





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__A             =       [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

__A             =       [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

__A             =       []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
     # loop over downblocks/upblocks

     for j in range(2):
          # loop over resnets/attentions for downblocks
          __A             =       f'down_blocks.{i}.resnets.{j}.'
          __A             =       f'input_blocks.{3*i + j + 1}.0.'
          unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

          if i < 3:
               # no attention layers in down_blocks.3
               __A             =       f'down_blocks.{i}.attentions.{j}.'
               __A             =       f'input_blocks.{3*i + j + 1}.1.'
               unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

     for j in range(3):
          # loop over resnets/attentions for upblocks
          __A             =       f'up_blocks.{i}.resnets.{j}.'
          __A             =       f'output_blocks.{3*i + j}.0.'
          unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

          if i > 0:
               # no attention layers in up_blocks.0
               __A             =       f'up_blocks.{i}.attentions.{j}.'
               __A             =       f'output_blocks.{3*i + j}.1.'
               unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

     if i < 3:
          # no downsample in down_blocks.3
          __A             =       f'down_blocks.{i}.downsamplers.0.conv.'
          __A             =       f'input_blocks.{3*(i+1)}.0.op.'
          unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

          # no upsample in up_blocks.3
          __A             =       f'up_blocks.{i}.upsamplers.0.'
          __A             =       f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__A             =       "mid_block.attentions.0."
__A             =       "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     __A             =       f'mid_block.resnets.{j}.'
     __A             =       f'middle_block.{2*j}.'
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def   lowerCAmelCase_				( __a	)  ->	Any:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						={k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    lowerCamelCase__:   Union[str, Any]    						=sd_name
  for k, v in mapping.items():
    if "resnets" in k:
      for sd_part, hf_part in unet_conversion_map_resnet:
        lowerCamelCase__:   List[str]    						=v.replace(__a			,					__a	)
      lowerCamelCase__:   Tuple    						=v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      lowerCamelCase__:   Union[str, Any]    						=v.replace(__a			,					__a	)
    lowerCamelCase__:   Optional[int]    						=v
  lowerCamelCase__:   Any    						={v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


# ================#
# VAE Conversion #
# ================#

__A             =       [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
     # down_blocks have two resnets
     for j in range(2):
          __A             =       f'encoder.down_blocks.{i}.resnets.{j}.'
          __A             =       f'encoder.down.{i}.block.{j}.'
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          __A             =       f'down_blocks.{i}.downsamplers.0.'
          __A             =       f'down.{i}.downsample.'
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          __A             =       f'up_blocks.{i}.upsamplers.0.'
          __A             =       f'up.{3-i}.upsample.'
          vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

     # up_blocks have three resnets
     # also, up blocks in hf are numbered in reverse from sd
     for j in range(3):
          __A             =       f'decoder.up_blocks.{i}.resnets.{j}.'
          __A             =       f'decoder.up.{3-i}.block.{j}.'
          vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
     __A             =       f'mid_block.resnets.{i}.'
     __A             =       f'mid.block_{i+1}.'
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__A             =       [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]





def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  return w.reshape(*w.shape			,					1			,					1	)





def   lowerCAmelCase_				( __a	)  ->	int:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      lowerCamelCase__:   Optional[int]    						=v.replace(__a			,					__a	)
    lowerCamelCase__:   Optional[Any]    						=v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        lowerCamelCase__:   Optional[int]    						=v.replace(__a			,					__a	)
      lowerCamelCase__:   int    						=v
  lowerCamelCase__:   Optional[Any]    						={v: vae_state_dict[k] for k, v in mapping.items()}
  lowerCamelCase__:   Tuple    						=["q", "k", "v", "proj_out"]
  for k, v in new_state_dict.items():
    for weight_name in weights_to_convert:
      if F"""mid.attn_1.{weight_name}.weight""" in k:
        print(F"""Reshaping {k} for SD format"""	)
        lowerCamelCase__:   List[Any]    						=reshape_weight_for_sd(__a	)
  return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__A             =       [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__A             =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__A             =       re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__A             =       {"q": 0, "k": 1, "v": 2}





def   lowerCAmelCase_				( __a	)  ->	Optional[int]:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						={}
  lowerCamelCase__:   str    						={}
  lowerCamelCase__:   Union[str, Any]    						={}
  for k, v in text_enc_dict.items():
    if (
        k.endswith(".self_attn.q_proj.weight"	)
        or k.endswith(".self_attn.k_proj.weight"	)
        or k.endswith(".self_attn.v_proj.weight"	)
    ):
      lowerCamelCase__:   Any    						=k[: -len(".q_proj.weight"	)]
      lowerCamelCase__:   Tuple    						=k[-len("q_proj.weight"	)]
      if k_pre not in capture_qkv_weight:
        lowerCamelCase__:   Union[str, Any]    						=[None, None, None]
      lowerCamelCase__:   Dict    						=v
      continue

    if (
        k.endswith(".self_attn.q_proj.bias"	)
        or k.endswith(".self_attn.k_proj.bias"	)
        or k.endswith(".self_attn.v_proj.bias"	)
    ):
      lowerCamelCase__:   Dict    						=k[: -len(".q_proj.bias"	)]
      lowerCamelCase__:   int    						=k[-len("q_proj.bias"	)]
      if k_pre not in capture_qkv_bias:
        lowerCamelCase__:   Tuple    						=[None, None, None]
      lowerCamelCase__:   int    						=v
      continue

    lowerCamelCase__:   str    						=textenc_pattern.sub(lambda __a	: protected[re.escape(m.group(0	)	)]			,					__a	)
    lowerCamelCase__:   Union[str, Any]    						=v

  for k_pre, tensors in capture_qkv_weight.items():
    if None in tensors:
      raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"	)
    lowerCamelCase__:   Optional[Any]    						=textenc_pattern.sub(lambda __a	: protected[re.escape(m.group(0	)	)]			,					__a	)
    lowerCamelCase__:   Any    						=torch.cat(__a	)

  for k_pre, tensors in capture_qkv_bias.items():
    if None in tensors:
      raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"	)
    lowerCamelCase__:   Optional[int]    						=textenc_pattern.sub(lambda __a	: protected[re.escape(m.group(0	)	)]			,					__a	)
    lowerCamelCase__:   Tuple    						=torch.cat(__a	)

  return new_state_dict





def   lowerCAmelCase_				( __a	)  ->	Dict:







  """simple docstring"""





  return text_enc_dict


if __name__ == "__main__":
     __A             =       argparse.ArgumentParser()

     parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
     parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
     parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
     parser.add_argument(
         "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
     )

     __A             =       parser.parse_args()

     assert args.model_path is not None, "Must provide a model path!"

     assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

     # Path for safetensors
     __A             =       osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
     __A             =       osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
     __A             =       osp.join(args.model_path, "text_encoder", "model.safetensors")

     # Load models from safetensors if it exists, if it doesn't pytorch
     if osp.exists(unet_path):
          __A             =       load_file(unet_path, device="cpu")
     else:
          __A             =       osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
          __A             =       torch.load(unet_path, map_location="cpu")

     if osp.exists(vae_path):
          __A             =       load_file(vae_path, device="cpu")
     else:
          __A             =       osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
          __A             =       torch.load(vae_path, map_location="cpu")

     if osp.exists(text_enc_path):
          __A             =       load_file(text_enc_path, device="cpu")
     else:
          __A             =       osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
          __A             =       torch.load(text_enc_path, map_location="cpu")

     # Convert the UNet model
     __A             =       convert_unet_state_dict(unet_state_dict)
     __A             =       {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     __A             =       convert_vae_state_dict(vae_state_dict)
     __A             =       {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

     # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
     __A             =       "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

     if is_vaa_model:
          # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
          __A             =       {"transformer." + k: v for k, v in text_enc_dict.items()}
          __A             =       convert_text_enc_state_dict_vaa(text_enc_dict)
          __A             =       {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
     else:
          __A             =       convert_text_enc_state_dict(text_enc_dict)
          __A             =       {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

     # Put together new checkpoint
     __A             =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
     if args.half:
          __A             =       {k: v.half() for k, v in state_dict.items()}

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          __A             =       {"state_dict": state_dict}
          torch.save(state_dict, args.checkpoint_path)








import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A             =       logging.get_logger(__name__)

