

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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)


@flax.struct.dataclass
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							42




@flax_register_to_config
class  A						(							nn.Module    ,							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							32
    lowerCamelCase			=							4
    lowerCamelCase			=							4
    lowerCamelCase			=							(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    lowerCamelCase			=							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    lowerCamelCase			=							False
    lowerCamelCase			=							(3_20, 6_40, 12_80, 12_80)
    lowerCamelCase			=							2
    lowerCamelCase			=							8
    lowerCamelCase			=							None
    lowerCamelCase			=							12_80
    lowerCamelCase			=							0.0
    lowerCamelCase			=							False
    lowerCamelCase			=							jnp.floataa
    lowerCamelCase			=							True
    lowerCamelCase			=							0
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       str,lowercase_ :       jax.random.KeyArray		)->    FrozenDict:





        '''simple docstring'''







        A__          =   (1, self.in_channels, self.sample_size, self.sample_size)
        A__          =   jnp.zeros(lowercase_,dtype=jnp.floataa		)
        A__          =   jnp.ones((1,),dtype=jnp.intaa		)
        A__          =   jnp.zeros((1, 1, self.cross_attention_dim),dtype=jnp.floataa		)

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

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


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   self.block_out_channels
        A__          =   block_out_channels[0] * 4

        if self.num_attention_heads is not None:
            raise ValueError(
                'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'		)

        # 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(lowercase_,dtype=self.dtype		)

        A__          =   self.only_cross_attention
        if isinstance(lowercase_,lowercase_		):
            A__          =   (only_cross_attention,) * len(self.down_block_types		)

        if isinstance(lowercase_,lowercase_		):
            A__          =   (num_attention_heads,) * len(self.down_block_types		)

        # down
        A__          =   []
        A__          =   block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types		):
            A__          =   output_channel
            A__          =   block_out_channels[i]
            A__          =   i == len(lowercase_		) - 1

            if down_block_type == "CrossAttnDownBlock2D":
                A__          =   FlaxCrossAttnDownBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,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],use_memory_efficient_attention=self.use_memory_efficient_attention,dtype=self.dtype,)
            else:
                A__          =   FlaxDownBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,dropout=self.dropout,num_layers=self.layers_per_block,add_downsample=not is_final_block,dtype=self.dtype,)

            down_blocks.append(lowercase_		)
        A__          =   down_blocks

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

        # up
        A__          =   []
        A__          =   list(reversed(lowercase_		)		)
        A__          =   list(reversed(lowercase_		)		)
        A__          =   list(reversed(lowercase_		)		)
        A__          =   reversed_block_out_channels[0]
        for i, up_block_type in enumerate(self.up_block_types		):
            A__          =   output_channel
            A__          =   reversed_block_out_channels[i]
            A__          =   reversed_block_out_channels[min(i + 1,len(lowercase_		) - 1		)]

            A__          =   i == len(lowercase_		) - 1

            if up_block_type == "CrossAttnUpBlock2D":
                A__          =   FlaxCrossAttnUpBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,num_layers=self.layers_per_block + 1,num_attention_heads=reversed_num_attention_heads[i],add_upsample=not is_final_block,dropout=self.dropout,use_linear_projection=self.use_linear_projection,only_cross_attention=only_cross_attention[i],use_memory_efficient_attention=self.use_memory_efficient_attention,dtype=self.dtype,)
            else:
                A__          =   FlaxUpBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,num_layers=self.layers_per_block + 1,add_upsample=not is_final_block,dropout=self.dropout,dtype=self.dtype,)

            up_blocks.append(lowercase_		)
            A__          =   output_channel
        A__          =   up_blocks

        # out
        A__          =   nn.GroupNorm(num_groups=3_2,epsilon=1E-5		)
        A__          =   nn.Conv(
            self.out_channels,kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)


    def __call__(      self :       Any,lowercase_ :       str,lowercase_ :       List[str],lowercase_ :       List[Any],lowercase_ :       Dict=None,lowercase_ :       List[str]=None,lowercase_ :       bool = True,lowercase_ :       bool = False,)->    Union[FlaxUNetaDConditionOutput, Tuple]:





        '''simple docstring'''







        if not isinstance(lowercase_,jnp.ndarray		):
            A__          =   jnp.array([timesteps],dtype=jnp.intaa		)
        elif isinstance(lowercase_,jnp.ndarray		) and len(timesteps.shape		) == 0:
            A__          =   timesteps.astype(dtype=jnp.floataa		)
            A__          =   jnp.expand_dims(lowercase_,0		)

        A__          =   self.time_proj(lowercase_		)
        A__          =   self.time_embedding(lowercase_		)

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

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

        if down_block_additional_residuals is not None:
            A__          =   ()

            for down_block_res_sample, down_block_additional_residual in zip(
                lowercase_,lowercase_		):
                down_block_res_sample += down_block_additional_residual
                new_down_block_res_samples += (down_block_res_sample,)

            A__          =   new_down_block_res_samples

        # 4. mid
        A__          =   self.mid_block(lowercase_,lowercase_,lowercase_,deterministic=not train		)

        if mid_block_additional_residual is not None:
            sample += mid_block_additional_residual

        # 5. up
        for up_block in self.up_blocks:
            A__          =   down_block_res_samples[-(self.layers_per_block + 1) :]
            A__          =   down_block_res_samples[: -(self.layers_per_block + 1)]
            if isinstance(lowercase_,lowercase_		):
                A__          =   up_block(
                    lowercase_,temb=lowercase_,encoder_hidden_states=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train,)
            else:
                A__          =   up_block(lowercase_,temb=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train		)

        # 6. post-process
        A__          =   self.conv_norm_out(lowercase_		)
        A__          =   nn.silu(lowercase_		)
        A__          =   self.conv_out(lowercase_		)
        A__          =   jnp.transpose(lowercase_,(0, 3, 1, 2)		)

        if not return_dict:
            return (sample,)

        return FlaxUNetaDConditionOutput(sample=lowercase_		)




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


