from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION

class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__ ):






    '''simple docstring'''




    def __init__(						self   ):
       super().__init__()

       self.register_buffer("watermark_image"					,	torch.zeros((62, 62, 4)   )   )
       lowerCAmelCase_:       str         =  None




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__=None   ):
       # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

       lowerCAmelCase_:       Union[str, Any]         =  images[0].height
       lowerCAmelCase_:       List[str]         =  images[0].width

       lowerCAmelCase_:       int         =  sample_size or h

       lowerCAmelCase_:       Optional[Any]         =  min(h / sample_size					,	w / sample_size   )
       lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

       lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  1_024**2, img_w * img_h
       lowerCAmelCase_:       Union[str, Any]         =  (Sa / Sa) ** 0.5
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

       if self.watermark_image_as_pil is None:
          lowerCAmelCase_:       str         =  self.watermark_image.to(torch.uinta   ).cpu().numpy()
          lowerCAmelCase_:       Any         =  Image.fromarray(lowerCamelCase__					,	mode="RGBA"   )
          lowerCAmelCase_:       Tuple         =  watermark_image

       lowerCAmelCase_:       Optional[Any]         =  self.watermark_image_as_pil.resize(
           (wm_size, wm_size)					,	PIL_INTERPOLATION["bicubic"]					,	reducing_gap=lowerCamelCase__   )

       for pil_img in images:
          pil_img.paste(lowerCamelCase__					,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)					,	mask=wm_img.split()[-1]   )

       return images
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 		snake_case__  (		lowercase       ,							lowercase=1				):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split("."				)[:n_shave_prefix_segments]				)
def 		snake_case__  (		lowercase       ,							lowercase=0				):
   lowerCAmelCase_:       List[Any]         =  []
   for old_item in old_list:
      lowerCAmelCase_:       Any         =  old_item
      lowerCAmelCase_:       Dict         =  new_item.replace("block."       ,							"resnets."				)
      lowerCAmelCase_:       Tuple         =  new_item.replace("conv_shorcut"       ,							"conv1"				)
      lowerCAmelCase_:       Optional[int]         =  new_item.replace("in_shortcut"       ,							"conv_shortcut"				)
      lowerCAmelCase_:       Any         =  new_item.replace("temb_proj"       ,							"time_emb_proj"				)

      lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)

      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 		snake_case__  (		lowercase       ,							lowercase=0       ,							lowercase=False				):
   lowerCAmelCase_:       Any         =  []
   for old_item in old_list:
      lowerCAmelCase_:       List[Any]         =  old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         lowerCAmelCase_:       str         =  new_item.replace("attn"       ,							"attentions"				)
      lowerCAmelCase_:       str         =  new_item.replace(".k."       ,							".key."				)
      lowerCAmelCase_:       Tuple         =  new_item.replace(".v."       ,							".value."				)
      lowerCAmelCase_:       Any         =  new_item.replace(".q."       ,							".query."				)

      lowerCAmelCase_:       List[Any]         =  new_item.replace("proj_out"       ,							"proj_attn"				)
      lowerCAmelCase_:       List[str]         =  new_item.replace("norm"       ,							"group_norm"				)

      lowerCAmelCase_:       Dict         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
   assert isinstance(lowercase       ,							lowercase				), "Paths should be a list of dicts containing 'old' and 'new' keys."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."				)

      for path, path_map in attention_paths_to_split.items():
         lowerCAmelCase_:       List[Any]         =  old_checkpoint[path]
         lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // 3

         lowerCAmelCase_:       Any         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         lowerCAmelCase_:       Optional[int]         =  old_tensor.shape[0] // config.get("num_head_channels"       ,							1				) // 3

         lowerCAmelCase_:       Optional[int]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  old_tensor.split(channels // num_heads       ,							dim=1				)

         lowerCAmelCase_:       List[str]         =  query.reshape(lowercase				).squeeze()
         lowerCAmelCase_:       str         =  key.reshape(lowercase				).squeeze()
         lowerCAmelCase_:       Tuple         =  value.reshape(lowercase				).squeeze()

   for path in paths:
      lowerCAmelCase_:       str         =  path["new"]

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      lowerCAmelCase_:       str         =  new_path.replace("down."       ,							"down_blocks."				)
      lowerCAmelCase_:       Tuple         =  new_path.replace("up."       ,							"up_blocks."				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            lowerCAmelCase_:       Union[str, Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)

      if "attentions" in new_path:
         lowerCAmelCase_:       List[Any]         =  old_checkpoint[path["old"]].squeeze()
      else:
         lowerCAmelCase_:       Union[str, Any]         =  old_checkpoint[path["old"]]
def 		snake_case__  (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Any         =  {}

   lowerCAmelCase_:       List[str]         =  checkpoint["temb.dense.0.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["temb.dense.0.bias"]
   lowerCAmelCase_:       Any         =  checkpoint["temb.dense.1.weight"]
   lowerCAmelCase_:       Optional[Any]         =  checkpoint["temb.dense.1.bias"]

