


'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	List[str]=1  )  ->			int:






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

              _a    : Dict     					=new_item.replace("""out_layers.0"""    ,"""norm2"""  )
              _a    : Dict     					=new_item.replace("""out_layers.3"""    ,"""conv2"""  )

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

              _a    : List[str]     					=shave_segments(__a    ,n_shave_prefix_segments=__a  )

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

       return mapping


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Union[str, Any]=0  )  ->			Dict:
       _a    : Optional[int]     					=[]
       for old_item in old_list:
              _a    : Tuple     					=old_item

              _a    : Any     					=new_item.replace("""norm.weight"""    ,"""group_norm.weight"""  )
              _a    : List[Any]     					=new_item.replace("""norm.bias"""    ,"""group_norm.bias"""  )

              _a    : Union[str, Any]     					=new_item.replace("""proj_out.weight"""    ,"""proj_attn.weight"""  )
              _a    : Tuple     					=new_item.replace("""proj_out.bias"""    ,"""proj_attn.bias"""  )

              _a    : Optional[int]     					=shave_segments(__a    ,n_shave_prefix_segments=__a  )

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

       return mapping


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	str    ,_UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Union[str, Any]=None    ,_UpperCAmelCase	:	List[str]=None    ,_UpperCAmelCase	:	int=None  )  ->			str:
       assert isinstance(__a    ,__a  ), "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():
                     _a    : Any     					=old_checkpoint[path]
                     _a    : List[str]     					=old_tensor.shape[0] // 3

                     _a    : Optional[Any]     					=(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

                     _a    : int     					=old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

                     _a    : Union[str, Any]     					=query.reshape(__a  )
                     _a    : List[Any]     					=key.reshape(__a  )
                     _a    : Union[str, Any]     					=value.reshape(__a  )

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

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            _a    : Tuple     					=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:
                     _a    : List[str]     					=old_checkpoint[path['''old''']][:, :, 0]
              else:
                     _a    : List[str]     					=old_checkpoint[path['''old''']]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int    ,_UpperCAmelCase	:	Optional[Any]  )  ->			int:
       _a    : int     					={}

       _a    : Union[str, Any]     					=checkpoint['''time_embed.0.weight''']
       _a    : Tuple     					=checkpoint['''time_embed.0.bias''']
       _a    : Union[str, Any]     					=checkpoint['''time_embed.2.weight''']
       _a    : Union[str, Any]     					=checkpoint['''time_embed.2.bias''']

       _a    : List[str]     					=checkpoint['''input_blocks.0.0.weight''']
       _a    : Tuple     					=checkpoint['''input_blocks.0.0.bias''']

       _a    : Any     					=checkpoint['''out.0.weight''']
       _a    : Dict     					=checkpoint['''out.0.bias''']
       _a    : str     					=checkpoint['''out.2.weight''']
       _a    : List[Any]     					=checkpoint['''out.2.bias''']

       # Retrieves the keys for the input blocks only
       _a    : Optional[Any]     					=len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """input_blocks""" in layer}  )
       _a    : Optional[int]     					={
           layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
           for layer_id in range(__a  )
       }

       # Retrieves the keys for the middle blocks only
       _a    : int     					=len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """middle_block""" in layer}  )
       _a    : int     					={
           layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
           for layer_id in range(__a  )
       }

       # Retrieves the keys for the output blocks only
       _a    : List[str]     					=len({""".""".join(layer.split("""."""  )[:2]  ) for layer in checkpoint if """output_blocks""" in layer}  )
       _a    : Any     					={
           layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
           for layer_id in range(__a  )
       }

       for i in range(1    ,__a  ):
              _a    : Optional[int]     					=(i - 1) // (config['''num_res_blocks'''] + 1)
              _a    : List[Any]     					=(i - 1) % (config['''num_res_blocks'''] + 1)

              _a    : int     					=[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
              _a    : Dict     					=[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:
                     _a    : List[Any]     					=checkpoint[
                         F"input_blocks.{i}.0.op.weight"
                     ]
                     _a    : Optional[Any]     					=checkpoint[
                         F"input_blocks.{i}.0.op.bias"
                     ]
                     continue

