"""simple docstring"""

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 							A						(    )				->     Optional[int]:
 __UpperCamelCase      =	'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
 __UpperCamelCase      =	Image.open(requests.get(lowerCAmelCase__    , stream=lowerCAmelCase__		).raw		).convert('RGB'		)

 return image






def 							A						(    snake_case				:int		)				->     List[str]:
 __UpperCamelCase      =	[]
 # fmt: off

 # vision encoder
 rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')		)
 rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')		)
 rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')		)
 rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')		)
 rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')		)
 rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')		)

 for i in range(config.vision_config.num_hidden_layers		):
  rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',)		)
  rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight')		)
  rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias')		)

 # QFormer
 rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')		)
 rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')		)

 # fmt: on
 return rename_keys






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:Optional[int]		)				->     List[str]:
 __UpperCamelCase      =	dct.pop(lowerCAmelCase__		)
 __UpperCamelCase      =	val






def 							A						(    snake_case				:Tuple    , snake_case				:List[Any]		)				->     List[Any]:


 for i in range(config.vision_config.num_hidden_layers		):
  # read in original q and v biases
  __UpperCamelCase      =	state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'		)
  __UpperCamelCase      =	state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'		)

  # next, set bias in the state dict
  __UpperCamelCase      =	torch.cat((q_bias, torch.zeros_like(lowerCAmelCase__    , requires_grad=lowerCAmelCase__		), v_bias)		)
  __UpperCamelCase      =	qkv_bias






def 							A						(    snake_case				:Union[str, Any]    , snake_case				:int		)				->     List[Any]:
 __UpperCamelCase      =	3_6_4 if 'coco' in model_name else 2_2_4
 __UpperCamelCase      =	BlipaVisionConfig(image_size=lowerCAmelCase__		).to_dict()

 # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
 # seems like flan-T5 models don't have bos_token_id properly set?
 if "opt-2.7b" in model_name:
  __UpperCamelCase      =	OPTConfig.from_pretrained('facebook/opt-2.7b'    , eos_token_id=lowerCAmelCase__		).to_dict()
 elif "opt-6.7b" in model_name:
  __UpperCamelCase      =	OPTConfig.from_pretrained('facebook/opt-6.7b'    , eos_token_id=lowerCAmelCase__		).to_dict()
 elif "t5-xl" in model_name:
  __UpperCamelCase      =	TaConfig.from_pretrained('google/flan-t5-xl'    , dense_act_fn='gelu'    , bos_token_id=1		).to_dict()
 elif "t5-xxl" in model_name:
  __UpperCamelCase      =	TaConfig.from_pretrained('google/flan-t5-xxl'    , dense_act_fn='gelu'    , bos_token_id=1		).to_dict()

 __UpperCamelCase      =	BlipaConfig(vision_config=lowerCAmelCase__    , text_config=lowerCAmelCase__		)

 return config, image_size






@torch.no_grad()
def 							A						(    snake_case				:List[Any]    , snake_case				:Optional[Any]=None    , snake_case				:Dict=False		)				->     List[Any]:
 __UpperCamelCase      =	(
     AutoTokenizer.from_pretrained('facebook/opt-2.7b'		)
     if 'opt' in model_name
     else AutoTokenizer.from_pretrained('google/flan-t5-xl'		)
 )
 __UpperCamelCase      =	tokenizer('\n'    , add_special_tokens=lowerCAmelCase__		).input_ids[0]
 __UpperCamelCase  ,     __UpperCamelCase      =	get_blipa_config(lowerCAmelCase__    , eos_token_id=lowerCAmelCase__		)

 __UpperCamelCase      =	BlipaForConditionalGeneration(lowerCAmelCase__		).eval()

 __UpperCamelCase      =	{
     'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
     'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
     'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
     'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
     'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
     'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
     'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
 }

 __UpperCamelCase  ,     __UpperCamelCase      =	model_name_to_original[model_name]

 # load original model
 print('Loading original model...'		)
 __UpperCamelCase      =	'cuda' if torch.cuda.is_available() else 'cpu'
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	load_model_and_preprocess(
     name=lowerCAmelCase__    , model_type=lowerCAmelCase__    , is_eval=lowerCAmelCase__    , device=lowerCAmelCase__		)
 original_model.eval()
 print('Done!'		)

 # update state dict keys
 __UpperCamelCase      =	original_model.state_dict()
 __UpperCamelCase      =	create_rename_keys(lowerCAmelCase__		)
 for src, dest in rename_keys:
  rename_key(lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__		)

