





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_UpperCamelCase    =	'''pcuenq/k-upscaler'''







def  lowerCAmelCase__(		lowercase    :      Union[str, Any]				,       lowercase    :      int				,       *, lowercase    :      Tuple				,       lowercase    :      str			)	->      Any:
  __snake_case				:			int            =    {
      # norm1
      f"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[f"""{resnet_prefix}.main.0.mapper.weight"""],
      f"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[f"""{resnet_prefix}.main.0.mapper.bias"""],
      # conv1
      f"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[f"""{resnet_prefix}.main.2.weight"""],
      f"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[f"""{resnet_prefix}.main.2.bias"""],
      # norm2
      f"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[f"""{resnet_prefix}.main.4.mapper.weight"""],
      f"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[f"""{resnet_prefix}.main.4.mapper.bias"""],
      # conv2
      f"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[f"""{resnet_prefix}.main.6.weight"""],
      f"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[f"""{resnet_prefix}.main.6.bias"""],
  }

  if resnet.conv_shortcut is not None:
    rv.update(
        {
            f"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[f"""{resnet_prefix}.skip.weight"""],
        }			)

  return rv







def  lowerCAmelCase__(		lowercase    :      str				,       *, lowercase    :      Optional[int]				,       lowercase    :      int			)	->      Optional[Any]:
  __snake_case      ,				__snake_case      ,				__snake_case				:			Optional[int]            =    checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3				,       dim=0			)
  __snake_case      ,				__snake_case      ,				__snake_case				:			Optional[Any]            =    checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3				,       dim=0			)
  __snake_case				:			int            =    {
      # norm
      f"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[f"""{attention_prefix}.norm_in.mapper.weight"""],
      f"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[f"""{attention_prefix}.norm_in.mapper.bias"""],
      # to_q
      f"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1			).squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
      # to_k
      f"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1			).squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
      # to_v
      f"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1			).squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
      # to_out
      f"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[f"""{attention_prefix}.out_proj.weight"""]
      .squeeze(-1			)
      .squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[f"""{attention_prefix}.out_proj.bias"""],
  }

  return rv







def  lowerCAmelCase__(		lowercase    :      int				,       *, lowercase    :      List[Any]				,       lowercase    :      List[str]				,       lowercase    :      Optional[Any]			)	->      List[Any]:
  __snake_case      ,				__snake_case				:			Optional[Any]            =    checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2				,       dim=0			)
  __snake_case      ,				__snake_case				:			Union[str, Any]            =    checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2				,       dim=0			)

  __snake_case				:			List[Any]            =    {
      # norm2 (ada groupnorm)
      f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
          f"""{attention_prefix}.norm_dec.mapper.weight"""
      ],
      f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
          f"""{attention_prefix}.norm_dec.mapper.bias"""
      ],
      # layernorm on encoder_hidden_state
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
          f"""{attention_prefix}.norm_enc.weight"""
      ],
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
          f"""{attention_prefix}.norm_enc.bias"""
      ],
      # to_q
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
          f"""{attention_prefix}.q_proj.weight"""
      ]
      .squeeze(-1			)
      .squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
          f"""{attention_prefix}.q_proj.bias"""
      ],
      # to_k
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1			).squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
      # to_v
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1			).squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
      # to_out
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
          f"""{attention_prefix}.out_proj.weight"""
      ]
      .squeeze(-1			)
      .squeeze(-1			),
      f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
          f"""{attention_prefix}.out_proj.bias"""
      ],
  }

  return rv







def  lowerCAmelCase__(		lowercase    :      List[str]				,       lowercase    :      int				,       lowercase    :      Union[str, Any]				,       lowercase    :      Optional[Any]			)	->      Union[str, Any]:
  __snake_case				:			Optional[Any]            =    "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
  __snake_case				:			int            =    f"""{block_prefix}.{block_idx}"""

