






'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 				lowercase_					(     __A			:							Dict       ,   __A			:							str=1      )							->   Any:



 """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_					(     __A			:							Dict       ,   __A			:							Dict=0      )							->   str:



 """simple docstring"""




 lowercase       : str						=[]
 for old_item in old_list:
  lowercase       : int						=old_item.replace('''in_layers.0'''       ,   '''norm1'''      )
  lowercase       : str						=new_item.replace('''in_layers.2'''       ,   '''conv1'''      )

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

  lowercase       : Optional[Any]						=new_item.replace('''emb_layers.1'''       ,   '''time_emb_proj'''      )
  lowercase       : str						=new_item.replace('''skip_connection'''       ,   '''conv_shortcut'''      )

  lowercase       : str						=shave_segments(__A       ,   n_shave_prefix_segments=__A      )

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

 return mapping


def 				lowercase_					(     __A			:							Any       ,   __A			:							Dict=0      )							->   Optional[Any]:



 """simple docstring"""




 lowercase       : Any						=[]
 for old_item in old_list:
  lowercase       : List[Any]						=old_item

  lowercase       : int						=new_item.replace('''norm.weight'''       ,   '''group_norm.weight'''      )
  lowercase       : List[Any]						=new_item.replace('''norm.bias'''       ,   '''group_norm.bias'''      )

  lowercase       : Any						=new_item.replace('''proj_out.weight'''       ,   '''proj_attn.weight'''      )
  lowercase       : Union[str, Any]						=new_item.replace('''proj_out.bias'''       ,   '''proj_attn.bias'''      )

  lowercase       : Union[str, Any]						=shave_segments(__A       ,   n_shave_prefix_segments=__A      )

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

 return mapping


def 				lowercase_					(     __A			:							Union[str, Any]       ,   __A			:							Optional[Any]       ,   __A			:							Union[str, Any]       ,   __A			:							Optional[Any]=None       ,   __A			:							List[str]=None       ,   __A			:							List[str]=None      )							->   int:



 """simple docstring"""




 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():
   lowercase       : List[Any]						=old_checkpoint[path]
   lowercase       : List[str]						=old_tensor.shape[0] // 3

   lowercase       : str						=(-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

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

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

   lowercase       : Optional[int]						=query.reshape(__A      )
   lowercase       : List[str]						=key.reshape(__A      )
   lowercase       : Dict						=value.reshape(__A      )

 for path in paths:
  lowercase       : Optional[int]						=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
  lowercase       : Dict						=new_path.replace('''middle_block.0'''       ,   '''mid_block.resnets.0'''      )
  lowercase       : Optional[Any]						=new_path.replace('''middle_block.1'''       ,   '''mid_block.attentions.0'''      )
  lowercase       : Any						=new_path.replace('''middle_block.2'''       ,   '''mid_block.resnets.1'''      )

  if additional_replacements is not None:
   for replacement in additional_replacements:
    lowercase       : Union[str, Any]						=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:
   lowercase       : List[str]						=old_checkpoint[path['''old''']][:, :, 0]
  else:
   lowercase       : int						=old_checkpoint[path['''old''']]


def 				lowercase_					(     __A			:							List[str]       ,   __A			:							Dict      )							->   Optional[int]:



 """simple docstring"""




 lowercase       : Optional[int]						={}

 lowercase       : Tuple						=checkpoint['''time_embed.0.weight''']
 lowercase       : Dict						=checkpoint['''time_embed.0.bias''']
 lowercase       : str						=checkpoint['''time_embed.2.weight''']
 lowercase       : Tuple						=checkpoint['''time_embed.2.bias''']

 lowercase       : Any						=checkpoint['''input_blocks.0.0.weight''']
 lowercase       : List[str]						=checkpoint['''input_blocks.0.0.bias''']

 lowercase       : Dict						=checkpoint['''out.0.weight''']
 lowercase       : Optional[int]						=checkpoint['''out.0.bias''']
 lowercase       : Union[str, Any]						=checkpoint['''out.2.weight''']
 lowercase       : List[str]						=checkpoint['''out.2.bias''']

