'''simple docstring'''




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=1    )     ->   int:
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('''.'''    )[n_shave_prefix_segments:]    )
    else:
        return ".".join(path.split('''.'''    )[:n_shave_prefix_segments]    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=0    )     ->   int:
    __lowerCamelCase          =  []
    for old_item in old_list:
        __lowerCamelCase          =  old_item.replace('''in_layers.0'''    ,  '''norm1'''    )
        __lowerCamelCase          =  new_item.replace('''in_layers.2'''    ,  '''conv1'''    )

        __lowerCamelCase          =  new_item.replace('''out_layers.0'''    ,  '''norm2'''    )
        __lowerCamelCase          =  new_item.replace('''out_layers.3'''    ,  '''conv2'''    )

        __lowerCamelCase          =  new_item.replace('''emb_layers.1'''    ,  '''time_emb_proj'''    )
        __lowerCamelCase          =  new_item.replace('''skip_connection'''    ,  '''conv_shortcut'''    )

        __lowerCamelCase          =  shave_segments(UpperCamelCase__    ,  n_shave_prefix_segments=UpperCamelCase__    )

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

    return mapping






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=0    )     ->   str:
    __lowerCamelCase          =  []
    for old_item in old_list:
        __lowerCamelCase          =  old_item

        __lowerCamelCase          =  new_item.replace('''norm.weight'''    ,  '''group_norm.weight'''    )
        __lowerCamelCase          =  new_item.replace('''norm.bias'''    ,  '''group_norm.bias'''    )

        __lowerCamelCase          =  new_item.replace('''proj_out.weight'''    ,  '''proj_attn.weight'''    )
        __lowerCamelCase          =  new_item.replace('''proj_out.bias'''    ,  '''proj_attn.bias'''    )

        __lowerCamelCase          =  shave_segments(UpperCamelCase__    ,  n_shave_prefix_segments=UpperCamelCase__    )

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

    return mapping






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    )     ->   Union[str, Any]:
    assert isinstance(UpperCamelCase__    ,  UpperCamelCase__    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            __lowerCamelCase          =  old_checkpoint[path]
            __lowerCamelCase          =  old_tensor.shape[0] // 3

            __lowerCamelCase          =  (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

            __lowerCamelCase          =  old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

            __lowerCamelCase          =  query.reshape(UpperCamelCase__    )
            __lowerCamelCase          =  key.reshape(UpperCamelCase__    )
            __lowerCamelCase          =  value.reshape(UpperCamelCase__    )

    for path in paths:
        __lowerCamelCase          =  path['''new''']

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        __lowerCamelCase          =  new_path.replace('''middle_block.0'''    ,  '''mid_block.resnets.0'''    )
        __lowerCamelCase          =  new_path.replace('''middle_block.1'''    ,  '''mid_block.attentions.0'''    )
        __lowerCamelCase          =  new_path.replace('''middle_block.2'''    ,  '''mid_block.resnets.1'''    )

        if additional_replacements is not None:
            for replacement in additional_replacements:
                __lowerCamelCase          =  new_path.replace(replacement['''old''']    ,  replacement['''new''']    )

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            __lowerCamelCase          =  old_checkpoint[path['''old''']][:, :, 0]
        else:
            __lowerCamelCase          =  old_checkpoint[path['''old''']]






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  {}

    __lowerCamelCase          =  checkpoint['''time_embed.0.weight''']
    __lowerCamelCase          =  checkpoint['''time_embed.0.bias''']
    __lowerCamelCase          =  checkpoint['''time_embed.2.weight''']
    __lowerCamelCase          =  checkpoint['''time_embed.2.bias''']

    __lowerCamelCase          =  checkpoint['''input_blocks.0.0.weight''']
    __lowerCamelCase          =  checkpoint['''input_blocks.0.0.bias''']

    __lowerCamelCase          =  checkpoint['''out.0.weight''']
    __lowerCamelCase          =  checkpoint['''out.0.bias''']
    __lowerCamelCase          =  checkpoint['''out.2.weight''']
    __lowerCamelCase          =  checkpoint['''out.2.bias''']

    # Retrieves the keys for the input blocks only
    __lowerCamelCase          =  len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''input_blocks''' in layer}    )
    __lowerCamelCase          =  {
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase__    )
    }

    # Retrieves the keys for the middle blocks only
    __lowerCamelCase          =  len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''middle_block''' in layer}    )
    __lowerCamelCase          =  {
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase__    )
    }

    # Retrieves the keys for the output blocks only
    __lowerCamelCase          =  len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''output_blocks''' in layer}    )
    __lowerCamelCase          =  {
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase__    )
    }

    for i in range(1    ,  UpperCamelCase__    ):
        __lowerCamelCase          =  (i - 1) // (config['''num_res_blocks'''] + 1)
        __lowerCamelCase          =  (i - 1) % (config['''num_res_blocks'''] + 1)

        __lowerCamelCase          =  [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        __lowerCamelCase          =  [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            __lowerCamelCase          =  checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            __lowerCamelCase          =  checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    )
        __lowerCamelCase          =  {'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        __lowerCamelCase          =  {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
        assign_to_checkpoint(
            UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  additional_replacements=[meta_path, resnet_op]    ,  config=UpperCamelCase__    )

        if len(UpperCamelCase__    ):
            __lowerCamelCase          =  renew_attention_paths(UpperCamelCase__    )
            __lowerCamelCase          =  {
                '''old''': f"""input_blocks.{i}.1""",
                '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            __lowerCamelCase          =  {
                f"""input_blocks.{i}.1.qkv.bias""": {
                    '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  additional_replacements=[meta_path]    ,  attention_paths_to_split=UpperCamelCase__    ,  config=UpperCamelCase__    ,  )

