

"""simple docstring"""




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def 		A    (       snake_case__						,			snake_case__=1      ):




   '''simple docstring'''


   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("""."""      )[n_shave_prefix_segments:]      )
   else:
      return ".".join(path.split("""."""      )[:n_shave_prefix_segments]      )

def 		A    (       snake_case__						,			snake_case__=0      ):




   '''simple docstring'''


   SCREAMING_SNAKE_CASE__									= []
   for old_item in old_list:
      SCREAMING_SNAKE_CASE__									= old_item
      SCREAMING_SNAKE_CASE__									= new_item.replace("""block."""						,			"""resnets."""      )
      SCREAMING_SNAKE_CASE__									= new_item.replace("""conv_shorcut"""						,			"""conv1"""      )
      SCREAMING_SNAKE_CASE__									= new_item.replace("""in_shortcut"""						,			"""conv_shortcut"""      )
      SCREAMING_SNAKE_CASE__									= new_item.replace("""temb_proj"""						,			"""time_emb_proj"""      )

      SCREAMING_SNAKE_CASE__									= shave_segments(snake_case__						,			n_shave_prefix_segments=snake_case__      )

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

   return mapping

def 		A    (       snake_case__						,			snake_case__=0						,			snake_case__=False      ):




   '''simple docstring'''


   SCREAMING_SNAKE_CASE__									= []
   for old_item in old_list:
      SCREAMING_SNAKE_CASE__									= old_item

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

      SCREAMING_SNAKE_CASE__									= new_item.replace("""proj_out"""						,			"""proj_attn"""      )
      SCREAMING_SNAKE_CASE__									= new_item.replace("""norm"""						,			"""group_norm"""      )

      SCREAMING_SNAKE_CASE__									= shave_segments(snake_case__						,			n_shave_prefix_segments=snake_case__      )
      mapping.append({"""old""": old_item, """new""": new_item}      )

   return mapping

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__=None						,			snake_case__=None						,			snake_case__=None      ):




   '''simple docstring'''


   assert isinstance(snake_case__						,			snake_case__      ), "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():
         SCREAMING_SNAKE_CASE__									= old_checkpoint[path]
         SCREAMING_SNAKE_CASE__									= old_tensor.shape[0] // 3

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

         SCREAMING_SNAKE_CASE__									= old_tensor.shape[0] // config.get("""num_head_channels"""						,			1      ) // 3

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

         SCREAMING_SNAKE_CASE__									= query.reshape(snake_case__      ).squeeze()
         SCREAMING_SNAKE_CASE__									= key.reshape(snake_case__      ).squeeze()
         SCREAMING_SNAKE_CASE__									= value.reshape(snake_case__      ).squeeze()

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

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      SCREAMING_SNAKE_CASE__									= new_path.replace("""down."""						,			"""down_blocks."""      )
      SCREAMING_SNAKE_CASE__									= new_path.replace("""up."""						,			"""up_blocks."""      )

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

      if "attentions" in new_path:
         SCREAMING_SNAKE_CASE__									= old_checkpoint[path['old']].squeeze()
      else:
         SCREAMING_SNAKE_CASE__									= old_checkpoint[path['old']]

def 		A    (       snake_case__						,			snake_case__      ):




   '''simple docstring'''


   SCREAMING_SNAKE_CASE__									= {}

   SCREAMING_SNAKE_CASE__									= checkpoint['temb.dense.0.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['temb.dense.0.bias']
   SCREAMING_SNAKE_CASE__									= checkpoint['temb.dense.1.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['temb.dense.1.bias']

   SCREAMING_SNAKE_CASE__									= checkpoint['norm_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['norm_out.bias']

   SCREAMING_SNAKE_CASE__									= checkpoint['conv_in.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['conv_in.bias']
   SCREAMING_SNAKE_CASE__									= checkpoint['conv_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['conv_out.bias']

   SCREAMING_SNAKE_CASE__									= len({""".""".join(layer.split("""."""      )[:2]      ) for layer in checkpoint if """down""" in layer}      )
   SCREAMING_SNAKE_CASE__									= {
       layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(snake_case__      )
   }

   SCREAMING_SNAKE_CASE__									= len({""".""".join(layer.split("""."""      )[:2]      ) for layer in checkpoint if """up""" in layer}      )
   SCREAMING_SNAKE_CASE__									= {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(snake_case__      )}

   for i in range(snake_case__      ):
      SCREAMING_SNAKE_CASE__									= (i - 1) // (config['layers_per_block'] + 1)

      if any("""downsample""" in layer for layer in down_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""down.{i}.downsample.op.weight"""
         ]
         SCREAMING_SNAKE_CASE__									= 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]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			2      ).split("""."""      )[:2]      ) for layer in down_blocks[i] if """block""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
             for layer_id in range(snake_case__      )
         }

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

      if any("""attn""" in layer for layer in down_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			2      ).split("""."""      )[:2]      ) for layer in down_blocks[i] if """attn""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
             for layer_id in range(snake_case__      )
         }

         if num_attn > 0:
            for j in range(config["""layers_per_block"""]      ):
               SCREAMING_SNAKE_CASE__									= renew_attention_paths(attns[j]      )
               assign_to_checkpoint(snake_case__						,			snake_case__						,			snake_case__						,			config=snake_case__      )

