import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=1       )					->Dict:

  '''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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=0       )					->Dict:

  '''simple docstring'''




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

    a__  		=  shave_segments(A__			,					n_shave_prefix_segments=A__       )

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

  return mapping
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=0			,					lowerCamelCase__=False       )					->int:

  '''simple docstring'''




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

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

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

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

  return mapping
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None			,					lowerCamelCase__=None			,					lowerCamelCase__=None       )					->List[str]:

  '''simple docstring'''




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

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

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

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

      a__  		=  query.reshape(A__       ).squeeze()
      a__  		=  key.reshape(A__       ).squeeze()
      a__  		=  value.reshape(A__       ).squeeze()

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

    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

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

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

    if "attentions" in new_path:
      a__  		=  old_checkpoint[path["""old"""]].squeeze()
    else:
      a__  		=  old_checkpoint[path["""old"""]]
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  for i in range(A__       ):
    a__  		=  num_up_blocks - 1 - i

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

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

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

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

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

  a__  		=  {k.replace("mid_new_2"			,					"mid_block"       ): v for k, v in new_checkpoint.items()}
  return new_checkpoint
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  {}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  for i in range(A__       ):
    a__  		=  num_up_blocks - 1 - i

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

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

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

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

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

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

  return new_checkpoint


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     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.""")

     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     _SCREAMING_SNAKE_CASE     =     torch.load(args.checkpoint_path)

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

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

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

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

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

          _SCREAMING_SNAKE_CASE     =     DDPMPipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   10

					if only_cross_attention:
						_snake_case					:							Tuple					     =   12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						_snake_case					:							Any					     =   query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   2

					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	3						,	2      )
					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	4						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					torch.manual_seed(0      )

					_snake_case					:							Optional[int]					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is not None      )
					self.assertTrue(attn.to_v is not None      )

					_snake_case					:							Dict					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					# only self attention

					torch.manual_seed(0      )

					_snake_case					:							Any					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is None      )
					self.assertTrue(attn.to_v is None      )

					_snake_case					:							str					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Tuple					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					# the initial conversion succeeds
					_snake_case					:							List[str]					     =   DiffusionPipeline.from_pretrained(
					    """hf-internal-testing/tiny-stable-diffusion-pipe"""						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Union[str, Any]					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(SCREAMING_SNAKE_CASE__      )

						# can also load the converted weights
						_snake_case					:							str					     =   DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							int					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )





