


"""simple docstring"""






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


__snake_case				=				logging.get_logger(__name__)

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


class 			__lowerCamelCase     (    UpperCAmelCase__ ):

     '''simple docstring'''
     A_				:		Union[str, Any]			=						'blip_2_vision_model'


     def __init__(					self			, __UpperCAmelCase=1408			, __UpperCAmelCase=6144			, __UpperCAmelCase=39			, __UpperCAmelCase=16			, __UpperCAmelCase=224			, __UpperCAmelCase=14			, __UpperCAmelCase="gelu"			, __UpperCAmelCase=0.00001			, __UpperCAmelCase=0.0			, __UpperCAmelCase=1e-1_0			, __UpperCAmelCase=True			, **__UpperCAmelCase			, )				->							Tuple:
          super().__init__(**UpperCAmelCase__							)

          _a					= hidden_size
          _a					= intermediate_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= patch_size
          _a					= image_size
          _a					= initializer_range
          _a					= attention_dropout
          _a					= layer_norm_eps
          _a					= hidden_act
          _a					= qkv_bias





     @classmethod
     def 							_UpperCAmelCase					(					cls			, __UpperCAmelCase			, **__UpperCAmelCase							)				->							"PretrainedConfig":
          cls._set_token_in_kwargs(UpperCAmelCase__							)

          _a		,					_a					= 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":
               _a					= 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 			__lowerCamelCase     (    UpperCAmelCase__ ):

     '''simple docstring'''
     A_				:		int			=						'blip_2_qformer'


     def __init__(					self			, __UpperCAmelCase=30522			, __UpperCAmelCase=768			, __UpperCAmelCase=12			, __UpperCAmelCase=12			, __UpperCAmelCase=3072			, __UpperCAmelCase="gelu"			, __UpperCAmelCase=0.1			, __UpperCAmelCase=0.1			, __UpperCAmelCase=512			, __UpperCAmelCase=0.02			, __UpperCAmelCase=1e-1_2			, __UpperCAmelCase=0			, __UpperCAmelCase="absolute"			, __UpperCAmelCase=2			, __UpperCAmelCase=1408			, **__UpperCAmelCase			, )				->							Union[str, Any]:
          super().__init__(pad_token_id=UpperCAmelCase__			, **UpperCAmelCase__							)

          _a					= vocab_size
          _a					= hidden_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= hidden_act
          _a					= intermediate_size
          _a					= hidden_dropout_prob
          _a					= attention_probs_dropout_prob
          _a					= max_position_embeddings
          _a					= initializer_range
          _a					= layer_norm_eps
          _a					= position_embedding_type
          _a					= cross_attention_frequency
          _a					= encoder_hidden_size





     @classmethod
     def 							_UpperCAmelCase					(					cls			, __UpperCAmelCase			, **__UpperCAmelCase							)				->							"PretrainedConfig":
          cls._set_token_in_kwargs(UpperCAmelCase__							)

          _a		,					_a					= 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":
               _a					= 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 			__lowerCamelCase     (    UpperCAmelCase__ ):

     '''simple docstring'''
     A_				:		int			=						'blip-2'
     A_				:		int			=						True


     def __init__(					self			, __UpperCAmelCase=None			, __UpperCAmelCase=None			, __UpperCAmelCase=None			, __UpperCAmelCase=32			, **__UpperCAmelCase							)				->							List[str]:
          super().__init__(**UpperCAmelCase__							)

          if vision_config is None:
               _a					= {}
               logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.'''							)

          if qformer_config is None:
               _a					= {}
               logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.'''							)

          if text_config is None:
               _a					= {}
               logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''							)

          _a					= BlipaVisionConfig(**UpperCAmelCase__							)
          _a					= BlipaQFormerConfig(**UpperCAmelCase__							)
          _a					= text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
          _a					= CONFIG_MAPPING[text_model_type](**UpperCAmelCase__							)

          _a					= self.text_config.tie_word_embeddings
          _a					= self.text_config.is_encoder_decoder

          _a					= num_query_tokens
          _a					= self.vision_config.hidden_size
          _a					= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          _a					= 1.0
          _a					= 0.02


     @classmethod
     def 							_UpperCAmelCase					(					cls			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, **__UpperCAmelCase			, )				->							Union[str, Any]:
          return cls(
              vision_config=vision_config.to_dict()			, qformer_config=qformer_config.to_dict()			, text_config=text_config.to_dict()			, **UpperCAmelCase__			, )





     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= copy.deepcopy(self.__dict__							)
          _a					= self.vision_config.to_dict()
          _a					= self.qformer_config.to_dict()
          _a					= self.text_config.to_dict()
          _a					= self.__class__.model_type
          return output





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

       # directly update weight in diffusers model
       for key in state_dict:
              # it is suggested to print out the key, it usually will be something like below
              # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

              # as we have set the alpha beforehand, so just skip
              if ".alpha" in key or key in visited:
                     continue

              if "text" in key:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.text_encoder
              else:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_UNET + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.unet

              # find the target layer
              A__       =    layer_infos.pop(0						)
              while len(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 0:
                                   temp_name += "_" + layer_infos.pop(0						)
                            else:
                                   A__       =    layer_infos.pop(0						)

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

              # update weight
              if len(state_dict[pair_keys[0]].shape						) == 4:
                     A__       =    state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						).unsqueeze(2						).unsqueeze(3						)
              else:
                     A__       =    state_dict[pair_keys[0]].to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						)

              # update visited list
              for item in pair_keys:
                     visited.append(lowercase_						)

       return pipeline


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  _lowerCamelCase	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


