01' for char in bin_string							):
      raise ValueError('Non-binary value was passed to the function'							)
   if not bin_string:
      raise ValueError('Empty string was passed to the function'							)
   _UpperCAmelCase       :       str							   =''
   while len(A__							) % 3 != 0:
      _UpperCAmelCase       :       Optional[Any]							   ='0' + bin_string
   _UpperCAmelCase       :       List[Any]							   =[
       bin_string[index : index + 3]
       for index in range(len(A__							)							)
       if index % 3 == 0
   ]
   for bin_group in bin_string_in_3_list:
      _UpperCAmelCase       :       List[Any]							   =0
      for index, val in enumerate(A__							):
         oct_val += int(2 ** (2 - index) * int(A__							)							)
      oct_string += str(A__							)
   return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()





'''simple docstring'''






from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42






class  __magic_name__							(		nn.Module    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=(1_6, 3_2, 9_6, 2_5_6)
       UpperCAmelCase										=jnp.floataa






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




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =nn.Conv(
              self.block_out_channels[0]  ,							kernel_size=(3, 3)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

          _UpperCAmelCase       :       Tuple							   =[]
          for i in range(len(self.block_out_channels) - 1):
             _UpperCAmelCase       :       Optional[int]							   =self.block_out_channels[i]
             _UpperCAmelCase       :       List[Any]							   =self.block_out_channels[i + 1]
             _UpperCAmelCase       :       Tuple							   =nn.Conv(
                 snake_case  ,							kernel_size=(3, 3)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)
             blocks.append(snake_case)
             _UpperCAmelCase       :       Optional[int]							   =nn.Conv(
                 snake_case  ,							kernel_size=(3, 3)  ,							strides=(2, 2)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)
             blocks.append(snake_case)
          _UpperCAmelCase       :       Dict							   =blocks

          _UpperCAmelCase       :       Tuple							   =nn.Conv(
              self.conditioning_embedding_channels  ,							kernel_size=(3, 3)  ,							padding=((1, 1), (1, 1))  ,							kernel_init=nn.initializers.zeros_init()  ,							bias_init=nn.initializers.zeros_init()  ,							dtype=self.dtype  ,							)






       def __call__(      self  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =self.conv_in(snake_case)
          _UpperCAmelCase       :       Any							   =nn.silu(snake_case)

          for block in self.blocks:
             _UpperCAmelCase       :       Optional[Any]							   =block(snake_case)
             _UpperCAmelCase       :       Union[str, Any]							   =nn.silu(snake_case)

          _UpperCAmelCase       :       str							   =self.conv_out(snake_case)

          return embedding






@flax_register_to_config
class  __magic_name__							(		nn.Module ,lowerCAmelCase ,lowerCAmelCase    ):
       UpperCAmelCase										=3_2
       UpperCAmelCase										=4
       UpperCAmelCase										=(
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       UpperCAmelCase										=False
       UpperCAmelCase										=(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
       UpperCAmelCase										=2
       UpperCAmelCase										=8
       UpperCAmelCase										=None
       UpperCAmelCase										=1_2_8_0
       UpperCAmelCase										=0.0
       UpperCAmelCase										=False
       UpperCAmelCase										=jnp.floataa
       UpperCAmelCase										=True
       UpperCAmelCase										=0
       UpperCAmelCase										="rgb"
       UpperCAmelCase										=(1_6, 3_2, 9_6, 2_5_6)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  FrozenDict:




          '''simple docstring'''
          # init input tensors
          _UpperCAmelCase       :       Any							   =(1, self.in_channels, self.sample_size, self.sample_size)
          _UpperCAmelCase       :       Optional[Any]							   =jnp.zeros(snake_case  ,							dtype=jnp.floataa)
          _UpperCAmelCase       :       Optional[int]							   =jnp.ones((1,)  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       str							   =jnp.zeros((1, 1, self.cross_attention_dim)  ,							dtype=jnp.floataa)
          _UpperCAmelCase       :       Optional[Any]							   =(1, 3, self.sample_size * 8, self.sample_size * 8)
          _UpperCAmelCase       :       int							   =jnp.zeros(snake_case  ,							dtype=jnp.floataa)

          _UpperCAmelCase   ,	_UpperCAmelCase       :       List[Any]							   =jax.random.split(snake_case)
          _UpperCAmelCase       :       str							   ={'params': params_rng, 'dropout': dropout_rng}

          return self.init(snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)["params"]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.block_out_channels
          _UpperCAmelCase       :       Tuple							   =block_out_channels[0] * 4

          # If `num_attention_heads` is not defined (which is the case for most models)
          # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
          # The reason for this behavior is to correct for incorrectly named variables that were introduced
          # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
          # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
          # which is why we correct for the naming here.
          _UpperCAmelCase       :       Optional[Any]							   =self.num_attention_heads or self.attention_head_dim

          # input
          _UpperCAmelCase       :       Tuple							   =nn.Conv(
              block_out_channels[0]  ,							kernel_size=(3, 3)  ,							strides=(1, 1)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

          # time
          _UpperCAmelCase       :       Union[str, Any]							   =FlaxTimesteps(
              block_out_channels[0]  ,							flip_sin_to_cos=self.flip_sin_to_cos  ,							freq_shift=self.config.freq_shift)
          _UpperCAmelCase       :       str							   =FlaxTimestepEmbedding(snake_case  ,							dtype=self.dtype)

          _UpperCAmelCase       :       Optional[Any]							   =FlaxControlNetConditioningEmbedding(
              conditioning_embedding_channels=block_out_channels[0]  ,							block_out_channels=self.conditioning_embedding_out_channels  ,							)

          _UpperCAmelCase       :       Optional[int]							   =self.only_cross_attention
          if isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       Dict							   =(only_cross_attention,) * len(self.down_block_types)

          if isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       Optional[Any]							   =(num_attention_heads,) * len(self.down_block_types)

          # down
          _UpperCAmelCase       :       int							   =[]
          _UpperCAmelCase       :       Optional[int]							   =[]

          _UpperCAmelCase       :       List[str]							   =block_out_channels[0]

          _UpperCAmelCase       :       int							   =nn.Conv(
              snake_case  ,							kernel_size=(1, 1)  ,							padding='VALID'  ,							kernel_init=nn.initializers.zeros_init()  ,							bias_init=nn.initializers.zeros_init()  ,							dtype=self.dtype  ,							)
          controlnet_down_blocks.append(snake_case)

          for i, down_block_type in enumerate(self.down_block_types):
             _UpperCAmelCase       :       Tuple							   =output_channel
             _UpperCAmelCase       :       Dict							   =block_out_channels[i]
             _UpperCAmelCase       :       str							   =i == len(snake_case) - 1

             if down_block_type == "CrossAttnDownBlock2D":
                _UpperCAmelCase       :       Tuple							   =FlaxCrossAttnDownBlockaD(
                    in_channels=snake_case  ,							out_channels=snake_case  ,							dropout=self.dropout  ,							num_layers=self.layers_per_block  ,							num_attention_heads=num_attention_heads[i]  ,							add_downsample=not is_final_block  ,							use_linear_projection=self.use_linear_projection  ,							only_cross_attention=only_cross_attention[i]  ,							dtype=self.dtype  ,							)
             else:
                _UpperCAmelCase       :       Optional[Any]							   =FlaxDownBlockaD(
                    in_channels=snake_case  ,							out_channels=snake_case  ,							dropout=self.dropout  ,							num_layers=self.layers_per_block  ,							add_downsample=not is_final_block  ,							dtype=self.dtype  ,							)

             down_blocks.append(snake_case)

             for _ in range(self.layers_per_block):
                _UpperCAmelCase       :       Tuple							   =nn.Conv(
                    snake_case  ,							kernel_size=(1, 1)  ,							padding='VALID'  ,							kernel_init=nn.initializers.zeros_init()  ,							bias_init=nn.initializers.zeros_init()  ,							dtype=self.dtype  ,							)
                controlnet_down_blocks.append(snake_case)

             if not is_final_block:
                _UpperCAmelCase       :       List[str]							   =nn.Conv(
                    snake_case  ,							kernel_size=(1, 1)  ,							padding='VALID'  ,							kernel_init=nn.initializers.zeros_init()  ,							bias_init=nn.initializers.zeros_init()  ,							dtype=self.dtype  ,							)
                controlnet_down_blocks.append(snake_case)

