


'''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


lowerCAmelCase :Dict             =     logging.get_logger(__name__)

lowerCAmelCase :Optional[Any]             =     {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class _lowerCamelCase			(		__UpperCamelCase				):



		'''simple docstring'''







		A_      :     Union[str, Any]        =							'instructblip_vision_model'







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

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







		@classmethod
		def   __lowerCAmelCase						(   cls						:		Dict    ,	_A						:		List[Any]    ,	**_A						:		Optional[int]     )    ->					Union[str, Any]:
			cls._set_token_in_kwargs(_A     )

			__magic_name__      ,							__magic_name__			:       Tuple	      =	cls.get_config_dict(_A    ,	**_A     )

			# get the vision config dict if we are loading from InstructBlipConfig
			if config_dict.get('model_type'     ) == "instructblip":
				__magic_name__			:       Optional[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(_A    ,	**_A     )







class _lowerCamelCase			(		__UpperCamelCase				):



		'''simple docstring'''







		A_      :     int        =							'instructblip_qformer'







		def __init__(   self						:		List[Any]    ,	_A						:		List[str]=30522    ,	_A						:		Dict=768    ,	_A						:		int=12    ,	_A						:		Optional[int]=12    ,	_A						:		List[str]=3072    ,	_A						:		List[str]="gelu"    ,	_A						:		int=0.1    ,	_A						:		Tuple=0.1    ,	_A						:		Union[str, Any]=512    ,	_A						:		Tuple=0.02    ,	_A						:		Optional[int]=1E-12    ,	_A						:		List[str]=0    ,	_A						:		str="absolute"    ,	_A						:		Optional[Any]=2    ,	_A						:		str=1408    ,	**_A						:		List[str]    ,	)    ->					Optional[Any]:
			super().__init__(pad_token_id=_A    ,	**_A     )

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







		@classmethod
		def   __lowerCAmelCase						(   cls						:		List[Any]    ,	_A						:		Optional[int]    ,	**_A						:		Any     )    ->					Union[str, Any]:
			cls._set_token_in_kwargs(_A     )

			__magic_name__      ,							__magic_name__			:       List[str]	      =	cls.get_config_dict(_A    ,	**_A     )

			# get the qformer config dict if we are loading from InstructBlipConfig
			if config_dict.get('model_type'     ) == "instructblip":
				__magic_name__			:       Tuple	      =	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(_A    ,	**_A     )







class _lowerCamelCase			(		__UpperCamelCase				):



		'''simple docstring'''







		A_      :     int        =							'instructblip'
		A_      :     Optional[Any]        =							True







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

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

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

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

			__magic_name__			:       Union[str, Any]	      =	InstructBlipVisionConfig(**_A     )
			__magic_name__			:       Optional[int]	      =	InstructBlipQFormerConfig(**_A     )
			__magic_name__			:       List[str]	      =	text_config['model_type'] if 'model_type' in text_config else 'opt'
			__magic_name__			:       int	      =	CONFIG_MAPPING[text_model_type](**_A     )

			__magic_name__			:       int	      =	self.text_config.tie_word_embeddings
			__magic_name__			:       List[Any]	      =	self.text_config.is_encoder_decoder

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







		@classmethod
		def   __lowerCAmelCase						(   cls						:		Any    ,	_A						:		Any    ,	_A						:		Tuple    ,	_A						:		Optional[int]    ,	**_A						:		Tuple    ,	)    ->					Dict:
			return cls(
			    vision_config=vision_config.to_dict()    ,	qformer_config=qformer_config.to_dict()    ,	text_config=text_config.to_dict()    ,	**_A    ,	)







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					Union[str, Any]:
			__magic_name__			:       Optional[Any]	      =	copy.deepcopy(self.__dict__     )
			__magic_name__			:       List[Any]	      =	self.vision_config.to_dict()
			__magic_name__			:       Dict	      =	self.qformer_config.to_dict()
			__magic_name__			:       Union[str, Any]	      =	self.text_config.to_dict()
			__magic_name__			:       Dict	      =	self.__class__.model_type
			return output

def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float(moles / volume				) * nfactor				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (volume)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (pressure)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((pressure * volume) / (0.0_821 * moles)				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



