code: def 		snake_case__  (		lowercase = "mytext.txt"       ,							lowercase = False				):
     import socket
  
     lowerCAmelCase_:       Dict         =  12312  # Reserve a port for your service.
     lowerCAmelCase_:       Optional[int]         =  socket.socket()  # Create a socket object
     lowerCAmelCase_:       Dict         =  socket.gethostname()  # Get local machine name
     sock.bind((host, port)				)  # Bind to the port
     sock.listen(5				)  # Now wait for client connection.
  
     print("Server listening...."				)
  
     while True:
        lowerCAmelCase_:       Any         =  sock.accept()  # Establish connection with client.
        print(F'''Got connection from {addr}'''				)
        lowerCAmelCase_:       Optional[Any]         =  conn.recv(1024				)
        print(F'''Server received: {data = }'''				)
  
        with open(_UpperCAmelCase       ,							"rb"				) as in_file:
           lowerCAmelCase_:       Any         =  in_file.read(1024				)
           while data:
              conn.send(_UpperCAmelCase				)
              print(F'''Sent {data!r}'''				)
              lowerCAmelCase_:       Optional[int]         =  in_file.read(1024				)
  
        print("Done sending"				)
        conn.close()
        if testing:  # Allow the test to complete
           break
  
     sock.shutdown(1				)
     sock.close()
  
  
  if __name__ == "__main__":
   send_file()
style_context: 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)