   lowerCAmelCase_:       Optional[Any]         =  checkpoint["norm_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["norm_out.bias"]

   lowerCAmelCase_:       int         =  checkpoint["conv_in.weight"]
   lowerCAmelCase_:       int         =  checkpoint["conv_in.bias"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["conv_out.weight"]
   lowerCAmelCase_:       Dict         =  checkpoint["conv_out.bias"]

   lowerCAmelCase_:       Dict         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
   lowerCAmelCase_:       Tuple         =  {
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
   }

   lowerCAmelCase_:       str         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
   lowerCAmelCase_:       Optional[int]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}

   for i in range(lowercase				):
      lowerCAmelCase_:       List[str]         =  (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Any         =  checkpoint[
             F'''down.{i}.downsample.op.weight'''
         ]
         lowerCAmelCase_:       List[Any]         =  checkpoint[F'''down.{i}.downsample.op.bias''']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any("block" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       str         =  {
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       List[Any]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)

   lowerCAmelCase_:       List[str]         =  [key for key in checkpoint if "mid.block_1" in key]
   lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowerCAmelCase_:       Optional[int]         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)

   lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)

   lowerCAmelCase_:       int         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)

   for i in range(lowercase				):
      lowerCAmelCase_:       Any         =  num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       str         =  checkpoint[
             F'''up.{i}.upsample.conv.weight'''
         ]
         lowerCAmelCase_:       Union[str, Any]         =  checkpoint[F'''up.{i}.upsample.conv.bias''']

      if any("block" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       List[str]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       List[Any]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       List[Any]         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Optional[int]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Optional[int]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       Dict         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

   lowerCAmelCase_:       Tuple         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
   return new_checkpoint
def 		snake_case__  (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Optional[int]         =  {}

   lowerCAmelCase_:       Any         =  checkpoint["encoder.norm_out.weight"]
   lowerCAmelCase_:       Tuple         =  checkpoint["encoder.norm_out.bias"]

   lowerCAmelCase_:       Optional[int]         =  checkpoint["encoder.conv_in.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_in.bias"]
   lowerCAmelCase_:       List[str]         =  checkpoint["encoder.conv_out.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_out.bias"]

   lowerCAmelCase_:       Dict         =  checkpoint["decoder.norm_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["decoder.norm_out.bias"]

   lowerCAmelCase_:       Dict         =  checkpoint["decoder.conv_in.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_in.bias"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["decoder.conv_out.bias"]

   lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
   lowerCAmelCase_:       Any         =  {
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
   }

   lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
   lowerCAmelCase_:       Union[str, Any]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}

   for i in range(lowercase				):
      lowerCAmelCase_:       Any         =  (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  checkpoint[
             F'''encoder.down.{i}.downsample.conv.weight'''
         ]
         lowerCAmelCase_:       Any         =  checkpoint[
             F'''encoder.down.{i}.downsample.conv.bias'''
         ]

      if any("block" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Union[str, Any]         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       Dict         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Dict         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       List[str]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)

   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.block_1" in key]
   lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)

   lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)

   lowerCAmelCase_:       List[Any]         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)

   for i in range(lowercase				):
      lowerCAmelCase_:       str         =  num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  checkpoint[
             F'''decoder.up.{i}.upsample.conv.weight'''
         ]
         lowerCAmelCase_:       str         =  checkpoint[
             F'''decoder.up.{i}.upsample.conv.bias'''
         ]

      if any("block" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       str         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Dict         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Dict         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       Optional[int]         =  {
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Any         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

   lowerCAmelCase_:       Optional[Any]         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
   lowerCAmelCase_:       Optional[int]         =  checkpoint["quant_conv.weight"]
   lowerCAmelCase_:       Optional[Any]         =  checkpoint["quant_conv.bias"]
   if "quantize.embedding.weight" in checkpoint:
      lowerCAmelCase_:       List[Any]         =  checkpoint["quantize.embedding.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["post_quant_conv.weight"]
   lowerCAmelCase_:       Any         =  checkpoint["post_quant_conv.bias"]

   return new_checkpoint


if __name__ == "__main__":
 a     :			Any					    =						argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
 )

 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The config json file corresponding to the architecture.""",
 )

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

 a     :			Union[str, Any]					    =						parser.parse_args()
 a     :			List[Any]					    =						torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  a     :			Any					    =						json.loads(f.read())

 # unet case
 a     :			List[str]					    =						{key.split(""".""")[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  a     :			Optional[Any]					    =						convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  a     :			Optional[Any]					    =						convert_ddpm_checkpoint(checkpoint, config)

 if "ddpm" in config:
  del config["ddpm"]

 if config["_class_name"] == "VQModel":
  a     :			Any					    =						VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  a     :			Tuple					    =						AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  a     :			Optional[Any]					    =						UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  a     :			Dict					    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

  a     :			int					    =						DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)