


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=0    ):



     """simple docstring"""






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

          snake_case					=    new_item.replace('''out_layers.0'''							,'''norm2'''    )
          snake_case					=    new_item.replace('''out_layers.3'''							,'''conv2'''    )

          snake_case					=    new_item.replace('''emb_layers.1'''							,'''time_emb_proj'''    )
          snake_case					=    new_item.replace('''skip_connection'''							,'''conv_shortcut'''    )

          snake_case					=    shave_segments(UpperCamelCase_							,n_shave_prefix_segments=UpperCamelCase_    )

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

     return mapping
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=0    ):



     """simple docstring"""






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

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

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

          snake_case					=    shave_segments(UpperCamelCase_							,n_shave_prefix_segments=UpperCamelCase_    )

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

     return mapping
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=None    ):



     """simple docstring"""






     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():
               snake_case					=    old_checkpoint[path]
               snake_case					=    old_tensor.shape[0] // 3

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

               snake_case					=    old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

               snake_case					=    query.reshape(UpperCamelCase_    )
               snake_case					=    key.reshape(UpperCamelCase_    )
               snake_case					=    value.reshape(UpperCamelCase_    )

     for path in paths:
          snake_case					=    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
          snake_case					=    new_path.replace('''middle_block.0'''							,'''mid_block.resnets.0'''    )
          snake_case					=    new_path.replace('''middle_block.1'''							,'''mid_block.attentions.0'''    )
          snake_case					=    new_path.replace('''middle_block.2'''							,'''mid_block.resnets.1'''    )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    snake_case					=    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:
               snake_case					=    old_checkpoint[path['''old''']][:, :, 0]
          else:
               snake_case					=    old_checkpoint[path['''old''']]
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    {}

     snake_case					=    checkpoint['''time_embed.0.weight''']
     snake_case					=    checkpoint['''time_embed.0.bias''']
     snake_case					=    checkpoint['''time_embed.2.weight''']
     snake_case					=    checkpoint['''time_embed.2.bias''']

     snake_case					=    checkpoint['''input_blocks.0.0.weight''']
     snake_case					=    checkpoint['''input_blocks.0.0.bias''']

     snake_case					=    checkpoint['''out.0.weight''']
     snake_case					=    checkpoint['''out.0.bias''']
     snake_case					=    checkpoint['''out.2.weight''']
     snake_case					=    checkpoint['''out.2.bias''']

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

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

          snake_case					=    renew_resnet_paths(UpperCamelCase_    )
          snake_case					=    {'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          snake_case					=    {'''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_    ):
               snake_case					=    renew_attention_paths(UpperCamelCase_    )
               snake_case					=    {
                   '''old''': F'''input_blocks.{i}.1''',
                   '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
               }
               snake_case					=    {
                   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_							,)

     snake_case					=    middle_blocks[0]
     snake_case					=    middle_blocks[1]
     snake_case					=    middle_blocks[2]

     snake_case					=    renew_resnet_paths(UpperCamelCase_    )
     assign_to_checkpoint(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,config=UpperCamelCase_    )

     snake_case					=    renew_resnet_paths(UpperCamelCase_    )
     assign_to_checkpoint(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,config=UpperCamelCase_    )

     snake_case					=    renew_attention_paths(UpperCamelCase_    )
     snake_case					=    {
         '''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_    ):
          snake_case					=    i // (config['''num_res_blocks'''] + 1)
          snake_case					=    i % (config['''num_res_blocks'''] + 1)
          snake_case					=    [shave_segments(UpperCamelCase_							,2    ) for name in output_blocks[i]]
          snake_case					=    {}

          for layer in output_block_layers:
               snake_case			,  snake_case					=    layer.split('''.'''    )[0], shave_segments(UpperCamelCase_							,1    )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(UpperCamelCase_    )
               else:
                    snake_case					=    [layer_name]

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

               snake_case					=    renew_resnet_paths(UpperCamelCase_    )
               snake_case					=    renew_resnet_paths(UpperCamelCase_    )

               snake_case					=    {'''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():
                    snake_case					=    list(output_block_list.values()    ).index(['''conv.weight''', '''conv.bias''']    )
                    snake_case					=    checkpoint[
                        F'''output_blocks.{i}.{index}.conv.weight'''
                    ]
                    snake_case					=    checkpoint[
                        F'''output_blocks.{i}.{index}.conv.bias'''
                    ]

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

               if len(UpperCamelCase_    ):
                    snake_case					=    renew_attention_paths(UpperCamelCase_    )
                    snake_case					=    {
                        '''old''': F'''output_blocks.{i}.1''',
                        '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                    }
                    snake_case					=    {
                        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:
               snake_case					=    renew_resnet_paths(UpperCamelCase_							,n_shave_prefix_segments=1    )
               for path in resnet_0_paths:
                    snake_case					=    '''.'''.join(['''output_blocks''', str(UpperCamelCase_    ), path['''old''']]    )
                    snake_case					=    '''.'''.join(['''up_blocks''', str(UpperCamelCase_    ), '''resnets''', str(UpperCamelCase_    ), path['''new''']]    )

                    snake_case					=    checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   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      :			List[Any]      				=   parser.parse_args()

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   torch.load(args.checkpoint_path)

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

 _SCREAMING_SNAKE_CASE      :			int      				=   convert_ldm_checkpoint(checkpoint, config)

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

 _SCREAMING_SNAKE_CASE      :			str      				=   UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _SCREAMING_SNAKE_CASE      :			List[str]      				=   DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  _SCREAMING_SNAKE_CASE      :			int      				=   VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

  _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