   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.block_1' in key]
   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.block_2' in key]
   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.attn_1' in key]

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

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

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

   for i in range(snake_case__      ):
      SCREAMING_SNAKE_CASE__									= num_up_blocks - 1 - i

      if any("""upsample""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""up.{i}.upsample.conv.weight"""
         ]
         SCREAMING_SNAKE_CASE__									= checkpoint[f"""up.{i}.upsample.conv.bias"""]

      if any("""block""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			2      ).split("""."""      )[:2]      ) for layer in up_blocks[i] if """block""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(snake_case__      )
         }

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

      if any("""attn""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			2      ).split("""."""      )[:2]      ) for layer in up_blocks[i] if """attn""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(snake_case__      )
         }

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

   SCREAMING_SNAKE_CASE__									= {k.replace("""mid_new_2"""						,			"""mid_block"""      ): v for k, v in new_checkpoint.items()}
   return new_checkpoint

def 		A    (       snake_case__						,			snake_case__      ):




   '''simple docstring'''


   SCREAMING_SNAKE_CASE__									= {}

   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.norm_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.norm_out.bias']

   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.conv_in.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.conv_in.bias']
   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.conv_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['encoder.conv_out.bias']

   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.norm_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.norm_out.bias']

   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.conv_in.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.conv_in.bias']
   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.conv_out.weight']
   SCREAMING_SNAKE_CASE__									= checkpoint['decoder.conv_out.bias']

   SCREAMING_SNAKE_CASE__									= len({""".""".join(layer.split("""."""      )[:3]      ) for layer in checkpoint if """down""" in layer}      )
   SCREAMING_SNAKE_CASE__									= {
       layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(snake_case__      )
   }

   SCREAMING_SNAKE_CASE__									= len({""".""".join(layer.split("""."""      )[:3]      ) for layer in checkpoint if """up""" in layer}      )
   SCREAMING_SNAKE_CASE__									= {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(snake_case__      )}

   for i in range(snake_case__      ):
      SCREAMING_SNAKE_CASE__									= (i - 1) // (config['layers_per_block'] + 1)

      if any("""downsample""" in layer for layer in down_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""encoder.down.{i}.downsample.conv.weight"""
         ]
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""encoder.down.{i}.downsample.conv.bias"""
         ]

      if any("""block""" in layer for layer in down_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			3      ).split("""."""      )[:3]      ) for layer in down_blocks[i] if """block""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
             for layer_id in range(snake_case__      )
         }

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

      if any("""attn""" in layer for layer in down_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			3      ).split("""."""      )[:3]      ) for layer in down_blocks[i] if """attn""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
             for layer_id in range(snake_case__      )
         }

         if num_attn > 0:
            for j in range(config["""layers_per_block"""]      ):
               SCREAMING_SNAKE_CASE__									= renew_attention_paths(attns[j]      )
               assign_to_checkpoint(snake_case__						,			snake_case__						,			snake_case__						,			config=snake_case__      )

   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.block_1' in key]
   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.block_2' in key]
   SCREAMING_SNAKE_CASE__									= [key for key in checkpoint if 'mid.attn_1' in key]

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

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

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

   for i in range(snake_case__      ):
      SCREAMING_SNAKE_CASE__									= num_up_blocks - 1 - i

      if any("""upsample""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""decoder.up.{i}.upsample.conv.weight"""
         ]
         SCREAMING_SNAKE_CASE__									= checkpoint[
             f"""decoder.up.{i}.upsample.conv.bias"""
         ]

      if any("""block""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			3      ).split("""."""      )[:3]      ) for layer in up_blocks[i] if """block""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(snake_case__      )
         }

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

      if any("""attn""" in layer for layer in up_blocks[i]      ):
         SCREAMING_SNAKE_CASE__									= len(
             {""".""".join(shave_segments(snake_case__						,			3      ).split("""."""      )[:3]      ) for layer in up_blocks[i] if """attn""" in layer}      )
         SCREAMING_SNAKE_CASE__									= {
             layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(snake_case__      )
         }

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

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

   return new_checkpoint


if __name__ == "__main__":
      A_							:     List[str]     =	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_							:     int     =	parser.parse_args()
      A_							:     Tuple     =	torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            A_							:     Optional[int]     =	json.loads(f.read())

      # unet case
      A_							:     int     =	{key.split(".")[0] for key in checkpoint.keys()}
      if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
            A_							:     Dict     =	convert_vq_autoenc_checkpoint(checkpoint, config)
      else:
            A_							:     int     =	convert_ddpm_checkpoint(checkpoint, config)

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

      if config["_class_name"] == "VQModel":
            A_							:     List[str]     =	VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            A_							:     Optional[int]     =	AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            A_							:     int     =	UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

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

            A_							:     List[Any]     =	DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)





"""simple docstring"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
						return self.sequence[:index]

def 		A    (       ):




			'''simple docstring'''


			print(
			    """Fibonacci Series Using Dynamic Programming\n"""						,			"""Enter the index of the Fibonacci number you want to calculate """						,			"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""						,			sep=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

			while True:
						SCREAMING_SNAKE_CASE__									= input(""">> """      )
						if prompt in {"exit", "quit"}:
									break

						try:
									SCREAMING_SNAKE_CASE__									= int(snake_case__      )
						except ValueError:
									print("""Enter a number or 'exit'"""      )
									continue

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


