





'''simple docstring'''






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder




@dataclass
class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     "DiagonalGaussianDistribution"




class 					UpperCAmelCase	(     snake_case_				,						snake_case_  ):
      _lowercase:     Union[str, Any]			=  True




      @register_to_config
      def __init__(     self     :					List[str]     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 3     ,     __snake_case     :					Tuple[str] = ("DownEncoderBlock2D",)     ,     __snake_case     :					Tuple[str] = ("UpDecoderBlock2D",)     ,     __snake_case     :					Tuple[int] = (64,)     ,     __snake_case     :					int = 1     ,     __snake_case     :					str = "silu"     ,     __snake_case     :					int = 4     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     __snake_case     :					float = 0.1_82_15     ,     )							-> Any:
            super().__init__()

            # pass init params to Encoder
            _lowerCAmelCase					=				Encoder(
                in_channels=__snake_case     ,     out_channels=__snake_case     ,     down_block_types=__snake_case     ,     block_out_channels=__snake_case     ,     layers_per_block=__snake_case     ,     act_fn=__snake_case     ,     norm_num_groups=__snake_case     ,     double_z=__snake_case     ,     )

            # pass init params to Decoder
            _lowerCAmelCase					=				Decoder(
                in_channels=__snake_case     ,     out_channels=__snake_case     ,     up_block_types=__snake_case     ,     block_out_channels=__snake_case     ,     layers_per_block=__snake_case     ,     norm_num_groups=__snake_case     ,     act_fn=__snake_case     ,     )

            _lowerCAmelCase					=				nn.Convad(2 * latent_channels     ,     2 * latent_channels     ,     1    )
            _lowerCAmelCase					=				nn.Convad(__snake_case     ,     __snake_case     ,     1    )

            _lowerCAmelCase					=				False
            _lowerCAmelCase					=				False

            # only relevant if vae tiling is enabled
            _lowerCAmelCase					=				self.config.sample_size
            _lowerCAmelCase					=				(
                self.config.sample_size[0]
                if isinstance(self.config.sample_size     ,     (list, tuple)    )
                else self.config.sample_size
            )
            _lowerCAmelCase					=				int(sample_size / (2 ** (len(self.config.block_out_channels    ) - 1))    )
            _lowerCAmelCase					=				0.25




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					int     ,     __snake_case     :					Any=False    )							-> Any:
            if isinstance(__snake_case     ,     (Encoder, Decoder)    ):
                  _lowerCAmelCase					=				value




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					bool = True    )							-> Optional[Any]:
            _lowerCAmelCase					=				use_tiling




      def 							lowercase__       (     self     :					Any    )							-> Union[str, Any]:
            self.enable_tiling(__snake_case    )




      def 							lowercase__       (     self     :					List[str]    )							-> List[Any]:
            _lowerCAmelCase					=				True




      def 							lowercase__       (     self     :					int    )							-> Optional[Any]:
            _lowerCAmelCase					=				False




      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def 							lowercase__       (     self     :					List[Any]    )							-> Dict[str, AttentionProcessor]:
            _lowerCAmelCase					=				{}

            def fn_recursive_add_processors(__snake_case     :					str     ,     __snake_case     :					torch.nn.Module     ,     __snake_case     :					Dict[str, AttentionProcessor]    ):
                  if hasattr(__snake_case     ,     """set_processor"""    ):
                        _lowerCAmelCase					=				module.processor

                  for sub_name, child in module.named_children():
                        fn_recursive_add_processors(f"{name}.{sub_name}"     ,     __snake_case     ,     __snake_case    )

                  return processors

            for name, module in self.named_children():
                  fn_recursive_add_processors(__snake_case     ,     __snake_case     ,     __snake_case    )

            return processors




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					Union[AttentionProcessor, Dict[str, AttentionProcessor]]    )							-> str:
            _lowerCAmelCase					=				len(self.attn_processors.keys()    )