__A             =       {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    "blip_2_vision_model"

 def __init__(self       :     Union[str, Any] ,				UpperCAmelCase_       :     int=1_408 ,				UpperCAmelCase_       :     List[str]=6_144 ,				UpperCAmelCase_       :     List[Any]=39 ,				UpperCAmelCase_       :     Tuple=16 ,				UpperCAmelCase_       :     List[str]=224 ,				UpperCAmelCase_       :     Any=14 ,				UpperCAmelCase_       :     Dict="gelu" ,				UpperCAmelCase_       :     str=0.0_0001 ,				UpperCAmelCase_       :     List[str]=0.0 ,				UpperCAmelCase_       :     str=1E-1_0 ,				UpperCAmelCase_       :     Any=True ,				**UpperCAmelCase_       :     Optional[Any] ,				) ->Optional[int]:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   lowerCamelCase__:   Any    						=hidden_size
   lowerCamelCase__:   Any    						=intermediate_size
   lowerCamelCase__:   Union[str, Any]    						=num_hidden_layers
   lowerCamelCase__:   Optional[Any]    						=num_attention_heads
   lowerCamelCase__:   Dict    						=patch_size
   lowerCamelCase__:   List[Any]    						=image_size
   lowerCamelCase__:   Union[str, Any]    						=initializer_range
   lowerCamelCase__:   Optional[Any]    						=attention_dropout
   lowerCamelCase__:   Union[str, Any]    						=layer_norm_eps
   lowerCamelCase__:   Dict    						=hidden_act
   lowerCamelCase__:   Union[str, Any]    						=qkv_bias







 @classmethod
 def 							SCREAMING_SNAKE_CASE_      (cls       :     Optional[int] ,				UpperCAmelCase_       :     Union[str, os.PathLike] ,				**UpperCAmelCase_       :     List[Any]) ->"PretrainedConfig":



   '''simple docstring'''



   cls._set_token_in_kwargs(UpperCAmelCase_)

   lowerCamelCase__						,      lowerCamelCase__:   str    						=cls.get_config_dict(UpperCAmelCase_ ,				**UpperCAmelCase_)

   # get the vision config dict if we are loading from Blip2Config
   if config_dict.get("model_type") == "blip-2":
     lowerCamelCase__:   Any    						=config_dict["vision_config"]

   if "model_type" in config_dict and hasattr(cls ,				"model_type") and config_dict["model_type"] != cls.model_type:
     logger.warning(
         F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
         F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

   return cls.from_dict(UpperCAmelCase_ ,				**UpperCAmelCase_)



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    "blip_2_qformer"

 def __init__(self       :     str ,				UpperCAmelCase_       :     Any=30_522 ,				UpperCAmelCase_       :     Union[str, Any]=768 ,				UpperCAmelCase_       :     Tuple=12 ,				UpperCAmelCase_       :     Union[str, Any]=12 ,				UpperCAmelCase_       :     Optional[int]=3_072 ,				UpperCAmelCase_       :     Any="gelu" ,				UpperCAmelCase_       :     Optional[int]=0.1 ,				UpperCAmelCase_       :     List[Any]=0.1 ,				UpperCAmelCase_       :     Optional[int]=512 ,				UpperCAmelCase_       :     List[Any]=0.02 ,				UpperCAmelCase_       :     Optional[Any]=1E-1_2 ,				UpperCAmelCase_       :     Dict=0 ,				UpperCAmelCase_       :     Optional[int]="absolute" ,				UpperCAmelCase_       :     List[Any]=2 ,				UpperCAmelCase_       :     int=1_408 ,				**UpperCAmelCase_       :     Optional[int] ,				) ->List[str]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_ ,				**UpperCAmelCase_)

   lowerCamelCase__:   Optional[int]    						=vocab_size
   lowerCamelCase__:   Dict    						=hidden_size
   lowerCamelCase__:   Tuple    						=num_hidden_layers
   lowerCamelCase__:   List[Any]    						=num_attention_heads
   lowerCamelCase__:   Optional[Any]    						=hidden_act
   lowerCamelCase__:   Optional[Any]    						=intermediate_size
   lowerCamelCase__:   Dict    						=hidden_dropout_prob
   lowerCamelCase__:   Any    						=attention_probs_dropout_prob
   lowerCamelCase__:   Union[str, Any]    						=max_position_embeddings
   lowerCamelCase__:   Optional[Any]    						=initializer_range
   lowerCamelCase__:   List[Any]    						=layer_norm_eps
   lowerCamelCase__:   Tuple    						=position_embedding_type
   lowerCamelCase__:   List[Any]    						=cross_attention_frequency
   lowerCamelCase__:   Tuple    						=encoder_hidden_size







