






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 				A		(					lowercase__  :  Any ,		lowercase__  :  List[str]=1  )		-> Union[str, Any]:




	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		(					lowercase__  :  List[str] ,		lowercase__  :  str=0  )		-> Dict:
	UpperCamelCase__ :Optional[int]						=      []
	for old_item in old_list:
		UpperCamelCase__ :Union[str, Any]						=      old_item.replace("""in_layers.0""" ,		"""norm1"""  )
		UpperCamelCase__ :Any						=      new_item.replace("""in_layers.2""" ,		"""conv1"""  )

		UpperCamelCase__ :Any						=      new_item.replace("""out_layers.0""" ,		"""norm2"""  )
		UpperCamelCase__ :Optional[int]						=      new_item.replace("""out_layers.3""" ,		"""conv2"""  )

		UpperCamelCase__ :Tuple						=      new_item.replace("""emb_layers.1""" ,		"""time_emb_proj"""  )
		UpperCamelCase__ :Union[str, Any]						=      new_item.replace("""skip_connection""" ,		"""conv_shortcut"""  )

		UpperCamelCase__ :Optional[Any]						=      shave_segments(_UpperCamelCase ,		n_shave_prefix_segments=_UpperCamelCase  )

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

	return mapping




def 				A		(					lowercase__  :  Any ,		lowercase__  :  Optional[int]=0  )		-> List[Any]:
	UpperCamelCase__ :int						=      []
	for old_item in old_list:
		UpperCamelCase__ :Optional[Any]						=      old_item

		UpperCamelCase__ :str						=      new_item.replace("""norm.weight""" ,		"""group_norm.weight"""  )
		UpperCamelCase__ :Optional[Any]						=      new_item.replace("""norm.bias""" ,		"""group_norm.bias"""  )

		UpperCamelCase__ :Dict						=      new_item.replace("""proj_out.weight""" ,		"""proj_attn.weight"""  )
		UpperCamelCase__ :Optional[Any]						=      new_item.replace("""proj_out.bias""" ,		"""proj_attn.bias"""  )

		UpperCamelCase__ :Optional[Any]						=      shave_segments(_UpperCamelCase ,		n_shave_prefix_segments=_UpperCamelCase  )

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

	return mapping




def 				A		(					lowercase__  :  Tuple ,		lowercase__  :  List[Any] ,		lowercase__  :  str ,		lowercase__  :  Optional[Any]=None ,		lowercase__  :  List[str]=None ,		lowercase__  :  int=None  )		-> Optional[int]:
	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():
			UpperCamelCase__ :Union[str, Any]						=      old_checkpoint[path]
			UpperCamelCase__ :Optional[Any]						=      old_tensor.shape[0] // 3

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

			UpperCamelCase__ :Union[str, Any]						=      old_tensor.shape[0] // config["num_head_channels"] // 3

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

			UpperCamelCase__ :Union[str, Any]						=      query.reshape(_UpperCamelCase  )
			UpperCamelCase__ :str						=      key.reshape(_UpperCamelCase  )
			UpperCamelCase__ :Dict						=      value.reshape(_UpperCamelCase  )

	for path in paths:
		UpperCamelCase__ :Union[str, Any]						=      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
		UpperCamelCase__ :List[str]						=      new_path.replace("""middle_block.0""" ,		"""mid_block.resnets.0"""  )
		UpperCamelCase__ :Dict						=      new_path.replace("""middle_block.1""" ,		"""mid_block.attentions.0"""  )
		UpperCamelCase__ :List[Any]						=      new_path.replace("""middle_block.2""" ,		"""mid_block.resnets.1"""  )

		if additional_replacements is not None:
			for replacement in additional_replacements:
				UpperCamelCase__ :Optional[int]						=      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:
			UpperCamelCase__ :Optional[Any]						=      old_checkpoint[path["old"]][:, :, 0]
		else:
			UpperCamelCase__ :Optional[int]						=      old_checkpoint[path["old"]]




def 				A		(					lowercase__  :  Dict ,		lowercase__  :  Any  )		-> List[str]:
	UpperCamelCase__ :Union[str, Any]						=      {}

	UpperCamelCase__ :Union[str, Any]						=      checkpoint["time_embed.0.weight"]
	UpperCamelCase__ :str						=      checkpoint["time_embed.0.bias"]
	UpperCamelCase__ :str						=      checkpoint["time_embed.2.weight"]
	UpperCamelCase__ :Union[str, Any]						=      checkpoint["time_embed.2.bias"]

	UpperCamelCase__ :List[Any]						=      checkpoint["input_blocks.0.0.weight"]
	UpperCamelCase__ :Any						=      checkpoint["input_blocks.0.0.bias"]

	UpperCamelCase__ :List[Any]						=      checkpoint["out.0.weight"]
	UpperCamelCase__ :Any						=      checkpoint["out.0.bias"]
	UpperCamelCase__ :Tuple						=      checkpoint["out.2.weight"]
	UpperCamelCase__ :Optional[Any]						=      checkpoint["out.2.bias"]

	# Retrieves the keys for the input blocks only
	UpperCamelCase__ :Any						=      len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """input_blocks""" in layer}  )
	UpperCamelCase__ :List[Any]						=      {
	    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
	UpperCamelCase__ :List[Any]						=      len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """middle_block""" in layer}  )
	UpperCamelCase__ :Union[str, Any]						=      {
	    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
	UpperCamelCase__ :List[Any]						=      len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """output_blocks""" in layer}  )
	UpperCamelCase__ :Dict						=      {
	    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  ):
		UpperCamelCase__ :List[Any]						=      (i - 1) // (config["num_res_blocks"] + 1)
		UpperCamelCase__ :Any						=      (i - 1) % (config["num_res_blocks"] + 1)

		UpperCamelCase__ :Dict						=      [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
		UpperCamelCase__ :str						=      [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:
			UpperCamelCase__ :int						=      checkpoint[
			    f"""input_blocks.{i}.0.op.weight"""
			]
			UpperCamelCase__ :Any						=      checkpoint[
			    f"""input_blocks.{i}.0.op.bias"""
			]
			continue

		UpperCamelCase__ :Union[str, Any]						=      renew_resnet_paths(_UpperCamelCase  )
		UpperCamelCase__ :Union[str, Any]						=      {"old": f"""input_blocks.{i}.0""", "new": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
		UpperCamelCase__ :Any						=      {"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  ):
			UpperCamelCase__ :Tuple						=      renew_attention_paths(_UpperCamelCase  )
			UpperCamelCase__ :Dict						=      {
			    "old": f"""input_blocks.{i}.1""",
			    "new": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
			}
			UpperCamelCase__ :List[Any]						=      {
			    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 ,		)

	UpperCamelCase__ :Optional[Any]						=      middle_blocks[0]
	UpperCamelCase__ :Optional[Any]						=      middle_blocks[1]
	UpperCamelCase__ :Any						=      middle_blocks[2]

	UpperCamelCase__ :int						=      renew_resnet_paths(_UpperCamelCase  )
	assign_to_checkpoint(_UpperCamelCase ,		_UpperCamelCase ,		_UpperCamelCase ,		config=_UpperCamelCase  )

	UpperCamelCase__ :Optional[int]						=      renew_resnet_paths(_UpperCamelCase  )
	assign_to_checkpoint(_UpperCamelCase ,		_UpperCamelCase ,		_UpperCamelCase ,		config=_UpperCamelCase  )

	UpperCamelCase__ :Optional[int]						=      renew_attention_paths(_UpperCamelCase  )
	UpperCamelCase__ :Union[str, Any]						=      {
	    "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  ):
		UpperCamelCase__ :int						=      i // (config["num_res_blocks"] + 1)
		UpperCamelCase__ :str						=      i % (config["num_res_blocks"] + 1)
		UpperCamelCase__ :List[Any]						=      [shave_segments(_UpperCamelCase ,		2  ) for name in output_blocks[i]]
		UpperCamelCase__ :str						=      {}

		for layer in output_block_layers:
			UpperCamelCase__ :int						=      layer.split("""."""  )[0], shave_segments(_UpperCamelCase ,		1  )
			if layer_id in output_block_list:
				output_block_list[layer_id].append(_UpperCamelCase  )
			else:
				UpperCamelCase__ :Dict						=      [layer_name]

		if len(_UpperCamelCase  ) > 1:
			UpperCamelCase__ :Optional[int]						=      [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
			UpperCamelCase__ :List[str]						=      [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

			UpperCamelCase__ :Optional[int]						=      renew_resnet_paths(_UpperCamelCase  )
			UpperCamelCase__ :Tuple						=      renew_resnet_paths(_UpperCamelCase  )

