




"""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       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  _snake_case							:	Union[str, Any]			=						Image.open(requests.get(snake_case__	,     stream=snake_case__					).raw					).convert("""RGB"""					)

  return image


def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	str			=						[]
  # 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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      List[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						dct.pop(snake_case__					)
  _snake_case							:	Optional[int]			=						val


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      str					):
  """simple docstring"""

  for i in range(config.vision_config.num_hidden_layers					):
    # read in original q and v biases
    _snake_case							:	Optional[int]			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"					)
    _snake_case							:	Tuple			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"					)

    # next, set bias in the state dict
    _snake_case							:	List[str]			=						torch.cat((q_bias, torch.zeros_like(snake_case__	,     requires_grad=snake_case__					), v_bias)					)
    _snake_case							:	Dict			=						qkv_bias


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						3_64 if """coco""" in model_name else 2_24
  _snake_case							:	List[str]			=						BlipaVisionConfig(image_size=snake_case__					).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:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-2.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "opt-6.7b" in model_name:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-6.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "t5-xl" in model_name:
    _snake_case							:	Tuple			=						TaConfig.from_pretrained("""google/flan-t5-xl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()
  elif "t5-xxl" in model_name:
    _snake_case							:	List[Any]			=						TaConfig.from_pretrained("""google/flan-t5-xxl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()

  _snake_case							:	int			=						BlipaConfig(vision_config=snake_case__	,     text_config=snake_case__					)

  return config, image_size


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int=None	,     snake_case__							:      str=False					):
  """simple docstring"""

  _snake_case							:	List[str]			=						(
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""					)
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""					)
  )
  _snake_case							:	str			=						tokenizer("""\n"""	,     add_special_tokens=snake_case__					).input_ids[0]
  _snake_case	,      _snake_case							:	Dict			=						get_blipa_config(snake_case__	,     eos_token_id=snake_case__					)

  _snake_case							:	str			=						BlipaForConditionalGeneration(snake_case__					).eval()

  _snake_case							:	int			=						{
      """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"""),
  }

  _snake_case	,      _snake_case							:	List[Any]			=						model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""					)
  _snake_case							:	int			=						"""cuda""" if torch.cuda.is_available() else """cpu"""
  _snake_case	,      _snake_case	,      _snake_case							:	Any			=						load_model_and_preprocess(
      name=snake_case__	,     model_type=snake_case__	,     is_eval=snake_case__	,     device=snake_case__					)
  original_model.eval()
  print("""Done!"""					)

  # update state dict keys
  _snake_case							:	Any			=						original_model.state_dict()
  _snake_case							:	Dict			=						create_rename_keys(snake_case__					)
  for src, dest in rename_keys:
    rename_key(snake_case__	,     snake_case__	,     snake_case__					)

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    _snake_case							:	str			=						state_dict.pop(snake_case__					)
    if key.startswith("""Qformer.bert"""					):
      _snake_case							:	str			=						key.replace("""Qformer.bert"""	,     """qformer"""					)
    if "attention.self" in key:
      _snake_case							:	Any			=						key.replace("""self"""	,     """attention"""					)
    if "opt_proj" in key:
      _snake_case							:	List[str]			=						key.replace("""opt_proj"""	,     """language_projection"""					)
    if "t5_proj" in key:
      _snake_case							:	Optional[Any]			=						key.replace("""t5_proj"""	,     """language_projection"""					)
    if key.startswith("""opt"""					):
      _snake_case							:	List[Any]			=						key.replace("""opt"""	,     """language"""					)
    if key.startswith("""t5"""					):
      _snake_case							:	List[Any]			=						key.replace("""t5"""	,     """language"""					)
    _snake_case							:	str			=						val

  # read in qv biases
  read_in_q_v_bias(snake_case__	,     snake_case__					)

  _snake_case	,      _snake_case							:	List[str]			=						hf_model.load_state_dict(snake_case__	,     strict=snake_case__					)
  assert len(snake_case__					) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _snake_case							:	Any			=						load_demo_image()
  _snake_case							:	str			=						vis_processors["""eval"""](snake_case__					).unsqueeze(0					).to(snake_case__					)
  _snake_case							:	List[Any]			=						tokenizer(["""\n"""]	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

  # create processor
  _snake_case							:	Any			=						BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}	,     image_mean=snake_case__	,     image_std=snake_case__					)
  _snake_case							:	int			=						BlipaProcessor(image_processor=snake_case__	,     tokenizer=snake_case__					)
  _snake_case							:	Any			=						processor(images=snake_case__	,     return_tensors="""pt"""					).pixel_values.to(snake_case__					)

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

  original_model.to(snake_case__					)
  hf_model.to(snake_case__					)
  with torch.no_grad():
    if "opt" in model_name:
      _snake_case							:	str			=						original_model({"""image""": original_pixel_values, """text_input""": [""""""]}					).logits
      _snake_case							:	int			=						hf_model(snake_case__	,     snake_case__					).logits
    else:
      _snake_case							:	str			=						original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}					).logits
      _snake_case							:	Optional[int]			=						input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,     -1_00					)
      _snake_case							:	Union[str, Any]			=						hf_model(snake_case__	,     snake_case__	,     labels=snake_case__					).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":
    _snake_case							:	List[str]			=						torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]	,     device=snake_case__					)
    assert torch.allclose(logits[0, :3, :3]	,     snake_case__	,     atol=1e-4					)
  elif model_name == "blip2-flan-t5-xl-coco":
    _snake_case							:	Union[str, Any]			=						torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]	,     device=snake_case__					)
  else:
    # cast to same type
    _snake_case							:	int			=						logits.dtype
    assert torch.allclose(original_logits.to(snake_case__					)	,     snake_case__	,     atol=1e-2					)
  print("""Looks ok!"""					)

  print("""Generating a caption..."""					)
  _snake_case							:	Any			=						""""""
  _snake_case							:	str			=						tokenizer(snake_case__	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

  _snake_case							:	Union[str, Any]			=						original_model.generate({"""image""": original_pixel_values}					)
  _snake_case							:	Tuple			=						hf_model.generate(
      snake_case__	,     snake_case__	,     do_sample=snake_case__	,     num_beams=5	,     max_length=30	,     min_length=1	,     top_p=0.9	,     repetition_penalty=1.0	,     length_penalty=1.0	,     temperature=1	,     )
  print("""Original generation:"""	,     snake_case__					)
  _snake_case							:	Optional[Any]			=						input_ids.shape[1]
  _snake_case							:	int			=						processor.batch_decode(outputs[:, prompt_length:]	,     skip_special_tokens=snake_case__					)
  _snake_case							:	Optional[Any]			=						[text.strip() for text in output_text]
  print("""HF generation:"""	,     snake_case__					)

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

  if push_to_hub:
    processor.push_to_hub(F"nielsr/{model_name}"					)
    hf_model.push_to_hub(F"nielsr/{model_name}"					)


if __name__ == "__main__":
     A_      			=       argparse.ArgumentParser()
     A_      			=       [
         '''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''',
     )

     A_      			=       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       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Union[str, Any]=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__ (snake_case__							:      str	,     snake_case__							:      List[Any]=0					):
  """simple docstring"""

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

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

    _snake_case							:	int			=						new_item.replace("""emb_layers.1"""	,     """time_emb_proj"""					)
    _snake_case							:	Optional[Any]			=						new_item.replace("""skip_connection"""	,     """conv_shortcut"""					)

    _snake_case							:	str			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Dict=0					):
  """simple docstring"""

  _snake_case							:	Dict			=						[]
  for old_item in old_list:
    _snake_case							:	Dict			=						old_item

    _snake_case							:	int			=						new_item.replace("""norm.weight"""	,     """group_norm.weight"""					)
    _snake_case							:	str			=						new_item.replace("""norm.bias"""	,     """group_norm.bias"""					)

    _snake_case							:	Optional[Any]			=						new_item.replace("""proj_out.weight"""	,     """proj_attn.weight"""					)
    _snake_case							:	Optional[Any]			=						new_item.replace("""proj_out.bias"""	,     """proj_attn.bias"""					)

    _snake_case							:	Optional[Any]			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      List[str]	,     snake_case__							:      str=None	,     snake_case__							:      str=None	,     snake_case__							:      List[str]=None					):
  """simple docstring"""

  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():
      _snake_case							:	Union[str, Any]			=						old_checkpoint[path]
      _snake_case							:	Optional[int]			=						old_tensor.shape[0] // 3