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

  lowercase       : int						=[key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
  lowercase       : 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:
   lowercase       : Optional[Any]						=checkpoint[
       F'input_blocks.{i}.0.op.weight'
   ]
   lowercase       : Optional[Any]						=checkpoint[
       F'input_blocks.{i}.0.op.bias'
   ]
   continue

  lowercase       : Any						=renew_resnet_paths(__A      )
  lowercase       : Any						={'''old''': F'input_blocks.{i}.0', '''new''': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
  lowercase       : str						={'''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      ):
   lowercase       : Tuple						=renew_attention_paths(__A      )
   lowercase       : Tuple						={
       '''old''': F'input_blocks.{i}.1',
       '''new''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
   }
   lowercase       : Tuple						={
       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       ,   )

 lowercase       : Dict						=middle_blocks[0]
 lowercase       : Optional[int]						=middle_blocks[1]
 lowercase       : Tuple						=middle_blocks[2]

 lowercase       : int						=renew_resnet_paths(__A      )
 assign_to_checkpoint(__A       ,   __A       ,   __A       ,   config=__A      )

 lowercase       : Dict						=renew_resnet_paths(__A      )
 assign_to_checkpoint(__A       ,   __A       ,   __A       ,   config=__A      )

 lowercase       : List[Any]						=renew_attention_paths(__A      )
 lowercase       : Tuple						={
     '''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      ):
  lowercase       : str						=i // (config['''num_res_blocks'''] + 1)
  lowercase       : Dict						=i % (config['''num_res_blocks'''] + 1)
  lowercase       : Optional[int]						=[shave_segments(__A       ,   2      ) for name in output_blocks[i]]
  lowercase       : Tuple						={}

  for layer in output_block_layers:
   lowercase	,		lowercase       : Union[str, Any]						=layer.split('''.'''      )[0], shave_segments(__A       ,   1      )
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__A      )
   else:
    lowercase       : List[Any]						=[layer_name]

  if len(__A      ) > 1:
   lowercase       : Union[str, Any]						=[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
   lowercase       : Optional[int]						=[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

   lowercase       : List[str]						=renew_resnet_paths(__A      )
   lowercase       : List[Any]						=renew_resnet_paths(__A      )

   lowercase       : Optional[int]						={'''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():
    lowercase       : Optional[int]						=list(output_block_list.values()      ).index(['''conv.weight''', '''conv.bias''']      )
    lowercase       : Optional[Any]						=checkpoint[
        F'output_blocks.{i}.{index}.conv.weight'
    ]
    lowercase       : Tuple						=checkpoint[
        F'output_blocks.{i}.{index}.conv.bias'
    ]

    # Clear attentions as they have been attributed above.
    if len(__A      ) == 2:
     lowercase       : Optional[int]						=[]

   if len(__A      ):
    lowercase       : List[Any]						=renew_attention_paths(__A      )
    lowercase       : int						={
        '''old''': F'output_blocks.{i}.1',
        '''new''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
    }
    lowercase       : Tuple						={
        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:
   lowercase       : Union[str, Any]						=renew_resnet_paths(__A       ,   n_shave_prefix_segments=1      )
   for path in resnet_0_paths:
    lowercase       : int						='''.'''.join(['''output_blocks''', str(__A      ), path['''old''']]      )
    lowercase       : Dict						='''.'''.join(['''up_blocks''', str(__A      ), '''resnets''', str(__A      ), path['''new''']]      )

    lowercase       : Tuple						=checkpoint[old_path]

 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())

      SCREAMING_SNAKE_CASE							=						convert_ldm_checkpoint(checkpoint, config)

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

      SCREAMING_SNAKE_CASE							=						UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

            SCREAMING_SNAKE_CASE							=						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)









'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

