


'''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)







'''simple docstring'''






from math import gcd




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 1_20_00	)     ->			int:







   """simple docstring"""
   __snake_case					:   Dict  				=			0
   for d in range(max_d + 1	):
      for n in range(d // 3 + 1	,		(d + 1) // 2	):
         if gcd(A	,		A	) == 1:
            fractions_number += 1
   return fractions_number


if __name__ == "__main__":
      print(f'''{solution() = }''')