  __snake_case				:			Any            =    {}

  if not hasattr(lowercase				,       "attentions"			):
    __snake_case				:			Optional[Any]            =    1  # resnet only
  elif not block.attentions[0].add_self_attention:
    __snake_case				:			List[str]            =    2  # resnet -> cross-attention
  else:
    __snake_case				:			Optional[Any]            =    3  # resnet -> self-attention -> cross-attention)

  for resnet_idx, resnet in enumerate(block.resnets			):
    # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
    __snake_case				:			Any            =    f"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
    __snake_case				:			int            =    n * resnet_idx if block_type == "up" else n * resnet_idx + 1
    __snake_case				:			Optional[Any]            =    f"""{block_prefix}.{idx}""" if block_type == "up" else f"""{block_prefix}.{idx}"""

    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            lowercase				,       lowercase				,       diffusers_resnet_prefix=lowercase				,       resnet_prefix=lowercase			)			)

  if hasattr(lowercase				,       "attentions"			):
    for attention_idx, attention in enumerate(block.attentions			):
      __snake_case				:			Optional[int]            =    f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
      __snake_case				:			Dict            =    n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
      __snake_case				:			Dict            =    f"""{block_prefix}.{idx}"""
      __snake_case				:			int            =    f"""{block_prefix}.{idx }"""
      __snake_case				:			Optional[int]            =    1 if not attention.add_self_attention else 2
      __snake_case				:			List[Any]            =    (
          n * attention_idx + cross_attention_index
          if block_type == "up"
          else n * attention_idx + cross_attention_index + 1
      )
      __snake_case				:			Optional[Any]            =    f"""{block_prefix}.{idx }"""

      diffusers_checkpoint.update(
          cross_attn_to_diffusers_checkpoint(
              lowercase				,       diffusers_attention_prefix=lowercase				,       diffusers_attention_index=2				,       attention_prefix=lowercase				,       )			)

      if attention.add_self_attention is True:
        diffusers_checkpoint.update(
            self_attn_to_diffusers_checkpoint(
                lowercase				,       diffusers_attention_prefix=lowercase				,       attention_prefix=lowercase				,       )			)

  return diffusers_checkpoint







def  lowerCAmelCase__(		lowercase    :      List[str]				,       lowercase    :      Optional[int]			)	->      Optional[Any]:
  __snake_case				:			Optional[int]            =    {}

  # pre-processing
  diffusers_checkpoint.update(
      {
          "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
          "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
      }			)

  # timestep and class embedding
  diffusers_checkpoint.update(
      {
          "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1			),
          "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
          "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
          "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
          "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
          "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
      }			)

  # down_blocks
  for down_block_idx, down_block in enumerate(model.down_blocks			):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase				,       lowercase				,       lowercase				,       "down"			)			)

  # up_blocks
  for up_block_idx, up_block in enumerate(model.up_blocks			):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase				,       lowercase				,       lowercase				,       "up"			)			)

  # post-processing
  diffusers_checkpoint.update(
      {
          "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
          "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
      }			)

  return diffusers_checkpoint







def  lowerCAmelCase__(		lowercase    :      Any			)	->      List[str]:
  __snake_case				:			Any            =    original_config["input_channels"] + original_config["unet_cond_dim"]
  __snake_case				:			str            =    original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

  __snake_case				:			Optional[int]            =    original_config["channels"]

  assert (
      len(set(original_config["depths"]			)			) == 1
  ), "UNet2DConditionModel currently do not support blocks with different number of layers"
  __snake_case				:			List[str]            =    original_config["depths"][0]

  __snake_case				:			str            =    original_config["mapping_cond_dim"]
  __snake_case				:			List[str]            =    original_config["cross_cond_dim"]

  __snake_case				:			List[Any]            =    []
  __snake_case				:			Union[str, Any]            =    []
  for s, c in zip(original_config["self_attn_depths"]				,       original_config["cross_attn_depths"]			):
    if s:
      __snake_case				:			List[Any]            =    "self"
      __snake_case				:			Union[str, Any]            =    "cross" if c else None
    elif c:
      __snake_case				:			Dict            =    "cross"
      __snake_case				:			Any            =    None
    else:
      __snake_case				:			Tuple            =    None
      __snake_case				:			Optional[Any]            =    None
    attna_types.append(lowercase			)
    attna_types.append(lowercase			)

