






"""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


__a       :	Any             = logging.get_logger(__name__)

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    ="instructblip_vision_model"




       def __init__(   self:     Optional[int]				,		__A:     Optional[int]=1408				,		__A:     Dict=6144				,		__A:     Dict=39				,		__A:     Tuple=16				,		__A:     Union[str, Any]=224				,		__A:     str=14				,		__A:     Any="gelu"				,		__A:     Union[str, Any]=1e-6				,		__A:     Tuple=0.0				,		__A:     str=1e-10				,		__A:     int=True				,		**__A:     Optional[int]				,		):

            '''simple docstring'''

            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  lowercase		(   cls:     Any				,		__A:     Any				,		**__A:     Optional[int]    ):

            '''simple docstring'''

            cls._set_token_in_kwargs(UpperCamelCase_    )

            a__          =  cls.get_config_dict(UpperCamelCase_				,		**UpperCamelCase_    )

            # get the vision config dict if we are loading from InstructBlipConfig
            if config_dict.get('''model_type'''    ) == "instructblip":
                 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        _SCREAMING_SNAKE_CASE						(  __snake_case							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    ="instructblip_qformer"




       def __init__(   self:     Dict				,		__A:     Dict=30522				,		__A:     int=768				,		__A:     List[Any]=12				,		__A:     Optional[Any]=12				,		__A:     Optional[Any]=3072				,		__A:     List[str]="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[Any]=0.1				,		__A:     Dict=512				,		__A:     Optional[Any]=0.0_2				,		__A:     str=1e-12				,		__A:     str=0				,		__A:     str="absolute"				,		__A:     Union[str, Any]=2				,		__A:     List[Any]=1408				,		**__A:     Dict				,		):

            '''simple docstring'''

            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  lowercase		(   cls:     Optional[int]				,		__A:     str				,		**__A:     Optional[Any]    ):

            '''simple docstring'''

            cls._set_token_in_kwargs(UpperCamelCase_    )

            a__          =  cls.get_config_dict(UpperCamelCase_				,		**UpperCamelCase_    )

            # get the qformer config dict if we are loading from InstructBlipConfig
            if config_dict.get('''model_type'''    ) == "instructblip":
                 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        _SCREAMING_SNAKE_CASE						(  __snake_case							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    ="instructblip"
       _SCREAMING_SNAKE_CASE    =True




       def __init__(   self:     Union[str, Any]				,		__A:     Union[str, Any]=None				,		__A:     List[str]=None				,		__A:     Optional[Any]=None				,		__A:     int=32				,		**__A:     Optional[int]    ):

            '''simple docstring'''

            super().__init__(**UpperCamelCase_    )

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

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

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

            a__          =  InstructBlipVisionConfig(**UpperCamelCase_    )
            a__          =  InstructBlipQFormerConfig(**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__          =  0.0_2




       @classmethod
       def  lowercase		(   cls:     List[Any]				,		__A:     List[Any]				,		__A:     List[Any]				,		__A:     List[str]				,		**__A:     List[Any]				,		):

            '''simple docstring'''

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





       def  lowercase		(   self:     Union[str, Any]    ):

            '''simple docstring'''

            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










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


