


'''simple docstring'''

import argparse
import json

import torch

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



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					str=1 )		->       Optional[Any]:





   '''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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Optional[Any]=0 )		->       List[Any]:





   '''simple docstring'''







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

      UpperCamelCase__			    =    shave_segments(_UpperCamelCase     ,		n_shave_prefix_segments=_UpperCamelCase )

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

   return mapping



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Tuple=0     ,		_UpperCamelCase   :					Dict=False )		->       Any:





   '''simple docstring'''







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

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

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

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

   return mapping



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Any=None     ,		_UpperCamelCase   :					Dict=None     ,		_UpperCamelCase   :					Optional[int]=None )		->       List[Any]:





   '''simple docstring'''







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

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

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

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

         UpperCamelCase__			    =    query.reshape(_UpperCamelCase ).squeeze()
         UpperCamelCase__			    =    key.reshape(_UpperCamelCase ).squeeze()
         UpperCamelCase__			    =    value.reshape(_UpperCamelCase ).squeeze()

   for path in paths:
      UpperCamelCase__			    =    path["new"]

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

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

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

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



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Optional[Any] )		->       Tuple:





   '''simple docstring'''







   UpperCamelCase__			    =    {}

   UpperCamelCase__			    =    checkpoint["temb.dense.0.weight"]
   UpperCamelCase__			    =    checkpoint["temb.dense.0.bias"]
   UpperCamelCase__			    =    checkpoint["temb.dense.1.weight"]
   UpperCamelCase__			    =    checkpoint["temb.dense.1.bias"]

   UpperCamelCase__			    =    checkpoint["norm_out.weight"]
   UpperCamelCase__			    =    checkpoint["norm_out.bias"]

   UpperCamelCase__			    =    checkpoint["conv_in.weight"]
   UpperCamelCase__			    =    checkpoint["conv_in.bias"]
   UpperCamelCase__			    =    checkpoint["conv_out.weight"]
   UpperCamelCase__			    =    checkpoint["conv_out.bias"]

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

   UpperCamelCase__			    =    len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "up" in layer} )
   UpperCamelCase__			    =    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_UpperCamelCase )}

   for i in range(_UpperCamelCase ):
      UpperCamelCase__			    =    (i - 1) // (config["layers_per_block"] + 1)

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

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

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

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

   UpperCamelCase__			    =    [key for key in checkpoint if "mid.block_1" in key]
   UpperCamelCase__			    =    [key for key in checkpoint if "mid.block_2" in key]
   UpperCamelCase__			    =    [key for key in checkpoint if "mid.attn_1" in key]

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

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

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

   for i in range(_UpperCamelCase ):
      UpperCamelCase__			    =    num_up_blocks - 1 - i

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

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

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

      if any("attn" in layer for layer in up_blocks[i] ):
         UpperCamelCase__			    =    len(
             {".".join(shave_segments(_UpperCamelCase     ,		2 ).split("." )[:2] ) for layer in up_blocks[i] if "attn" in layer} )
         UpperCamelCase__			    =    {
             layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_UpperCamelCase )
         }

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

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



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[Any]     ,		_UpperCamelCase   :					List[str] )		->       Optional[int]:





   '''simple docstring'''







   UpperCamelCase__			    =    {}

   UpperCamelCase__			    =    checkpoint["encoder.norm_out.weight"]
   UpperCamelCase__			    =    checkpoint["encoder.norm_out.bias"]

   UpperCamelCase__			    =    checkpoint["encoder.conv_in.weight"]
   UpperCamelCase__			    =    checkpoint["encoder.conv_in.bias"]
   UpperCamelCase__			    =    checkpoint["encoder.conv_out.weight"]
   UpperCamelCase__			    =    checkpoint["encoder.conv_out.bias"]

   UpperCamelCase__			    =    checkpoint["decoder.norm_out.weight"]
   UpperCamelCase__			    =    checkpoint["decoder.norm_out.bias"]

   UpperCamelCase__			    =    checkpoint["decoder.conv_in.weight"]
   UpperCamelCase__			    =    checkpoint["decoder.conv_in.bias"]
   UpperCamelCase__			    =    checkpoint["decoder.conv_out.weight"]
   UpperCamelCase__			    =    checkpoint["decoder.conv_out.bias"]

   UpperCamelCase__			    =    len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "down" in layer} )
   UpperCamelCase__			    =    {
       layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_UpperCamelCase )
   }

   UpperCamelCase__			    =    len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "up" in layer} )
   UpperCamelCase__			    =    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_UpperCamelCase )}

   for i in range(_UpperCamelCase ):
      UpperCamelCase__			    =    (i - 1) // (config["layers_per_block"] + 1)

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

      if any("block" in layer for layer in down_blocks[i] ):
         UpperCamelCase__			    =    len(
             {".".join(shave_segments(_UpperCamelCase     ,		3 ).split("." )[:3] ) for layer in down_blocks[i] if "block" in layer} )
         UpperCamelCase__			    =    {
             layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
             for layer_id in range(_UpperCamelCase )
         }

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

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

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

   UpperCamelCase__			    =    [key for key in checkpoint if "mid.block_1" in key]
   UpperCamelCase__			    =    [key for key in checkpoint if "mid.block_2" in key]
   UpperCamelCase__			    =    [key for key in checkpoint if "mid.attn_1" in key]

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

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

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

   for i in range(_UpperCamelCase ):
      UpperCamelCase__			    =    num_up_blocks - 1 - i

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

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

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

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

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

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

   return new_checkpoint


if __name__ == "__main__":
       __lowercase:		Optional[int]    						=  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.")

       __lowercase:		Any    						=  parser.parse_args()
       __lowercase:		Dict    						=  torch.load(args.checkpoint_path)

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

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

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

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

              __lowercase:		Optional[Any]    						=  DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

              __lowercase:		Tuple    						=  DDPMPipeline(unet=model, scheduler=scheduler)
              pipe.save_pretrained(args.dump_path)




UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

