



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


lowercase_	       =			logging.get_logger(__name__)

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






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "instructblip_vision_model"



	def __init__(						self							:    Any				,	snake_case_							:    Optional[int]=14_08				,	snake_case_							:    Any=61_44				,	snake_case_							:    str=39				,	snake_case_							:    Union[str, Any]=16				,	snake_case_							:    Tuple=2_24				,	snake_case_							:    Dict=14				,	snake_case_							:    Dict="gelu"				,	snake_case_							:    Optional[int]=1e-6				,	snake_case_							:    Any=0.0				,	snake_case_							:    Optional[int]=1e-10				,	snake_case_							:    List[str]=True				,	**snake_case_							:    Tuple				,	)->							str:
						super().__init__(**snake_case_)

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



	@classmethod
	def 		UpperCamelCase							(						cls							:    Optional[int]				,	snake_case_							:    Union[str, os.PathLike]				,	**snake_case_							:    Any)->							"PretrainedConfig":
						cls._set_token_in_kwargs(snake_case_)

						__lowerCAmelCase   ,	__lowerCAmelCase	   =cls.get_config_dict(snake_case_				,	**snake_case_)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get("""model_type""") == "instructblip":
											__lowerCAmelCase	   =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				):
	SCREAMING_SNAKE_CASE       =       "instructblip_qformer"



	def __init__(						self							:    Dict				,	snake_case_							:    List[Any]=3_05_22				,	snake_case_							:    Dict=7_68				,	snake_case_							:    List[Any]=12				,	snake_case_							:    Union[str, Any]=12				,	snake_case_							:    Any=30_72				,	snake_case_							:    str="gelu"				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    str=0.1				,	snake_case_							:    List[Any]=5_12				,	snake_case_							:    Dict=0.0_2				,	snake_case_							:    Any=1e-12				,	snake_case_							:    str=0				,	snake_case_							:    int="absolute"				,	snake_case_							:    Optional[Any]=2				,	snake_case_							:    Any=14_08				,	**snake_case_							:    Optional[int]				,	)->							str:
						super().__init__(pad_token_id=snake_case_				,	**snake_case_)

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



	@classmethod
	def 		UpperCamelCase							(						cls							:    str				,	snake_case_							:    Union[str, os.PathLike]				,	**snake_case_							:    Tuple)->							"PretrainedConfig":
						cls._set_token_in_kwargs(snake_case_)

						__lowerCAmelCase   ,	__lowerCAmelCase	   =cls.get_config_dict(snake_case_				,	**snake_case_)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get("""model_type""") == "instructblip":
											__lowerCAmelCase	   =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				):
	SCREAMING_SNAKE_CASE       =       "instructblip"
	SCREAMING_SNAKE_CASE       =       True



	def __init__(						self							:    Optional[Any]				,	snake_case_							:    List[Any]=None				,	snake_case_							:    Optional[int]=None				,	snake_case_							:    Any=None				,	snake_case_							:    int=32				,	**snake_case_							:    Optional[int])->							Dict:
						super().__init__(**snake_case_)

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

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

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

						__lowerCAmelCase	   =InstructBlipVisionConfig(**snake_case_)
						__lowerCAmelCase	   =InstructBlipQFormerConfig(**snake_case_)
						__lowerCAmelCase	   =text_config["""model_type"""] if """model_type""" in text_config else """opt"""
						__lowerCAmelCase	   =CONFIG_MAPPING[text_model_type](**snake_case_)

						__lowerCAmelCase	   =self.text_config.tie_word_embeddings
						__lowerCAmelCase	   =self.text_config.is_encoder_decoder

						__lowerCAmelCase	   =num_query_tokens
						__lowerCAmelCase	   =self.vision_config.hidden_size
						__lowerCAmelCase	   =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						__lowerCAmelCase	   =1.0
						__lowerCAmelCase	   =0.0_2



	@classmethod
	def 		UpperCamelCase							(						cls							:    List[Any]				,	snake_case_							:    InstructBlipVisionConfig				,	snake_case_							:    InstructBlipQFormerConfig				,	snake_case_							:    PretrainedConfig				,	**snake_case_							:    int				,	)->							Union[str, Any]:
						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							:    Dict)->							Tuple:
						__lowerCAmelCase	   =copy.deepcopy(self.__dict__)
						__lowerCAmelCase	   =self.vision_config.to_dict()
						__lowerCAmelCase	   =self.qformer_config.to_dict()
						__lowerCAmelCase	   =self.text_config.to_dict()
						__lowerCAmelCase	   =self.__class__.model_type
						return output









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




