





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase     :     Optional[int]							=	False
UpperCAmelCase     :     Optional[int]							=	True
UpperCAmelCase     :     Dict							=	False


if __name__ == "__main__":
     UpperCAmelCase     :     Optional[Any]							=	argparse.ArgumentParser()

     parser.add_argument(
         """--repo_path""",
         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     :     int							=	parser.parse_args()

     UpperCAmelCase     :     Optional[int]							=	{
         """image_size""": """sample_size""",
         """num_res_blocks""": """layers_per_block""",
         """block_channels""": """block_out_channels""",
         """down_blocks""": """down_block_types""",
         """up_blocks""": """up_block_types""",
         """downscale_freq_shift""": """freq_shift""",
         """resnet_num_groups""": """norm_num_groups""",
         """resnet_act_fn""": """act_fn""",
         """resnet_eps""": """norm_eps""",
         """num_head_channels""": """attention_head_dim""",
     }

     UpperCAmelCase     :     Dict							=	{
         """time_steps""": """time_proj""",
         """mid""": """mid_block""",
         """downsample_blocks""": """down_blocks""",
         """upsample_blocks""": """up_blocks""",
     }

     UpperCAmelCase     :     List[str]							=	"""""" if has_file(args.repo_path, """config.json""") else """unet"""

     with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
          UpperCAmelCase     :     List[Any]							=	reader.read()
          UpperCAmelCase     :     Optional[Any]							=	json.loads(text)

     if do_only_config:
          for key in config_parameters_to_change.keys():
               config.pop(key, None)

     if has_file(args.repo_path, """config.json"""):
          UpperCAmelCase     :     str							=	UNetaDModel(**config)
     else:
          UpperCAmelCase     :     Optional[int]							=	UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
          UpperCAmelCase     :     Optional[int]							=	class_name(**config)

     if do_only_config:
          model.save_config(os.path.join(args.repo_path, subfolder))

     UpperCAmelCase     :     Dict							=	dict(model.config)

     if do_only_renaming:
          for key, value in config_parameters_to_change.items():
               if key in config:
                    UpperCAmelCase     :     str							=	config[key]
                    del config[key]

          UpperCAmelCase     :     Optional[Any]							=	[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
          UpperCAmelCase     :     Optional[int]							=	[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

     if do_only_weights:
          UpperCAmelCase     :     List[Any]							=	torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

          UpperCAmelCase     :     Optional[int]							=	{}
          for param_key, param_value in state_dict.items():
               if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
                    continue
               UpperCAmelCase     :     List[str]							=	False
               for key, new_key in key_parameters_to_change.items():
                    if not has_changed and param_key.split(""".""")[0] == key:
                         UpperCAmelCase     :     List[str]							=	param_value
                         UpperCAmelCase     :     List[Any]							=	True
               if not has_changed:
                    UpperCAmelCase     :     Tuple							=	param_value

          model.load_state_dict(new_state_dict)
          model.save_pretrained(os.path.join(args.repo_path, subfolder))













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


UpperCAmelCase     :     List[Any]							=	logging.get_logger(__name__)

UpperCAmelCase     :     Tuple							=	{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_vision_model"""







      def __init__(   self		,				lowerCAmelCase__=1_4_0_8		,				lowerCAmelCase__=6_1_4_4		,				lowerCAmelCase__=3_9		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=2_2_4		,				lowerCAmelCase__=1_4		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1E-6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=1E-10		,				lowerCAmelCase__=True		,				**lowerCAmelCase__		,				)       ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

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







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : Optional[Any]  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"							) == "instructblip":
          a__     : 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(lowerCAmelCase__		,				**lowerCAmelCase__							)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_qformer"""







      def __init__(   self		,				lowerCAmelCase__=3_0_5_2_2		,				lowerCAmelCase__=7_6_8		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=3_0_7_2		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=5_1_2		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=1E-12		,				lowerCAmelCase__=0		,				lowerCAmelCase__="absolute"		,				lowerCAmelCase__=2		,				lowerCAmelCase__=1_4_0_8		,				**lowerCAmelCase__		,				)       ->						Dict:



        '''simple docstring'''




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

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







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : str  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"							) == "instructblip":
          a__     : Optional[int]  =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(lowerCAmelCase__		,				**lowerCAmelCase__							)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip"""
      _lowercase    : List[Any]            =      True







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=3_2		,				**lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        if vision_config is None:
          a__     : List[Any]  ={}
          logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."							)

        if qformer_config is None:
          a__     : Tuple  ={}
          logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."							)

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

        a__     : Dict  =InstructBlipVisionConfig(**lowerCAmelCase__							)
        a__     : Union[str, Any]  =InstructBlipQFormerConfig(**lowerCAmelCase__							)
        a__     : Tuple  =text_config["model_type"] if "model_type" in text_config else "opt"
        a__     : List[str]  =CONFIG_MAPPING[text_model_type](**lowerCAmelCase__							)

        a__     : Union[str, Any]  =self.text_config.tie_word_embeddings
        a__     : Optional[Any]  =self.text_config.is_encoder_decoder

        a__     : str  =num_query_tokens
        a__     : List[Any]  =self.vision_config.hidden_size
        a__     : str  =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        a__     : List[Any]  =1.0
        a__     : List[str]  =0.02







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




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