			UpperCamelCase__ :Optional[Any]						=      {"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():
				UpperCamelCase__ :Dict						=      list(output_block_list.values()  ).index(["""conv.weight""", """conv.bias"""]  )
				UpperCamelCase__ :Union[str, Any]						=      checkpoint[
				    f"""output_blocks.{i}.{index}.conv.weight"""
				]
				UpperCamelCase__ :int						=      checkpoint[
				    f"""output_blocks.{i}.{index}.conv.bias"""
				]

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

			if len(_UpperCamelCase  ):
				UpperCamelCase__ :Optional[int]						=      renew_attention_paths(_UpperCamelCase  )
				UpperCamelCase__ :Any						=      {
				    "old": f"""output_blocks.{i}.1""",
				    "new": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
				}
				UpperCamelCase__ :Any						=      {
				    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:
			UpperCamelCase__ :Union[str, Any]						=      renew_resnet_paths(_UpperCamelCase ,		n_shave_prefix_segments=1  )
			for path in resnet_0_paths:
				UpperCamelCase__ :str						=      ".".join(["""output_blocks""", str(_UpperCamelCase  ), path["""old"""]]  )
				UpperCamelCase__ :Optional[int]						=      ".".join(["""up_blocks""", str(_UpperCamelCase  ), """resnets""", str(_UpperCamelCase  ), path["""new"""]]  )

				UpperCamelCase__ :List[str]						=      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 builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__			=      False
try:
 UpperCamelCase__			=      _is_package_available('google.colab')
except ModuleNotFoundError:
 pass


@input.register
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							str = None	,  a	:							list = []      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  0
       lowercase_						:     Dict							     =  choices
       lowercase_						:     List[str]							     =  prompt
       if sys.platform == "win32":
          lowercase_						:     List[Any]							     =  "*"
       else:
          lowercase_						:     Union[str, Any]							     =  "➔ "




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							str = ""      ):






       '''simple docstring'''






       if sys.platform != "win32":
          writeColor(self.choices[index]	,  3_2	,  a      )
       else:
          forceWrite(self.choices[index]	,  a      )




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






       '''simple docstring'''






       if index == self.position:
          forceWrite(f""" {self.arrow_char} """      )
          self.write_choice(a      )
       else:
          forceWrite(f"""    {self.choices[index]}"""      )
       reset_cursor()




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Direction	,  a	:							int = 1      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.position
       if direction == Direction.DOWN:
          if self.position + 1 >= len(self.choices      ):
             return
          self.position += num_spaces
       else:
          if self.position - 1 < 0:
             return
          self.position -= num_spaces
       clear_line()
       self.print_choice(a      )
       move_cursor(a	,  direction.name      )
       self.print_choice(self.position      )




    @input.mark(KEYMAP["up"]      )
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.move_direction(Direction.UP      )




    @input.mark(KEYMAP["down"]      )
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self.move_direction(Direction.DOWN      )




    @input.mark(KEYMAP["newline"]      )
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       return self.position




    @input.mark(KEYMAP["interrupt"]      )
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       raise KeyboardInterrupt




    @input.mark_multiple(*[KEYMAP[str(a      )] for number in range(1_0      )]      )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     str							     =  int(chr(self.current_selection      )      )
       lowercase_						:     Tuple							     =  index - self.position
       if index == self.position:
          return
       if index < len(self.choices      ):
          if self.position > index:
             self.move_direction(Direction.UP	,  -movement      )
          elif self.position < index:
             self.move_direction(Direction.DOWN	,  a      )
          else:
             return
       else:
          return





    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int = 0      ):






       '''simple docstring'''






       if self.prompt:
          linebreak()
          forceWrite(self.prompt	,  "\n"      )
          if in_colab:
             forceWrite("Please input a choice index (starting from 0), and press enter"	,  "\n"      )
          else:
             forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,  "\n"      )
       lowercase_						:     Dict							     =  default_choice
       for i in range(len(self.choices      )      ):
          self.print_choice(a      )
          forceWrite("\n"      )
       move_cursor(len(self.choices      ) - self.position	,  "UP"      )
       with cursor.hide():
          while True:
             if in_colab:
                try:
                   lowercase_						:     List[str]							     =  int(builtins.input()      )
                except ValueError:
                   lowercase_						:     List[Any]							     =  default_choice
             else:
                lowercase_						:     Dict							     =  self.handle_input()
             if choice is not None:
                reset_cursor()
                for _ in range(len(self.choices      ) + 1      ):
                   move_cursor(1	,  "UP"      )
                   clear_line()
                self.write_choice(a	,  "\n"      )
                return choice