 # some keys can be renamed efficiently
 for key, val in state_dict.copy().items():
  __UpperCamelCase      =	state_dict.pop(lowerCAmelCase__		)
  if key.startswith('Qformer.bert'		):
   __UpperCamelCase      =	key.replace('Qformer.bert'    , 'qformer'		)
  if "attention.self" in key:
   __UpperCamelCase      =	key.replace('self'    , 'attention'		)
  if "opt_proj" in key:
   __UpperCamelCase      =	key.replace('opt_proj'    , 'language_projection'		)
  if "t5_proj" in key:
   __UpperCamelCase      =	key.replace('t5_proj'    , 'language_projection'		)
  if key.startswith('opt'		):
   __UpperCamelCase      =	key.replace('opt'    , 'language'		)
  if key.startswith('t5'		):
   __UpperCamelCase      =	key.replace('t5'    , 'language'		)
  __UpperCamelCase      =	val

 # read in qv biases
 read_in_q_v_bias(lowerCAmelCase__    , lowerCAmelCase__		)

 __UpperCamelCase  ,     __UpperCamelCase      =	hf_model.load_state_dict(lowerCAmelCase__    , strict=lowerCAmelCase__		)
 assert len(lowerCAmelCase__		) == 0
 assert unexpected_keys == ["qformer.embeddings.position_ids"]

 __UpperCamelCase      =	load_demo_image()
 __UpperCamelCase      =	vis_processors['eval'](lowerCAmelCase__		).unsqueeze(0		).to(lowerCAmelCase__		)
 __UpperCamelCase      =	tokenizer(['\n']    , return_tensors='pt'		).input_ids.to(lowerCAmelCase__		)

 # create processor
 __UpperCamelCase      =	BlipImageProcessor(
     size={'height': image_size, 'width': image_size}    , image_mean=lowerCAmelCase__    , image_std=lowerCAmelCase__		)
 __UpperCamelCase      =	BlipaProcessor(image_processor=lowerCAmelCase__    , tokenizer=lowerCAmelCase__		)
 __UpperCamelCase      =	processor(images=lowerCAmelCase__    , return_tensors='pt'		).pixel_values.to(lowerCAmelCase__		)

 # make sure processor creates exact same pixel values
 assert torch.allclose(lowerCAmelCase__    , lowerCAmelCase__		)

 original_model.to(lowerCAmelCase__		)
 hf_model.to(lowerCAmelCase__		)
 with torch.no_grad():
  if "opt" in model_name:
   __UpperCamelCase      =	original_model({'image': original_pixel_values, 'text_input': ['']}		).logits
   __UpperCamelCase      =	hf_model(lowerCAmelCase__    , lowerCAmelCase__		).logits
  else:
   __UpperCamelCase      =	original_model(
       {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}		).logits
   __UpperCamelCase      =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id    , -1_0_0		)
   __UpperCamelCase      =	hf_model(lowerCAmelCase__    , lowerCAmelCase__    , labels=lowerCAmelCase__		).logits

 assert original_logits.shape == logits.shape
 print('First values of original logits:'    , original_logits[0, :3, :3]		)
 print('First values of HF logits:'    , logits[0, :3, :3]		)

 # assert values
 if model_name == "blip2-flan-t5-xl":
  __UpperCamelCase      =	torch.tensor(
      [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]    , device=lowerCAmelCase__		)
  assert torch.allclose(logits[0, :3, :3]    , lowerCAmelCase__    , atol=1e-4		)
 elif model_name == "blip2-flan-t5-xl-coco":
  __UpperCamelCase      =	torch.tensor(
      [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]    , device=lowerCAmelCase__		)
 else:
  # cast to same type
  __UpperCamelCase      =	logits.dtype
  assert torch.allclose(original_logits.to(lowerCAmelCase__		)    , lowerCAmelCase__    , atol=1e-2		)
 print('Looks ok!'		)

 print('Generating a caption...'		)
 __UpperCamelCase      =	''
 __UpperCamelCase      =	tokenizer(lowerCAmelCase__    , return_tensors='pt'		).input_ids.to(lowerCAmelCase__		)

 __UpperCamelCase      =	original_model.generate({'image': original_pixel_values}		)
 __UpperCamelCase      =	hf_model.generate(
     lowerCAmelCase__    , lowerCAmelCase__    , do_sample=lowerCAmelCase__    , num_beams=5    , max_length=3_0    , min_length=1    , top_p=0.9    , repetition_penalty=1.0    , length_penalty=1.0    , temperature=1    , )
 print('Original generation:'    , lowerCAmelCase__		)
 __UpperCamelCase      =	input_ids.shape[1]
 __UpperCamelCase      =	processor.batch_decode(outputs[:, prompt_length:]    , skip_special_tokens=lowerCAmelCase__		)
 __UpperCamelCase      =	[text.strip() for text in output_text]
 print('HF generation:'    , lowerCAmelCase__		)

 if pytorch_dump_folder_path is not None:
  processor.save_pretrained(lowerCAmelCase__		)
  hf_model.save_pretrained(lowerCAmelCase__		)

 if push_to_hub:
  processor.push_to_hub(f'nielsr/{model_name}'		)
  hf_model.push_to_hub(f'nielsr/{model_name}'		)


if __name__ == "__main__":
      UpperCamelCase     :    str       =			argparse.ArgumentParser()
      UpperCamelCase     :    Optional[Any]       =			[
          """blip2-opt-2.7b""",
          """blip2-opt-6.7b""",
          """blip2-opt-2.7b-coco""",
          """blip2-opt-6.7b-coco""",
          """blip2-flan-t5-xl""",
          """blip2-flan-t5-xl-coco""",
          """blip2-flan-t5-xxl""",
      ]
      parser.add_argument(
          "--model_name",
          default="blip2-opt-2.7b",
          choices=choices,
          type=str,
          help="Path to hf config.json of model to convert",
      )
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether to push the model and processor to the hub after converting",
      )

