


"""simple docstring"""






import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=100       ,   UpperCAmelCase__=" "				)     ->     int:
   a   :	List[str]						=    text.split(UpperCamelCase_				)
   return [character.join(text[i : i + n]				).strip() for i in range(0       ,   len(UpperCamelCase_				)       ,   UpperCamelCase_				)]




def        A_					(					UpperCAmelCase__				)     ->     Any:
   a	, a   :	str						=    [], []
   for title, text in zip(documents['title']       ,   documents['text']				):
      if text is not None:
         for passage in split_text(UpperCamelCase_				):
            titles.append(title if title is not None else ''				)
            texts.append(UpperCamelCase_				)
   return {"title": titles, "text": texts}




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   a   :	Any						=    load_dataset(
       'csv'       ,   data_files=[csv_path]       ,   split='train'       ,   delimiter='\t'       ,   column_names=['title', 'text']				)
   a   :	int						=    kb_dataset.map(
       UpperCamelCase_       ,   batched=UpperCamelCase_       ,   num_proc=1				)  # if you want you can load already splitted csv.
   a   :	Union[str, Any]						=    [kb_dataset.shard(UpperCamelCase_       ,   UpperCamelCase_       ,   contiguous=UpperCamelCase_				) for i in range(UpperCamelCase_				)]
   a   :	List[str]						=    kb_list[process_num]

   a   :	List[Any]						=    'data_' + str(UpperCamelCase_				)
   a   :	str						=    os.path.join(UpperCamelCase_       ,   UpperCamelCase_				)

   a   :	List[str]						=    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'				)
   a   :	Optional[int]						=    ctx_encoder.to(device=UpperCamelCase_				)

   def embed(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				) -> dict:
      a   :	Tuple						=    ctx_tokenizer(
          documents['title']       ,   documents['text']       ,   truncation=UpperCamelCase_       ,   padding='longest'       ,   return_tensors='pt'				)['input_ids']
      a   :	int						=    ctx_encoder(input_ids.to(device=UpperCamelCase_				)       ,   return_dict=UpperCamelCase_				).pooler_output
      return {"embeddings": embeddings.detach().cpu().numpy()}

   a   :	List[Any]						=    Features(
       {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space

   a   :	Optional[int]						=    data_shrad.map(
       partial(UpperCamelCase_       ,   ctx_encoder=UpperCamelCase_       ,   ctx_tokenizer=UpperCamelCase_       ,   device=UpperCamelCase_				)       ,   batched=UpperCamelCase_       ,   batch_size=16       ,   features=UpperCamelCase_       ,   )
   dataset.save_to_disk(UpperCamelCase_				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   a   :	Union[str, Any]						=    []

   for shard_address in glob(str(UpperCamelCase_				) + '/*/'				):
      data_shard_list.append(load_from_disk(UpperCamelCase_				)				)

   a   :	str						=    concatenate_datasets(UpperCamelCase_				)
   faiss.omp_set_num_threads(96				)

   a   :	Optional[Any]						=    faiss.IndexHNSWFlat(768       ,   128       ,   faiss.METRIC_INNER_PRODUCT				)
   concat.add_faiss_index('embeddings'       ,   custom_index=UpperCamelCase_				)
   concat.get_index('embeddings'				).save(
       UpperCamelCase_				)  # since we load the index in to memory,we can directly update the index in the disk













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""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_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



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

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

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

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



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

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

												lowerCAmelCase__         =    new_item.replace('proj_out'     ,							'proj_attn'		)
												lowerCAmelCase__         =    new_item.replace('norm'     ,							'group_norm'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)
												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



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

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

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

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

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

						for path in paths:
												lowerCAmelCase__         =    path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase__         =    new_path.replace('down.'     ,							'down_blocks.'		)
												lowerCAmelCase__         =    new_path.replace('up.'     ,							'up_blocks.'		)

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

												if "attentions" in new_path:
																		lowerCAmelCase__         =    old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase__         =    old_checkpoint[path['old']]







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['temb.dense.0.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.0.bias']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.bias']

						lowerCAmelCase__         =    checkpoint['norm_out.weight']
						lowerCAmelCase__         =    checkpoint['norm_out.bias']

						lowerCAmelCase__         =    checkpoint['conv_in.weight']
						lowerCAmelCase__         =    checkpoint['conv_in.bias']
						lowerCAmelCase__         =    checkpoint['conv_out.weight']
						lowerCAmelCase__         =    checkpoint['conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

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

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						return new_checkpoint







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['encoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['encoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

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

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

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

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  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.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

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

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

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

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

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





