'''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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   42







class lowercase_       (		nn.Module			):







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   (1_6, 3_2, 9_6, 2_5_6)
   __lowerCAmelCase         =   jnp.floataa
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        _A    		=  nn.Conv(
            self.block_out_channels[0], kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )

        _A    		=  []
        for i in range(len(self.block_out_channels     ) - 1     ):
             _A    		=  self.block_out_channels[i]
             _A    		=  self.block_out_channels[i + 1]
             _A    		=  nn.Conv(
                 UpperCamelCase__, kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )
             blocks.append(UpperCamelCase__     )
             _A    		=  nn.Conv(
                 UpperCamelCase__, kernel_size=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)), dtype=self.dtype, )
             blocks.append(UpperCamelCase__     )
        _A    		=  blocks

        _A    		=  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      :			Optional[int], UpperCamelCase__      :			Union[str, Any]     )  ->			str:
        _A    		=  self.conv_in(UpperCamelCase__     )
        _A    		=  nn.silu(UpperCamelCase__     )

        for block in self.blocks:
             _A    		=  block(UpperCamelCase__     )
             _A    		=  nn.silu(UpperCamelCase__     )

        _A    		=  self.conv_out(UpperCamelCase__     )

        return embedding







@flax_register_to_config
class lowercase_       (		nn.Module    ,    _UpperCamelCase    ,    _UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   3_2
   __lowerCAmelCase         =   4
   __lowerCAmelCase         =   (
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "DownBlock2D",
   )
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
   __lowerCAmelCase         =   2
   __lowerCAmelCase         =   8
   __lowerCAmelCase         =   None
   __lowerCAmelCase         =   1_2_8_0
   __lowerCAmelCase         =   0.0
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   jnp.floataa
   __lowerCAmelCase         =   True
   __lowerCAmelCase         =   0
   __lowerCAmelCase         =   "rgb"
   __lowerCAmelCase         =   (1_6, 3_2, 9_6, 2_5_6)
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			jax.random.KeyArray     )  ->			FrozenDict:
        # init input tensors
        _A    		=  (1, self.in_channels, self.sample_size, self.sample_size)
        _A    		=  jnp.zeros(UpperCamelCase__, dtype=jnp.floataa     )
        _A    		=  jnp.ones((1,), dtype=jnp.intaa     )
        _A    		=  jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.floataa     )
        _A    		=  (1, 3, self.sample_size * 8, self.sample_size * 8)
        _A    		=  jnp.zeros(UpperCamelCase__, dtype=jnp.floataa     )

        _A		,       _A    		=  jax.random.split(UpperCamelCase__     )
        _A    		=  {'params': params_rng, 'dropout': dropout_rng}

        return self.init(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )["params"]
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  self.block_out_channels
        _A    		=  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.
        _A    		=  self.num_attention_heads or self.attention_head_dim

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

        # time
        _A    		=  FlaxTimesteps(
            block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift     )
        _A    		=  FlaxTimestepEmbedding(UpperCamelCase__, dtype=self.dtype     )

        _A    		=  FlaxControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0], block_out_channels=self.conditioning_embedding_out_channels, )

        _A    		=  self.only_cross_attention
        if isinstance(UpperCamelCase__, UpperCamelCase__     ):
             _A    		=  (only_cross_attention,) * len(self.down_block_types     )

        if isinstance(UpperCamelCase__, UpperCamelCase__     ):
             _A    		=  (num_attention_heads,) * len(self.down_block_types     )

        # down
        _A    		=  []
        _A    		=  []

        _A    		=  block_out_channels[0]

        _A    		=  nn.Conv(
            UpperCamelCase__, 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(UpperCamelCase__     )

        for i, down_block_type in enumerate(self.down_block_types     ):
             _A    		=  output_channel
             _A    		=  block_out_channels[i]
             _A    		=  i == len(UpperCamelCase__     ) - 1

             if down_block_type == "CrossAttnDownBlock2D":
                  _A    		=  FlaxCrossAttnDownBlockaD(
                      in_channels=UpperCamelCase__, out_channels=UpperCamelCase__, 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:
                  _A    		=  FlaxDownBlockaD(
                      in_channels=UpperCamelCase__, out_channels=UpperCamelCase__, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, )

             down_blocks.append(UpperCamelCase__     )

             for _ in range(self.layers_per_block     ):
                  _A    		=  nn.Conv(
                      UpperCamelCase__, 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(UpperCamelCase__     )

             if not is_final_block:
                  _A    		=  nn.Conv(
                      UpperCamelCase__, 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(UpperCamelCase__     )