      UpperCamelCase     :    int       =			parser.parse_args()

      convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict=1		)				->     Optional[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 							A						(    snake_case				:Dict    , snake_case				:int=0		)				->     Optional[int]:
 __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(snake_case    , n_shave_prefix_segments=snake_case		)

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

 return mapping






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Tuple=0		)				->     Tuple:
 __UpperCamelCase      =	[]
 for old_item in old_list:
  __UpperCamelCase      =	old_item

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

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

  __UpperCamelCase      =	shave_segments(snake_case    , n_shave_prefix_segments=snake_case		)

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

 return mapping






def 							A						(    snake_case				:int    , snake_case				:List[str]    , snake_case				:List[str]    , snake_case				:Any=None    , snake_case				:Optional[int]=None    , snake_case				:Union[str, Any]=None		)				->     Optional[int]:
 assert isinstance(snake_case    , snake_case		), "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(snake_case		)
   __UpperCamelCase      =	key.reshape(snake_case		)
   __UpperCamelCase      =	value.reshape(snake_case		)

 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

  # Global renaming happens here
  __UpperCamelCase      =	new_path.replace('middle_block.0'    , 'mid_block.resnets.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.1'    , 'mid_block.attentions.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.2'    , 'mid_block.resnets.1'		)

  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
  if "proj_attn.weight" in new_path:
   __UpperCamelCase      =	old_checkpoint[path['old']][:, :, 0]
  else:
   __UpperCamelCase      =	old_checkpoint[path['old']]






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Dict		)				->     Optional[Any]:
 __UpperCamelCase      =	{}

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

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

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

 # Retrieves the keys for the input blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the middle blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the output blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 for i in range(1    , snake_case		):
  __UpperCamelCase      =	(i - 1) // (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	(i - 1) % (config['num_res_blocks'] + 1)

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

  __UpperCamelCase      =	renew_resnet_paths(snake_case		)
  __UpperCamelCase      =	{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
  __UpperCamelCase      =	{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
  assign_to_checkpoint(
      snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path, resnet_op]    , config=snake_case		)

  if len(snake_case		):
   __UpperCamelCase      =	renew_attention_paths(snake_case		)
   __UpperCamelCase      =	{
       'old': f'input_blocks.{i}.1',
       'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
   }
   __UpperCamelCase      =	{
       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(
       snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=snake_case    , config=snake_case    , )

 __UpperCamelCase      =	middle_blocks[0]
 __UpperCamelCase      =	middle_blocks[1]
 __UpperCamelCase      =	middle_blocks[2]

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

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

 __UpperCamelCase      =	renew_attention_paths(snake_case		)
 __UpperCamelCase      =	{
     '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(
     snake_case    , snake_case    , snake_case    , attention_paths_to_split=snake_case    , config=snake_case		)

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

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

  if len(snake_case		) > 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      =	renew_resnet_paths(snake_case		)
   __UpperCamelCase      =	renew_resnet_paths(snake_case		)

   __UpperCamelCase      =	{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
   assign_to_checkpoint(snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , config=snake_case		)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    __UpperCamelCase      =	list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.weight'
    ]
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.bias'
    ]

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

   if len(snake_case		):
    __UpperCamelCase      =	renew_attention_paths(snake_case		)
    __UpperCamelCase      =	{
        'old': f'output_blocks.{i}.1',
        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
    }
    __UpperCamelCase      =	{
        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(
        snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=to_split if any('qkv' in key for key in attentions		) else None    , config=snake_case    , )
  else:
   __UpperCamelCase      =	renew_resnet_paths(snake_case    , n_shave_prefix_segments=1		)
   for path in resnet_0_paths:
    __UpperCamelCase      =	'.'.join(['output_blocks', str(snake_case		), path['old']]		)
    __UpperCamelCase      =	'.'.join(['up_blocks', str(snake_case		), 'resnets', str(snake_case		), path['new']]		)

    __UpperCamelCase      =	checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
      UpperCamelCase     :    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.")

      UpperCamelCase     :    Dict       =			parser.parse_args()

      UpperCamelCase     :    Optional[Any]       =			torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            UpperCamelCase     :    int       =			json.loads(f.read())

      UpperCamelCase     :    Dict       =			convert_ldm_checkpoint(checkpoint, config)

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

      UpperCamelCase     :    Optional[Any]       =			UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

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