              _a    : List[Any]     					=renew_resnet_paths(__a  )
              _a    : Union[str, Any]     					={'''old''': F"input_blocks.{i}.0", '''new''': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
              _a    : Union[str, Any]     					={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
              assign_to_checkpoint(
                  __a    ,__a    ,__a    ,additional_replacements=[meta_path, resnet_op]    ,config=__a  )

              if len(__a  ):
                     _a    : List[Any]     					=renew_attention_paths(__a  )
                     _a    : List[str]     					={
                         '''old''': F"input_blocks.{i}.1",
                         '''new''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                     }
                     _a    : Optional[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(
                         __a    ,__a    ,__a    ,additional_replacements=[meta_path]    ,attention_paths_to_split=__a    ,config=__a    ,)

       _a    : str     					=middle_blocks[0]
       _a    : List[Any]     					=middle_blocks[1]
       _a    : Optional[int]     					=middle_blocks[2]

       _a    : List[str]     					=renew_resnet_paths(__a  )
       assign_to_checkpoint(__a    ,__a    ,__a    ,config=__a  )

       _a    : Optional[int]     					=renew_resnet_paths(__a  )
       assign_to_checkpoint(__a    ,__a    ,__a    ,config=__a  )

       _a    : List[str]     					=renew_attention_paths(__a  )
       _a    : int     					={
           '''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(
           __a    ,__a    ,__a    ,attention_paths_to_split=__a    ,config=__a  )

       for i in range(__a  ):
              _a    : List[str]     					=i // (config['''num_res_blocks'''] + 1)
              _a    : Optional[Any]     					=i % (config['''num_res_blocks'''] + 1)
              _a    : Optional[int]     					=[shave_segments(__a    ,2  ) for name in output_blocks[i]]
              _a    : Optional[int]     					={}

              for layer in output_block_layers:
                     _a    : List[Any]     					=layer.split("""."""  )[0], shave_segments(__a    ,1  )
                     if layer_id in output_block_list:
                            output_block_list[layer_id].append(__a  )
                     else:
                            _a    : Dict     					=[layer_name]

              if len(__a  ) > 1:
                     _a    : Dict     					=[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                     _a    : str     					=[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                     _a    : Tuple     					=renew_resnet_paths(__a  )
                     _a    : int     					=renew_resnet_paths(__a  )

                     _a    : Union[str, Any]     					={'''old''': F"output_blocks.{i}.0", '''new''': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                     assign_to_checkpoint(__a    ,__a    ,__a    ,additional_replacements=[meta_path]    ,config=__a  )

                     if ["conv.weight", "conv.bias"] in output_block_list.values():
                            _a    : str     					=list(output_block_list.values()  ).index(["""conv.weight""", """conv.bias"""]  )
                            _a    : str     					=checkpoint[
                                F"output_blocks.{i}.{index}.conv.weight"
                            ]
                            _a    : List[Any]     					=checkpoint[
                                F"output_blocks.{i}.{index}.conv.bias"
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(__a  ) == 2:
                                   _a    : str     					=[]

                     if len(__a  ):
                            _a    : Optional[int]     					=renew_attention_paths(__a  )
                            _a    : Optional[Any]     					={
                                '''old''': F"output_blocks.{i}.1",
                                '''new''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                            }
                            _a    : Optional[int]     					={
                                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(
                                __a    ,__a    ,__a    ,additional_replacements=[meta_path]    ,attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions  ) else None    ,config=__a    ,)
              else:
                     _a    : Dict     					=renew_resnet_paths(__a    ,n_shave_prefix_segments=1  )
                     for path in resnet_0_paths:
                            _a    : List[Any]     					='''.'''.join(["""output_blocks""", str(__a  ), path["""old"""]]  )
                            _a    : Optional[int]     					='''.'''.join(["""up_blocks""", str(__a  ), """resnets""", str(__a  ), path["""new"""]]  )

                            _a    : int     					=checkpoint[old_path]

       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__:    List[Any]					    =       parser.parse_args()

   A__:    str					    =       torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      A__:    Union[str, Any]					    =       json.loads(f.read())

   A__:    List[Any]					    =       convert_ldm_checkpoint(checkpoint, config)

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

   A__:    List[str]					    =       UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      A__:    Optional[Any]					    =       DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
      A__:    int					    =       VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

      A__:    int					    =       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 re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )