





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		:  Dict         =	logging.get_logger(__name__)

UpperCAmelCase		:  List[Any]         =	{
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}





class 		_A(   SCREAMING_SNAKE_CASE_  ):


				"""simple docstring"""


				UpperCamelCase       :    Union[str, Any]          =      '''blip_2_vision_model'''



				def __init__(     self	,   _A=1408	,   _A=6144	,   _A=39	,   _A=16	,   _A=224	,   _A=14	,   _A="gelu"	,   _A=0.0_0_0_0_1	,   _A=0.0	,   _A=1e-1_0	,   _A=True	,   **_A	,   ):
									super().__init__(**__snake_case	)

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





				@classmethod
				def    UpperCAmelCase_						(     cls	,   _A	,   **_A	):
									cls._set_token_in_kwargs(__snake_case	)

									__A      :		Union[str, Any]         =							cls.get_config_dict(__snake_case	,   **__snake_case	)

									# get the vision config dict if we are loading from Blip2Config
									if config_dict.get('model_type'	) == "blip-2":
														__A      :		int         =							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(__snake_case	,   **__snake_case	)





class 		_A(   SCREAMING_SNAKE_CASE_  ):


				"""simple docstring"""


				UpperCamelCase       :    Tuple          =      '''blip_2_qformer'''



				def __init__(     self	,   _A=30522	,   _A=768	,   _A=12	,   _A=12	,   _A=3072	,   _A="gelu"	,   _A=0.1	,   _A=0.1	,   _A=512	,   _A=0.0_2	,   _A=1e-1_2	,   _A=0	,   _A="absolute"	,   _A=2	,   _A=1408	,   **_A	,   ):
									super().__init__(pad_token_id=__snake_case	,   **__snake_case	)

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





				@classmethod
				def    UpperCAmelCase_						(     cls	,   _A	,   **_A	):
									cls._set_token_in_kwargs(__snake_case	)

									__A      :		Optional[Any]         =							cls.get_config_dict(__snake_case	,   **__snake_case	)

									# get the qformer config dict if we are loading from Blip2Config
									if config_dict.get('model_type'	) == "blip-2":
														__A      :		List[str]         =							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(__snake_case	,   **__snake_case	)





class 		_A(   SCREAMING_SNAKE_CASE_  ):


				"""simple docstring"""


				UpperCamelCase       :    Any          =      '''blip-2'''
				UpperCamelCase       :    int          =      True



				def __init__(     self	,   _A=None	,   _A=None	,   _A=None	,   _A=32	,   **_A	):
									super().__init__(**__snake_case	)

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

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

									if text_config is None:
														__A      :		Optional[Any]         =							{}
														logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'	)

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

									__A      :		Tuple         =							self.text_config.tie_word_embeddings
									__A      :		Optional[int]         =							self.text_config.is_encoder_decoder

									__A      :		Optional[Any]         =							num_query_tokens
									__A      :		int         =							self.vision_config.hidden_size
									__A      :		List[Any]         =							self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									__A      :		Tuple         =							1.0
									__A      :		Any         =							0.0_2



				@classmethod
				def    UpperCAmelCase_						(     cls	,   _A	,   _A	,   _A	,   **_A	,   ):
									return cls(
									    vision_config=vision_config.to_dict()	,   qformer_config=qformer_config.to_dict()	,   text_config=text_config.to_dict()	,   **__snake_case	,   )





				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[int]         =							copy.deepcopy(self.__dict__	)
									__A      :		List[Any]         =							self.vision_config.to_dict()
									__A      :		int         =							self.qformer_config.to_dict()
									__A      :		Dict         =							self.text_config.to_dict()
									__A      :		Optional[int]         =							self.__class__.model_type
									return output












"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



