






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[Any]				):
			return np.dot(lowerCamelCase_   ,  lowerCamelCase_				)






class 			_UpperCamelCase:

		def __init__(					self		:    int    ,       *,
		    SCREAMING_SNAKE_CASE__		:    Optional[Any] = np.inf    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any] = "linear"    ,       SCREAMING_SNAKE_CASE__		:    Dict = 0.0    ,       ):



					'''simple docstring'''



					__a						:  List[str]									=     regularization
					__a						:  Any									=     gamma
					if kernel == "linear":
								__a						:  Any									=     self.__linear
					elif kernel == "rbf":
								if self.gamma == 0:
											raise ValueError('rbf kernel requires gamma'    )
								if not isinstance(self.gamma    ,       (float, int)    ):
											raise ValueError('gamma must be float or int'    )
								if not self.gamma > 0:
											raise ValueError('gamma must be > 0'    )
								__a						:  Tuple									=     self.__rbf
								# in the future, there could be a default value like in sklearn
								# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
								# previously it was 1/(n_features)
					else:
								__a						:  int									=     f'''Unknown kernel: {kernel}'''
								raise ValueError(A_    )

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					return np.dot(A_    ,       A_    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					return np.exp(-(self.gamma * norm_squared(vectora - vectora    ))    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Dict									=     observations
					__a						:  Dict									=     classes

					# using Wolfe's Dual to calculate w.
					# Primal problem: minimize 1/2*norm_squared(w)
					#   constraint: yn(w . xn + b) >= 1
					#
					# With l a vector
					# Dual problem: maximize sum_n(ln) -
					#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
					#   constraint: self.C >= ln >= 0
					#           and sum_n(ln*yn) = 0
					# Then we get w using w = sum_n(ln*yn*xn)
					# At the end we can get b ~= mean(yn - w . xn)
					#
					# Since we use kernels, we only need l_star to calculate b
					# and to classify observations

					((__a							)						,												)						:  Any									=     np.shape(A_    )

					def to_minimize(SCREAMING_SNAKE_CASE__		:    Optional[int]    ) -> float:
								__a						:  Tuple									=     0
								((__a							)						,												)						:  List[Any]									=     np.shape(A_    )
								for i in range(A_    ):
											for j in range(A_    ):
														s += (
														    candidate[i]
														    * candidate[j]
														    * classes[i]
														    * classes[j]
														    * self.kernel(observations[i]    ,       observations[j]    )
														)
								return 1 / 2 * s - sum(A_    )

					__a						:  int									=     LinearConstraint(A_    ,       0    ,       0    )
					__a						:  Any									=     Bounds(0    ,       self.regularization    )

					__a						:  Dict									=     minimize(
					    A_    ,       np.ones(A_    )    ,       bounds=A_    ,       constraints=[ly_contraint]    ).x
					__a						:  int									=     l_star

					# calculating mean offset of separation plane to points
					__a						:  str									=     0
					for i in range(A_    ):
								for j in range(A_    ):
											s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
											    observations[i]    ,       observations[j]    )
					__a						:  Tuple									=     s / n



		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  List[str]									=     sum(
					    self.optimum[n]
					    * self.classes[n]
					    * self.kernel(self.observations[n]    ,       A_    )
					    for n in range(len(self.classes    )    )    )
					return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()












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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42
 __UpperCamelCase       =      42

class     snake_case_		(   nn.Module							):






 '''simple docstring'''





 __UpperCamelCase       =      42
 __UpperCamelCase       =      (1_6, 3_2, 9_6, 2_5_6)
 __UpperCamelCase       =      jnp.floataa

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=nn.Conv(
        self.block_out_channels[0],		kernel_size=(3, 3),		padding=((1, 1), (1, 1)),		dtype=self.dtype,		)

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

    UpperCAmelCase__						=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,		A_		)	->       Any:
    UpperCAmelCase__						=self.conv_in(A_		)
    UpperCAmelCase__						=nn.silu(A_		)

    for block in self.blocks:
       UpperCAmelCase__						=block(A_		)
       UpperCAmelCase__						=nn.silu(A_		)

    UpperCAmelCase__						=self.conv_out(A_		)

    return embedding

@flax_register_to_config
class     snake_case_		(   nn.Module,		a,		a							):






 '''simple docstring'''





 __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  __UpperCAmelCase						(  self,		A_		)	->       FrozenDict:
    # init input tensors
    UpperCAmelCase__						=(1, self.in_channels, self.sample_size, self.sample_size)
    UpperCAmelCase__						=jnp.zeros(A_,		dtype=jnp.floataa		)
    UpperCAmelCase__						=jnp.ones((1,),		dtype=jnp.intaa		)
    UpperCAmelCase__						=jnp.zeros((1, 1, self.cross_attention_dim),		dtype=jnp.floataa		)
    UpperCAmelCase__						=(1, 3, self.sample_size * 8, self.sample_size * 8)
    UpperCAmelCase__						=jnp.zeros(A_,		dtype=jnp.floataa		)

    UpperCAmelCase__			,							UpperCAmelCase__						=jax.random.split(A_		)
    UpperCAmelCase__						={"params": params_rng, "dropout": dropout_rng}

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

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=self.block_out_channels
    UpperCAmelCase__						=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__						=self.num_attention_heads or self.attention_head_dim

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

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

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

    UpperCAmelCase__						=self.only_cross_attention
    if isinstance(A_,		A_		):
       UpperCAmelCase__						=(only_cross_attention,) * len(self.down_block_types		)

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=(num_attention_heads,) * len(self.down_block_types		)

    # down
    UpperCAmelCase__						=[]
    UpperCAmelCase__						=[]

    UpperCAmelCase__						=block_out_channels[0]

    UpperCAmelCase__						=nn.Conv(
        A_,		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(A_		)

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

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

       down_blocks.append(A_		)

       for _ in range(self.layers_per_block		):
          UpperCAmelCase__						=nn.Conv(
              A_,		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(A_		)

       if not is_final_block:
          UpperCAmelCase__						=nn.Conv(
              A_,		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(A_		)

    UpperCAmelCase__						=down_blocks
    UpperCAmelCase__						=controlnet_down_blocks

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

    UpperCAmelCase__						=nn.Conv(
        A_,		kernel_size=(1, 1),		padding="VALID",		kernel_init=nn.initializers.zeros_init(),		bias_init=nn.initializers.zeros_init(),		dtype=self.dtype,		)






 def __call__(  self,		A_,		A_,		A_,		A_,		A_ = 1.0,		A_ = True,		A_ = False,		)	->       Union[FlaxControlNetOutput, Tuple]:
    UpperCAmelCase__						=self.controlnet_conditioning_channel_order
    if channel_order == "bgr":
       UpperCAmelCase__						=jnp.flip(A_,		axis=1		)

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

    UpperCAmelCase__						=self.time_proj(A_		)
    UpperCAmelCase__						=self.time_embedding(A_		)

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

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

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

    # 4. mid
    UpperCAmelCase__						=self.mid_block(A_,		A_,		A_,		deterministic=not train		)

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

    UpperCAmelCase__						=controlnet_down_block_res_samples

    UpperCAmelCase__						=self.controlnet_mid_block(A_		)

    # 6. scaling
    UpperCAmelCase__						=[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=A_,		mid_block_res_sample=A_		)