    __lowerCamelCase          =  middle_blocks[0]
    __lowerCamelCase          =  middle_blocks[1]
    __lowerCamelCase          =  middle_blocks[2]

    __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    )
    assign_to_checkpoint(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  config=UpperCamelCase__    )

    __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    )
    assign_to_checkpoint(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  config=UpperCamelCase__    )

    __lowerCamelCase          =  renew_attention_paths(UpperCamelCase__    )
    __lowerCamelCase          =  {
        '''middle_block.1.qkv.bias''': {
            '''key''': '''mid_block.attentions.0.key.bias''',
            '''query''': '''mid_block.attentions.0.query.bias''',
            '''value''': '''mid_block.attentions.0.value.bias''',
        },
        '''middle_block.1.qkv.weight''': {
            '''key''': '''mid_block.attentions.0.key.weight''',
            '''query''': '''mid_block.attentions.0.query.weight''',
            '''value''': '''mid_block.attentions.0.value.weight''',
        },
    }
    assign_to_checkpoint(
        UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  attention_paths_to_split=UpperCamelCase__    ,  config=UpperCamelCase__    )

    for i in range(UpperCamelCase__    ):
        __lowerCamelCase          =  i // (config['''num_res_blocks'''] + 1)
        __lowerCamelCase          =  i % (config['''num_res_blocks'''] + 1)
        __lowerCamelCase          =  [shave_segments(UpperCamelCase__    ,  2    ) for name in output_blocks[i]]
        __lowerCamelCase          =  {}

        for layer in output_block_layers:
            __lowerCamelCase     ,  __lowerCamelCase          =  layer.split('''.'''    )[0], shave_segments(UpperCamelCase__    ,  1    )
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase__    )
            else:
                __lowerCamelCase          =  [layer_name]

        if len(UpperCamelCase__    ) > 1:
            __lowerCamelCase          =  [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            __lowerCamelCase          =  [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    )
            __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    )

            __lowerCamelCase          =  {'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            assign_to_checkpoint(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  additional_replacements=[meta_path]    ,  config=UpperCamelCase__    )

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                __lowerCamelCase          =  list(output_block_list.values()    ).index(['''conv.weight''', '''conv.bias''']    )
                __lowerCamelCase          =  checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                __lowerCamelCase          =  checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase__    ) == 2:
                    __lowerCamelCase          =  []

            if len(UpperCamelCase__    ):
                __lowerCamelCase          =  renew_attention_paths(UpperCamelCase__    )
                __lowerCamelCase          =  {
                    '''old''': f"""output_blocks.{i}.1""",
                    '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                __lowerCamelCase          =  {
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  additional_replacements=[meta_path]    ,  attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions    ) else None    ,  config=UpperCamelCase__    ,  )
        else:
            __lowerCamelCase          =  renew_resnet_paths(UpperCamelCase__    ,  n_shave_prefix_segments=1    )
            for path in resnet_0_paths:
                __lowerCamelCase          =  '''.'''.join(['''output_blocks''', str(UpperCamelCase__    ), path['''old''']]    )
                __lowerCamelCase          =  '''.'''.join(['''up_blocks''', str(UpperCamelCase__    ), '''resnets''', str(UpperCamelCase__    ), path['''new''']]    )

                __lowerCamelCase          =  checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
  __UpperCAmelCase												=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.")

  __UpperCAmelCase												=parser.parse_args()

  __UpperCAmelCase												=torch.load(args.checkpoint_path)

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

  __UpperCAmelCase												=convert_ldm_checkpoint(checkpoint, config)

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

  __UpperCAmelCase												=UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __UpperCAmelCase												=DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
    __UpperCAmelCase												=VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

    __UpperCAmelCase												=LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)

'''simple docstring'''




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




class    a__				:






     def __init__(			self		:		List[Any]  ,    a		:		Tuple  ,    a		:		int  ,    a		:		int   ):





         """simple docstring"""



         if dst_width < 0 or dst_height < 0:
             raise ValueError('''Destination width/height should be > 0'''   )

         __lowerCamelCase          =  img
         __lowerCamelCase          =  img.shape[1]
         __lowerCamelCase          =  img.shape[0]
         __lowerCamelCase          =  dst_width
         __lowerCamelCase          =  dst_height

         __lowerCamelCase          =  self.src_w / self.dst_w
         __lowerCamelCase          =  self.src_h / self.dst_h

         __lowerCamelCase          =  __lowerCamelCase          =  (
             np.ones((self.dst_h, self.dst_w, 3)  ,    np.uinta   ) * 2_55
         )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for i in range(self.dst_h   ):
             for j in range(self.dst_w   ):
                 __lowerCamelCase          =  self.img[self.get_y(a   )][self.get_x(a   )]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_x * x   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_y * y   )


if __name__ == "__main__":
  __UpperCAmelCase							,	__UpperCAmelCase												=8_0_0, 6_0_0
  __UpperCAmelCase												=imread("image_data/lena.jpg", 1)
  __UpperCAmelCase												=NearestNeighbour(im, dst_w, dst_h)
  n.process()

  imshow(
      f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
  )
  waitKey(0)
  destroyAllWindows()