 @classmethod
 def 							SCREAMING_SNAKE_CASE_      (cls       :     Union[str, Any] ,				UpperCAmelCase_       :     Union[str, os.PathLike] ,				**UpperCAmelCase_       :     List[Any]) ->"PretrainedConfig":



   '''simple docstring'''



   cls._set_token_in_kwargs(UpperCAmelCase_)

   lowerCamelCase__						,      lowerCamelCase__:   Tuple    						=cls.get_config_dict(UpperCAmelCase_ ,				**UpperCAmelCase_)

   # get the qformer config dict if we are loading from Blip2Config
   if config_dict.get("model_type") == "blip-2":
     lowerCamelCase__:   Any    						=config_dict["qformer_config"]

   if "model_type" in config_dict and hasattr(cls ,				"model_type") and config_dict["model_type"] != cls.model_type:
     logger.warning(
         F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
         F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

   return cls.from_dict(UpperCAmelCase_ ,				**UpperCAmelCase_)



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    "blip-2"
 lowercase_	=    True

 def __init__(self       :     Any ,				UpperCAmelCase_       :     Optional[int]=None ,				UpperCAmelCase_       :     Tuple=None ,				UpperCAmelCase_       :     List[str]=None ,				UpperCAmelCase_       :     int=32 ,				**UpperCAmelCase_       :     str) ->List[str]:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   if vision_config is None:
     lowerCamelCase__:   Optional[int]    						={}
     logger.info("vision_config is None. initializing the Blip2VisionConfig with default values.")

   if qformer_config is None:
     lowerCamelCase__:   str    						={}
     logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values.")

   if text_config is None:
     lowerCamelCase__:   Union[str, Any]    						={}
     logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")

   lowerCamelCase__:   Optional[Any]    						=BlipaVisionConfig(**UpperCAmelCase_)
   lowerCamelCase__:   Optional[int]    						=BlipaQFormerConfig(**UpperCAmelCase_)
   lowerCamelCase__:   Union[str, Any]    						=text_config["model_type"] if "model_type" in text_config else "opt"
   lowerCamelCase__:   Dict    						=CONFIG_MAPPING[text_model_type](**UpperCAmelCase_)

   lowerCamelCase__:   Optional[int]    						=self.text_config.tie_word_embeddings
   lowerCamelCase__:   List[str]    						=self.text_config.is_encoder_decoder

   lowerCamelCase__:   Dict    						=num_query_tokens
   lowerCamelCase__:   Optional[Any]    						=self.vision_config.hidden_size
   lowerCamelCase__:   Tuple    						=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
   lowerCamelCase__:   List[Any]    						=1.0
   lowerCamelCase__:   Union[str, Any]    						=0.02

 @classmethod
 def 							SCREAMING_SNAKE_CASE_      (cls       :     Any ,				UpperCAmelCase_       :     BlipaVisionConfig ,				UpperCAmelCase_       :     BlipaQFormerConfig ,				UpperCAmelCase_       :     PretrainedConfig ,				**UpperCAmelCase_       :     int ,				) ->Optional[int]:



   '''simple docstring'''



   return cls(
       vision_config=vision_config.to_dict() ,				qformer_config=qformer_config.to_dict() ,				text_config=text_config.to_dict() ,				**UpperCAmelCase_ ,				)







 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=copy.deepcopy(self.__dict__)
   lowerCamelCase__:   Any    						=self.vision_config.to_dict()
   lowerCamelCase__:   Any    						=self.qformer_config.to_dict()
   lowerCamelCase__:   Any    						=self.text_config.to_dict()
   lowerCamelCase__:   int    						=self.__class__.model_type
   return output

