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

snake_case__				= imread(R'digital_image_processing/image_data/lena_small.jpg')
snake_case__				= cvtColor(img, COLOR_BGR2GRAY)
def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				cn.convert_to_negative(a_		)
      # assert negative_img array for at least one True
      assert negative_img.any()
def       __magic_name__(  ):



      '''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(a_ ,						110		)		).startswith(
                '''<PIL.Image.Image image mode=RGB size=100x100 at'''		)
def       __magic_name__(  ):



      '''simple docstring'''

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



      '''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(a_		)
      # assert canny array for at least one True
      assert canny_array.any()
def       __magic_name__(  ):



      '''simple docstring'''

      assert gg.gaussian_filter(a_ ,						5 ,						sigma=0.9		).all()
def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]]		)
      _lowerCamelCase			    =				conv.img_convolve(a_ ,						a_		).astype(a_		)
      assert res.any()
def       __magic_name__(  ):



      '''simple docstring'''

      assert med.median_filter(a_ ,						3		).any()
def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				sob.sobel_filter(a_		)
      assert grad.any() and theta.any()
def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				sp.make_sepia(a_ ,						20		)
      assert sepia.all()
def       __magic_name__(  __UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg"		):



      '''simple docstring'''

      _lowerCamelCase			    =				bs.Burkes(imread(a_ ,						1		) ,						120		)
      burkes.process()
      assert burkes.output_img.any()
def       __magic_name__(  __UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg" ,						):



      '''simple docstring'''

      _lowerCamelCase			    =				rs.NearestNeighbour(imread(a_ ,						1		) ,						400 ,						200		)
      nn.process()
      assert nn.output.any()




def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				'''digital_image_processing/image_data/lena.jpg'''

      # Reading the image and converting it to grayscale.
      _lowerCamelCase			    =				imread(a_ ,						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(
          a_ ,						a_ ,						a_ ,						a_		)

      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(a_ ,						a_ ,						a_		)

      assert lbp_image.any()
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

           # time
           if time_embedding_type == "fourier":
                 _lowerCamelCase			    =				GaussianFourierProjection(
                     embedding_size=8				,				set_W_to_weight=A_				,				log=A_				,				flip_sin_to_cos=A_						)
                 _lowerCamelCase			    =				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _lowerCamelCase			    =				Timesteps(
                     block_out_channels[0]				,				flip_sin_to_cos=A_				,				downscale_freq_shift=A_						)
                 _lowerCamelCase			    =				block_out_channels[0]

           if use_timestep_embedding:
                 _lowerCamelCase			    =				block_out_channels[0] * 4
                 _lowerCamelCase			    =				TimestepEmbedding(
                     in_channels=A_				,				time_embed_dim=A_				,				act_fn=A_				,				out_dim=block_out_channels[0]				,				)

           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None

           # down
           _lowerCamelCase			    =				in_channels
           for i, down_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_down_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_downsample=not is_final_block or downsample_each_block				,				)
                 self.down_blocks.append(A_						)

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				in_channels=block_out_channels[-1]				,				mid_channels=block_out_channels[-1]				,				out_channels=block_out_channels[-1]				,				embed_dim=block_out_channels[0]				,				num_layers=A_				,				add_downsample=A_				,				)

           # up
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           if out_block_type is None:
                 _lowerCamelCase			    =				out_channels
           else:
                 _lowerCamelCase			    =				block_out_channels[0]

           for i, up_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				(
                     reversed_block_out_channels[i + 1] if i < len(A_						) - 1 else final_upsample_channels
                 )

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_up_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_upsample=not is_final_block				,				)
                 self.up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel

           # out
           _lowerCamelCase			    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,				32						)
           _lowerCamelCase			    =				get_out_block(
               out_block_type=A_				,				num_groups_out=A_				,				embed_dim=block_out_channels[0]				,				out_channels=A_				,				act_fn=A_				,				fc_dim=block_out_channels[-1] // 4				,				)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           if self.config.use_timestep_embedding:
                 _lowerCamelCase			    =				self.time_mlp(A_						)
           else:
                 _lowerCamelCase			    =				timestep_embed[..., None]
                 _lowerCamelCase			    =				timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
                 _lowerCamelCase			    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

           # 2. down
           _lowerCamelCase			    =				()
           for downsample_block in self.down_blocks:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				downsample_block(hidden_states=A_				,				temb=A_						)
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks						):
                 _lowerCamelCase			    =				down_block_res_samples[-1:]
                 _lowerCamelCase			    =				down_block_res_samples[:-1]
                 _lowerCamelCase			    =				upsample_block(A_				,				res_hidden_states_tuple=A_				,				temb=A_						)

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)