            if isinstance(__snake_case     ,     __snake_case    ) and len(__snake_case    ) != count:
                  raise ValueError(
                      f"A dict of processors was passed, but the number of processors {len(__snake_case    )} does not match the"
                      f" number of attention layers: {count}. Please make sure to pass {count} processor classes."    )

            def fn_recursive_attn_processor(__snake_case     :					str     ,     __snake_case     :					torch.nn.Module     ,     __snake_case     :					List[Any]    ):
                  if hasattr(__snake_case     ,     """set_processor"""    ):
                        if not isinstance(__snake_case     ,     __snake_case    ):
                              module.set_processor(__snake_case    )
                        else:
                              module.set_processor(processor.pop(f"{name}.processor"    )    )

                  for sub_name, child in module.named_children():
                        fn_recursive_attn_processor(f"{name}.{sub_name}"     ,     __snake_case     ,     __snake_case    )

            for name, module in self.named_children():
                  fn_recursive_attn_processor(__snake_case     ,     __snake_case     ,     __snake_case    )




      def 							lowercase__       (     self     :					int    )							-> Dict:
            self.set_attn_processor(AttnProcessor()    )




      @apply_forward_hook
      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True    )							-> AutoencoderKLOutput:
            if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
                  return self.tiled_encode(__snake_case     ,     return_dict=__snake_case    )

            if self.use_slicing and x.shape[0] > 1:
                  _lowerCAmelCase					=				[self.encoder(__snake_case    ) for x_slice in x.split(1    )]
                  _lowerCAmelCase					=				torch.cat(__snake_case    )
            else:
                  _lowerCAmelCase					=				self.encoder(__snake_case    )

            _lowerCAmelCase					=				self.quant_conv(__snake_case    )
            _lowerCAmelCase					=				DiagonalGaussianDistribution(__snake_case    )

            if not return_dict:
                  return (posterior,)

            return AutoencoderKLOutput(latent_dist=__snake_case    )




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True    )							-> Union[DecoderOutput, torch.FloatTensor]:
            if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
                  return self.tiled_decode(__snake_case     ,     return_dict=__snake_case    )

            _lowerCAmelCase					=				self.post_quant_conv(__snake_case    )
            _lowerCAmelCase					=				self.decoder(__snake_case    )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__snake_case    )




      @apply_forward_hook
      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True    )							-> Union[DecoderOutput, torch.FloatTensor]:
            if self.use_slicing and z.shape[0] > 1:
                  _lowerCAmelCase					=				[self._decode(__snake_case    ).sample for z_slice in z.split(1    )]
                  _lowerCAmelCase					=				torch.cat(__snake_case    )
            else:
                  _lowerCAmelCase					=				self._decode(__snake_case    ).sample

            if not return_dict:
                  return (decoded,)

            return DecoderOutput(sample=__snake_case    )




      def 							lowercase__       (     self     :					int     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					int     ,     __snake_case     :					int    )							-> List[str]:
            _lowerCAmelCase					=				min(a.shape[2]     ,     b.shape[2]     ,     __snake_case    )
            for y in range(__snake_case    ):
                  _lowerCAmelCase					=				a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
            return b




      def 							lowercase__       (     self     :					int     ,     __snake_case     :					int     ,     __snake_case     :					int     ,     __snake_case     :					List[Any]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				min(a.shape[3]     ,     b.shape[3]     ,     __snake_case    )
            for x in range(__snake_case    ):
                  _lowerCAmelCase					=				a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
            return b




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True    )							-> AutoencoderKLOutput:
            _lowerCAmelCase					=				int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)    )
            _lowerCAmelCase					=				int(self.tile_latent_min_size * self.tile_overlap_factor    )
            _lowerCAmelCase					=				self.tile_latent_min_size - blend_extent

            # Split the image into 512x512 tiles and encode them separately.
            _lowerCAmelCase					=				[]
            for i in range(0     ,     x.shape[2]     ,     __snake_case    ):
                  _lowerCAmelCase					=				[]
                  for j in range(0     ,     x.shape[3]     ,     __snake_case    ):
                        _lowerCAmelCase					=				x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                        _lowerCAmelCase					=				self.encoder(__snake_case    )
                        _lowerCAmelCase					=				self.quant_conv(__snake_case    )
                        row.append(__snake_case    )
                  rows.append(__snake_case    )
            _lowerCAmelCase					=				[]
            for i, row in enumerate(__snake_case    ):
                  _lowerCAmelCase					=				[]
                  for j, tile in enumerate(__snake_case    ):
                        # blend the above tile and the left tile
                        # to the current tile and add the current tile to the result row
                        if i > 0:
                              _lowerCAmelCase					=				self.blend_v(rows[i - 1][j]     ,     __snake_case     ,     __snake_case    )
                        if j > 0:
                              _lowerCAmelCase					=				self.blend_h(row[j - 1]     ,     __snake_case     ,     __snake_case    )
                        result_row.append(tile[:, :, :row_limit, :row_limit]    )
                  result_rows.append(torch.cat(__snake_case     ,     dim=3    )    )

            _lowerCAmelCase					=				torch.cat(__snake_case     ,     dim=2    )
            _lowerCAmelCase					=				DiagonalGaussianDistribution(__snake_case    )

            if not return_dict:
                  return (posterior,)

            return AutoencoderKLOutput(latent_dist=__snake_case    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True    )							-> Union[DecoderOutput, torch.FloatTensor]:
            _lowerCAmelCase					=				int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)    )
            _lowerCAmelCase					=				int(self.tile_sample_min_size * self.tile_overlap_factor    )
            _lowerCAmelCase					=				self.tile_sample_min_size - blend_extent

            # Split z into overlapping 64x64 tiles and decode them separately.
            # The tiles have an overlap to avoid seams between tiles.
            _lowerCAmelCase					=				[]
            for i in range(0     ,     z.shape[2]     ,     __snake_case    ):
                  _lowerCAmelCase					=				[]
                  for j in range(0     ,     z.shape[3]     ,     __snake_case    ):
                        _lowerCAmelCase					=				z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                        _lowerCAmelCase					=				self.post_quant_conv(__snake_case    )
                        _lowerCAmelCase					=				self.decoder(__snake_case    )
                        row.append(__snake_case    )
                  rows.append(__snake_case    )
            _lowerCAmelCase					=				[]
            for i, row in enumerate(__snake_case    ):
                  _lowerCAmelCase					=				[]
                  for j, tile in enumerate(__snake_case    ):
                        # blend the above tile and the left tile
                        # to the current tile and add the current tile to the result row
                        if i > 0:
                              _lowerCAmelCase					=				self.blend_v(rows[i - 1][j]     ,     __snake_case     ,     __snake_case    )
                        if j > 0:
                              _lowerCAmelCase					=				self.blend_h(row[j - 1]     ,     __snake_case     ,     __snake_case    )
                        result_row.append(tile[:, :, :row_limit, :row_limit]    )
                  result_rows.append(torch.cat(__snake_case     ,     dim=3    )    )

            _lowerCAmelCase					=				torch.cat(__snake_case     ,     dim=2    )
            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__snake_case    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = True     ,     __snake_case     :					Optional[torch.Generator] = None     ,     )							-> Union[DecoderOutput, torch.FloatTensor]:
            _lowerCAmelCase					=				sample
            _lowerCAmelCase					=				self.encode(__snake_case    ).latent_dist
            if sample_posterior:
                  _lowerCAmelCase					=				posterior.sample(generator=__snake_case    )
            else:
                  _lowerCAmelCase					=				posterior.mode()
            _lowerCAmelCase					=				self.decode(__snake_case    ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__snake_case    )













'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      _lowerCAmelCase					=				np.zeros((image.shape[0], image.shape[1])    )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,      image.shape[0]    ):
            for j in range(0				,      image.shape[1]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