          _UpperCAmelCase       :       List[Any]							   =down_blocks
          _UpperCAmelCase       :       Optional[Any]							   =controlnet_down_blocks

          # mid
          _UpperCAmelCase       :       int							   =block_out_channels[-1]
          _UpperCAmelCase       :       Optional[Any]							   =FlaxUNetMidBlockaDCrossAttn(
              in_channels=snake_case  ,							dropout=self.dropout  ,							num_attention_heads=num_attention_heads[-1]  ,							use_linear_projection=self.use_linear_projection  ,							dtype=self.dtype  ,							)

          _UpperCAmelCase       :       Optional[int]							   =nn.Conv(
              snake_case  ,							kernel_size=(1, 1)  ,							padding='VALID'  ,							kernel_init=nn.initializers.zeros_init()  ,							bias_init=nn.initializers.zeros_init()  ,							dtype=self.dtype  ,							)






       def __call__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case = 1.0  ,							snake_case = True  ,							snake_case = False  ,							)						->  Union[FlaxControlNetOutput, Tuple]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =self.controlnet_conditioning_channel_order
          if channel_order == "bgr":
             _UpperCAmelCase       :       Optional[int]							   =jnp.flip(snake_case  ,							axis=1)

          # 1. time
          if not isinstance(snake_case  ,							jnp.ndarray):
             _UpperCAmelCase       :       Optional[int]							   =jnp.array([timesteps]  ,							dtype=jnp.intaa)
          elif isinstance(snake_case  ,							jnp.ndarray) and len(timesteps.shape) == 0:
             _UpperCAmelCase       :       str							   =timesteps.astype(dtype=jnp.floataa)
             _UpperCAmelCase       :       Dict							   =jnp.expand_dims(snake_case  ,							0)

          _UpperCAmelCase       :       int							   =self.time_proj(snake_case)
          _UpperCAmelCase       :       Any							   =self.time_embedding(snake_case)

          # 2. pre-process
          _UpperCAmelCase       :       str							   =jnp.transpose(snake_case  ,							(0, 2, 3, 1))
          _UpperCAmelCase       :       Any							   =self.conv_in(snake_case)

          _UpperCAmelCase       :       List[str]							   =jnp.transpose(snake_case  ,							(0, 2, 3, 1))
          _UpperCAmelCase       :       Optional[int]							   =self.controlnet_cond_embedding(snake_case)
          sample += controlnet_cond

          # 3. down
          _UpperCAmelCase       :       Tuple							   =(sample,)
          for down_block in self.down_blocks:
             if isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =down_block(snake_case  ,							snake_case  ,							snake_case  ,							deterministic=not train)
             else:
                _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =down_block(snake_case  ,							snake_case  ,							deterministic=not train)
             down_block_res_samples += res_samples

          # 4. mid
          _UpperCAmelCase       :       List[Any]							   =self.mid_block(snake_case  ,							snake_case  ,							snake_case  ,							deterministic=not train)

          # 5. contronet blocks
          _UpperCAmelCase       :       Union[str, Any]							   =()
          for down_block_res_sample, controlnet_block in zip(snake_case  ,							self.controlnet_down_blocks):
             _UpperCAmelCase       :       List[str]							   =controlnet_block(snake_case)
             controlnet_down_block_res_samples += (down_block_res_sample,)

          _UpperCAmelCase       :       Optional[int]							   =controlnet_down_block_res_samples

          _UpperCAmelCase       :       List[str]							   =self.controlnet_mid_block(snake_case)

          # 6. scaling
          _UpperCAmelCase       :       Tuple							   =[sample * conditioning_scale for sample in down_block_res_samples]
          mid_block_res_sample *= conditioning_scale

          if not return_dict:
             return (down_block_res_samples, mid_block_res_sample)

          return FlaxControlNetOutput(
              down_block_res_samples=snake_case  ,							mid_block_res_sample=snake_case)

