






"""simple docstring"""







import argparse

import torch

from diffusers import UNetaDConditionModel






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None		,					lowerCAmelCase=None		,					lowerCAmelCase=None						):







       '''simple docstring'''
       assert isinstance(lowerCAmelCase		,					lowerCAmelCase						), "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					=	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["""num_head_channels"""] // 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(lowerCAmelCase						)
                     UpperCAmelCase					=	key.reshape(lowerCAmelCase						)
                     UpperCAmelCase					=	value.reshape(lowerCAmelCase						)

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

              # These have already been assigned
              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
              UpperCAmelCase					=	old_checkpoint[path["""old"""]]
              UpperCAmelCase					=	["""proj_attn.weight"""]
              UpperCAmelCase					=	["""proj_out.weight""", """proj_in.weight"""]
              if any(k in new_path for k in names						):
                     UpperCAmelCase					=	weight[:, :, 0]
              elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
                     UpperCAmelCase					=	weight[:, :, 0]
              else:
                     UpperCAmelCase					=	weight






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              UpperCAmelCase					=	old_item

              #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
              #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

              #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
              #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

              #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=1						):







       '''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 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              mapping.append({"""old""": old_item, """new""": old_item}						)

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              UpperCAmelCase					=	old_item.replace("""in_layers.0"""		,					"""norm1"""						)
              UpperCAmelCase					=	new_item.replace("""in_layers.2"""		,					"""conv1"""						)

              UpperCAmelCase					=	new_item.replace("""out_layers.0"""		,					"""norm2"""						)
              UpperCAmelCase					=	new_item.replace("""out_layers.3"""		,					"""conv2"""						)

              UpperCAmelCase					=	new_item.replace("""emb_layers.1"""		,					"""time_emb_proj"""						)
              UpperCAmelCase					=	new_item.replace("""skip_connection"""		,					"""conv_shortcut"""						)

              UpperCAmelCase					=	shave_segments(lowerCAmelCase		,					n_shave_prefix_segments=lowerCAmelCase						)

              if "temopral_conv" not in old_item:
                     mapping.append({"""old""": old_item, """new""": new_item}						)

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None		,					lowerCAmelCase=False						):







       '''simple docstring'''
       UpperCAmelCase					=	{}
       UpperCAmelCase					=	list(checkpoint.keys()						)

       UpperCAmelCase					=	"""model.diffusion_model."""

       # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
       if sum(k.startswith("""model_ema"""						) for k in keys						) > 100 and extract_ema:
              print(F'''Checkpoint {path} has both EMA and non-EMA weights.'''						)
              print(
                  """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
                  """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""						)
              for key in keys:
                     if key.startswith("""model.diffusion_model"""						):
                            UpperCAmelCase					=	"""model_ema.""" + """""".join(key.split("""."""						)[1:]						)
                            UpperCAmelCase					=	checkpoint.pop(lowerCAmelCase						)
       else:
              if sum(k.startswith("""model_ema"""						) for k in keys						) > 100:
                     print(
                         """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
                         """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""						)

              for key in keys:
                     UpperCAmelCase					=	checkpoint.pop(lowerCAmelCase						)

       UpperCAmelCase					=	{}

       UpperCAmelCase					=	unet_state_dict["""time_embed.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.0.bias"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.2.weight"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.2.bias"""]

       if config["class_embed_type"] is None:
              # No parameters to port
              ...
       elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.0.weight"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.0.bias"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.2.weight"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.2.bias"""]
       else:
              raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''						)

       UpperCAmelCase					=	unet_state_dict["""input_blocks.0.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""input_blocks.0.0.bias"""]

       UpperCAmelCase					=	[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""						)]
       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
       assign_to_checkpoint(lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	unet_state_dict["""out.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""out.0.bias"""]
       UpperCAmelCase					=	unet_state_dict["""out.2.weight"""]
       UpperCAmelCase					=	unet_state_dict["""out.2.bias"""]

       # Retrieves the keys for the input blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """input_blocks""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       # Retrieves the keys for the middle blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """middle_block""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       # Retrieves the keys for the output blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """output_blocks""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       for i in range(1		,					lowerCAmelCase						):
              UpperCAmelCase					=	(i - 1) // (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	(i - 1) % (config["""layers_per_block"""] + 1)

              UpperCAmelCase					=	[
                  key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
              ]
              UpperCAmelCase					=	[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
              UpperCAmelCase					=	[key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]

              if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
                     UpperCAmelCase					=	unet_state_dict.pop(
                         F'''input_blocks.{i}.op.weight'''						)
                     UpperCAmelCase					=	unet_state_dict.pop(
                         F'''input_blocks.{i}.op.bias'''						)

              UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
              UpperCAmelCase					=	{"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
              assign_to_checkpoint(
                  lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              UpperCAmelCase					=	[key for key in resnets if """temopral_conv""" in key]
              UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
              UpperCAmelCase					=	{
                  """old""": F'''input_blocks.{i}.0.temopral_conv''',
                  """new""": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              if len(lowerCAmelCase						):
                     UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{"""old""": F'''input_blocks.{i}.1''', """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              if len(lowerCAmelCase						):
                     UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{
                         """old""": F'''input_blocks.{i}.2''',
                         """new""": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                     }
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	middle_blocks[0]
       UpperCAmelCase					=	[key for key in resnet_a if """temopral_conv""" in key]
       UpperCAmelCase					=	middle_blocks[1]
       UpperCAmelCase					=	middle_blocks[2]
       UpperCAmelCase					=	middle_blocks[3]
       UpperCAmelCase					=	[key for key in resnet_a if """temopral_conv""" in key]

       UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       for i in range(lowerCAmelCase						):
              UpperCAmelCase					=	i // (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	i % (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	[shave_segments(lowerCAmelCase		,					2						) for name in output_blocks[i]]
              UpperCAmelCase					=	{}

              for layer in output_block_layers:
                     UpperCAmelCase			,				UpperCAmelCase					=	layer.split("""."""						)[0], shave_segments(lowerCAmelCase		,					1						)
                     if layer_id in output_block_list:
                            output_block_list[layer_id].append(lowerCAmelCase						)
                     else:
                            UpperCAmelCase					=	[layer_name]

              if len(lowerCAmelCase						) > 1:
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)

                     UpperCAmelCase					=	{"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     UpperCAmelCase					=	[key for key in resnets if """temopral_conv""" in key]
                     UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{
                         """old""": F'''output_blocks.{i}.0.temopral_conv''',
                         """new""": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
                     }
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     UpperCAmelCase					=	{k: sorted(lowerCAmelCase						) for k, v in output_block_list.items()}
                     if ["conv.bias", "conv.weight"] in output_block_list.values():
                            UpperCAmelCase					=	list(output_block_list.values()						).index(["""conv.bias""", """conv.weight"""]						)
                            UpperCAmelCase					=	unet_state_dict[
                                F'''output_blocks.{i}.{index}.conv.weight'''
                            ]
                            UpperCAmelCase					=	unet_state_dict[
                                F'''output_blocks.{i}.{index}.conv.bias'''
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(lowerCAmelCase						) == 2:
                                   UpperCAmelCase					=	[]

                     if len(lowerCAmelCase						):
                            UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                            UpperCAmelCase					=	{
                                """old""": F'''output_blocks.{i}.1''',
                                """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                            }
                            assign_to_checkpoint(
                                lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     if len(lowerCAmelCase						):
                            UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                            UpperCAmelCase					=	{
                                """old""": F'''output_blocks.{i}.2''',
                                """new""": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                            }
                            assign_to_checkpoint(
                                lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)
              else:
                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase		,					n_shave_prefix_segments=1						)
                     for path in resnet_0_paths:
                            UpperCAmelCase					=	""".""".join(["""output_blocks""", str(lowerCAmelCase						), path["""old"""]]						)
                            UpperCAmelCase					=	""".""".join(["""up_blocks""", str(lowerCAmelCase						), """resnets""", str(lowerCAmelCase						), path["""new"""]]						)
                            UpperCAmelCase					=	unet_state_dict[old_path]

                     UpperCAmelCase					=	[l for l in output_block_layers if """temopral_conv""" in l]
                     for path in temopral_conv_paths:
                            UpperCAmelCase					=	path.split("""temopral_conv."""						)[-1]
                            UpperCAmelCase					=	""".""".join(["""output_blocks""", str(lowerCAmelCase						), str(lowerCAmelCase						), """temopral_conv""", pruned_path]						)
                            UpperCAmelCase					=	""".""".join(["""up_blocks""", str(lowerCAmelCase						), """temp_convs""", str(lowerCAmelCase						), pruned_path]						)
                            UpperCAmelCase					=	unet_state_dict[old_path]

       return new_checkpoint


if __name__ == "__main__":
 lowerCAmelCase_		:			List[str]				   =					argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )
 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 lowerCAmelCase_		:			List[Any]				   =					parser.parse_args()

 lowerCAmelCase_		:			Any				   =					torch.load(args.checkpoint_path, map_location='''cpu''')
 lowerCAmelCase_		:			Any				   =					UNetaDConditionModel()

 lowerCAmelCase_		:			Optional[int]				   =					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 lowerCAmelCase_		:			Any				   =					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 lowerCAmelCase_		:			Any				   =					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

 assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

 # load state_dict
 unet.load_state_dict(converted_ckpt)

 unet.save_pretrained(args.dump_path)

 # -- finish converting the unet --







"""simple docstring"""





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= c_float

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       return sizeof(self.value						)







def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 if dimension <= 0:
  __SCREAMING_SNAKE_CASE :			str												=       fixed_dimension

 dimension -= num_token_to_add
 return dimension




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							ParameterFormat		):

 '''simple docstring'''






 return num_parameters * dtype.size






def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(lowercase_		)
 except (ValueError, OSError, KeyError):
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       None, None
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoTokenizer.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass

  if tokenizer is not None and feature_extractor is not None:
   raise ValueError(
       F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''		)
  elif tokenizer is None and feature_extractor is None:
   return None
  elif tokenizer is not None:
   return tokenizer
  else:
   return feature_extractor