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

      _snake_case							:	Union[str, Any]			=						old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

      _snake_case							:	Union[str, Any]			=						query.reshape(snake_case__					)
      _snake_case							:	Tuple			=						key.reshape(snake_case__					)
      _snake_case							:	Any			=						value.reshape(snake_case__					)

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

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case							:	int			=						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							:	Dict			=						old_checkpoint[path["""old"""]][:, :, 0]
    else:
      _snake_case							:	Optional[Any]			=						old_checkpoint[path["""old"""]]


def       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      List[str]					):
  """simple docstring"""

  _snake_case							:	int			=						{}

  _snake_case							:	Tuple			=						checkpoint["""time_embed.0.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.0.bias"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.2.weight"""]
  _snake_case							:	Tuple			=						checkpoint["""time_embed.2.bias"""]

  _snake_case							:	Dict			=						checkpoint["""input_blocks.0.0.weight"""]
  _snake_case							:	List[Any]			=						checkpoint["""input_blocks.0.0.bias"""]

  _snake_case							:	List[Any]			=						checkpoint["""out.0.weight"""]
  _snake_case							:	Any			=						checkpoint["""out.0.bias"""]
  _snake_case							:	Any			=						checkpoint["""out.2.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""out.2.bias"""]

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

    _snake_case							:	List[str]			=						[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
    _snake_case							:	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:
      _snake_case							:	Union[str, Any]			=						checkpoint[
          F"input_blocks.{i}.0.op.weight"
      ]
      _snake_case							:	Dict			=						checkpoint[
          F"input_blocks.{i}.0.op.bias"
      ]
      continue

    _snake_case							:	Optional[int]			=						renew_resnet_paths(snake_case__					)
    _snake_case							:	int			=						{"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    _snake_case							:	Tuple			=						{"""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__					):
      _snake_case							:	str			=						renew_attention_paths(snake_case__					)
      _snake_case							:	List[str]			=						{
          """old""": F"input_blocks.{i}.1",
          """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
      }
      _snake_case							:	Optional[int]			=						{
          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__	,     )

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

  _snake_case							:	Dict			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Any			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Dict			=						renew_attention_paths(snake_case__					)
  _snake_case							:	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(
      snake_case__	,     snake_case__	,     snake_case__	,     attention_paths_to_split=snake_case__	,     config=snake_case__					)

  for i in range(snake_case__					):
    _snake_case							:	Optional[Any]			=						i // (config["""num_res_blocks"""] + 1)
    _snake_case							:	Dict			=						i % (config["""num_res_blocks"""] + 1)
    _snake_case							:	List[str]			=						[shave_segments(snake_case__	,     2					) for name in output_blocks[i]]
    _snake_case							:	Any			=						{}

    for layer in output_block_layers:
      _snake_case	,      _snake_case							:	Any			=						layer.split("""."""					)[0], shave_segments(snake_case__	,     1					)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(snake_case__					)
      else:
        _snake_case							:	str			=						[layer_name]

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

      _snake_case							:	List[Any]			=						renew_resnet_paths(snake_case__					)
      _snake_case							:	int			=						renew_resnet_paths(snake_case__					)

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

        # Clear attentions as they have been attributed above.
        if len(snake_case__					) == 2:
          _snake_case							:	Any			=						[]

      if len(snake_case__					):
        _snake_case							:	str			=						renew_attention_paths(snake_case__					)
        _snake_case							:	str			=						{
            """old""": F"output_blocks.{i}.1",
            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        _snake_case							:	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(
            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:
      _snake_case							:	Optional[Any]			=						renew_resnet_paths(snake_case__	,     n_shave_prefix_segments=1					)
      for path in resnet_0_paths:
        _snake_case							:	Optional[Any]			=						""".""".join(["""output_blocks""", str(snake_case__					), path["""old"""]]					)
        _snake_case							:	Optional[int]			=						""".""".join(["""up_blocks""", str(snake_case__					), """resnets""", str(snake_case__					), path["""new"""]]					)

        _snake_case							:	Any			=						checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     A_      			=       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_      			=       parser.parse_args()

     A_      			=       torch.load(args.checkpoint_path)

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

     A_      			=       convert_ldm_checkpoint(checkpoint, config)

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

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

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

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