  __snake_case				:			Tuple            =    UNetaDConditionModel(
      in_channels=lowercase				,       out_channels=lowercase				,       down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")				,       mid_block_type=lowercase				,       up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,       block_out_channels=lowercase				,       layers_per_block=lowercase				,       act_fn="gelu"				,       norm_num_groups=lowercase				,       cross_attention_dim=lowercase				,       attention_head_dim=64				,       time_cond_proj_dim=lowercase				,       resnet_time_scale_shift="scale_shift"				,       time_embedding_type="fourier"				,       timestep_post_act="gelu"				,       conv_in_kernel=1				,       conv_out_kernel=1				,       )

  return unet







def  lowerCAmelCase__(		lowercase    :      Optional[Any]			)	->      Dict:
  __snake_case				:			List[str]            =    torch.device("cuda" if torch.cuda.is_available() else "cpu"			)

  __snake_case				:			Optional[int]            =    huggingface_hub.hf_hub_download(lowercase				,       "config_laion_text_cond_latent_upscaler_2.json"			)
  __snake_case				:			Any            =    huggingface_hub.hf_hub_download(
      lowercase				,       "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"			)
  print(f"""loading original model configuration from {orig_config_path}"""			)
  print(f"""loading original model checkpoint from {orig_weights_path}"""			)

  print("converting to diffusers unet"			)
  __snake_case				:			Tuple            =    K.config.load_config(open(lowercase			)			)["model"]
  __snake_case				:			int            =    unet_model_from_original_config(lowercase			)

  __snake_case				:			List[str]            =    torch.load(lowercase				,       map_location=lowercase			)["model_ema"]
  __snake_case				:			Dict            =    unet_to_diffusers_checkpoint(lowercase				,       lowercase			)

  model.load_state_dict(lowercase				,       strict=lowercase			)
  model.save_pretrained(args.dump_path			)
  print(f"""saving converted unet model in {args.dump_path}"""			)


if __name__ == "__main__":
     _UpperCamelCase    =	argparse.ArgumentParser()

     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     _UpperCamelCase    =	parser.parse_args()

     main(args)








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			int            =    0
        # max greyscale value for #FFFFFF
        __snake_case				:			int            =    int(self.get_greyscale(255			,  255			,  255				)				)

        if not self.min_threshold < threshold < self.max_threshold:
          __snake_case				:			Tuple            =    F"""Factor value should be from 0 to {self.max_threshold}"""
          raise ValueError(UpperCAmelCase				)

        __snake_case				:			List[Any]            =    input_img
        __snake_case				:			Any            =    threshold
        __snake_case      ,				__snake_case				:			str            =    self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        __snake_case				:			Dict            =    [
            [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
        ]
        __snake_case				:			str            =    np.ones((self.width, self.height, 3)			,  np.uinta				) * 255







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def   UpperCAmelCase						(	self				)    ->				None:






        '''simple docstring'''






        for y in range(self.height				):
          for x in range(self.width				):
            __snake_case				:			Optional[int]            =    int(self.get_greyscale(*self.input_img[y][x]				)				)
            if self.threshold > greyscale + self.error_table[y][x]:
              __snake_case				:			Any            =    (0, 0, 0)
              __snake_case				:			Optional[int]            =    greyscale + self.error_table[x][y]
            else:
              __snake_case				:			str            =    (255, 255, 255)
              __snake_case				:			Dict            =    greyscale + self.error_table[x][y] - 255
            self.error_table[y][x + 1] += int(8 / 32 * current_error				)
            self.error_table[y][x + 2] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x] += int(8 / 32 * current_error				)
            self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
            self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     _UpperCamelCase    =	[
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 126, 130, 140)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              F'''Original image with dithering threshold: {burkes.threshold}''',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()