        _A    		=  down_blocks
        _A    		=  controlnet_down_blocks

        # mid
        _A    		=  block_out_channels[-1]
        _A    		=  FlaxUNetMidBlockaDCrossAttn(
            in_channels=UpperCamelCase__, dropout=self.dropout, num_attention_heads=num_attention_heads[-1], use_linear_projection=self.use_linear_projection, dtype=self.dtype, )

        _A    		=  nn.Conv(
            UpperCamelCase__, kernel_size=(1, 1), padding='VALID', kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )

   def __call__(      self      :			List[Any], UpperCamelCase__      :			List[str], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Dict, UpperCamelCase__      :			float = 1.0, UpperCamelCase__      :			bool = True, UpperCamelCase__      :			bool = False, )  ->			Union[FlaxControlNetOutput, Tuple]:
        _A    		=  self.controlnet_conditioning_channel_order
        if channel_order == "bgr":
             _A    		=  jnp.flip(UpperCamelCase__, axis=1     )

        # 1. time
        if not isinstance(UpperCamelCase__, jnp.ndarray     ):
             _A    		=  jnp.array([timesteps], dtype=jnp.intaa     )
        elif isinstance(UpperCamelCase__, jnp.ndarray     ) and len(timesteps.shape     ) == 0:
             _A    		=  timesteps.astype(dtype=jnp.floataa     )
             _A    		=  jnp.expand_dims(UpperCamelCase__, 0     )

        _A    		=  self.time_proj(UpperCamelCase__     )
        _A    		=  self.time_embedding(UpperCamelCase__     )

        # 2. pre-process
        _A    		=  jnp.transpose(UpperCamelCase__, (0, 2, 3, 1)     )
        _A    		=  self.conv_in(UpperCamelCase__     )

        _A    		=  jnp.transpose(UpperCamelCase__, (0, 2, 3, 1)     )
        _A    		=  self.controlnet_cond_embedding(UpperCamelCase__     )
        sample += controlnet_cond

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

        # 4. mid
        _A    		=  self.mid_block(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, deterministic=not train     )

        # 5. contronet blocks
        _A    		=  ()
        for down_block_res_sample, controlnet_block in zip(UpperCamelCase__, self.controlnet_down_blocks     ):
             _A    		=  controlnet_block(UpperCamelCase__     )
             controlnet_down_block_res_samples += (down_block_res_sample,)

        _A    		=  controlnet_down_block_res_samples

        _A    		=  self.controlnet_mid_block(UpperCamelCase__     )

        # 6. scaling
        _A    		=  [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=UpperCamelCase__, mid_block_res_sample=UpperCamelCase__     )




'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			str   ,		__snake_case							:			List[Any]=False				):

     if isinstance(__snake_case   ,		__snake_case				) and isinstance(__snake_case   ,		__snake_case				):
          _A    		=  len(set_a.intersection(__snake_case				)				)

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
          else:
               _A    		=  len(set_a.union(__snake_case				)				)

          return intersection / union

     if isinstance(__snake_case   ,		(list, tuple)				) and isinstance(__snake_case   ,		(list, tuple)				):
          _A    		=  [element for element in set_a if element in set_b]

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
               return len(__snake_case				) / union
          else:
               _A    		=  set_a + [element for element in set_b if element not in set_a]
               return len(__snake_case				) / len(__snake_case				)

          return len(__snake_case				) / len(__snake_case				)
     return None


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[Any]         =       {'''a''', '''b''', '''c''', '''d''', '''e'''}
   _UpperCAmelCase    :     str         =       {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
   print(jaccard_similarity(set_a, set_b))



