






"""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


_UpperCamelCase       :					List[str]									=		logging.get_logger(__name__)

_UpperCamelCase       :					List[str]									=		{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''instructblip_vision_model'''





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


      '''simple docstring'''




      super().__init__(**A    )

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





    @classmethod
    def        lowerCamelCase__   (							cls			:      Any       ,	A			:      Union[str, os.PathLike]       ,	**A			:      List[str]    ):


      '''simple docstring'''




      cls._set_token_in_kwargs(A    )

      a,  a  :    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":
        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(A       ,	**A    )






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''instructblip_qformer'''





    def __init__(							self			:      Optional[int]       ,	A			:      List[str]=3_0_5_2_2       ,	A			:      int=7_6_8       ,	A			:      Optional[int]=1_2       ,	A			:      Optional[int]=1_2       ,	A			:      str=3_0_7_2       ,	A			:      List[Any]="gelu"       ,	A			:      Tuple=0.1       ,	A			:      Optional[int]=0.1       ,	A			:      Dict=5_1_2       ,	A			:      int=0.02       ,	A			:      Optional[int]=1E-12       ,	A			:      str=0       ,	A			:      int="absolute"       ,	A			:      int=2       ,	A			:      int=1_4_0_8       ,	**A			:      Optional[int]       ,	):


      '''simple docstring'''




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

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





    @classmethod
    def        lowerCamelCase__   (							cls			:      int       ,	A			:      Union[str, os.PathLike]       ,	**A			:      List[str]    ):


      '''simple docstring'''




      cls._set_token_in_kwargs(A    )

      a,  a  :    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":
        a  :    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(A       ,	**A    )






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''instructblip'''
    __magic_name__					  =  True





    def __init__(							self			:      Dict       ,	A			:      Dict=None       ,	A			:      Tuple=None       ,	A			:      Union[str, Any]=None       ,	A			:      Union[str, Any]=3_2       ,	**A			:      str    ):


      '''simple docstring'''




      super().__init__(**A    )

      if vision_config is None:
        a  :    Optional[int]       =     {}
        logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'    )

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

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

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

      a  :    Union[str, Any]       =     self.text_config.tie_word_embeddings
      a  :    int       =     self.text_config.is_encoder_decoder

      a  :    Tuple       =     num_query_tokens
      a  :    int       =     self.vision_config.hidden_size
      a  :    str       =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      a  :    Union[str, Any]       =     1.0
      a  :    List[str]       =     0.02





    @classmethod
    def        lowerCamelCase__   (							cls			:      Optional[int]       ,	A			:      InstructBlipVisionConfig       ,	A			:      InstructBlipQFormerConfig       ,	A			:      PretrainedConfig       ,	**A			:      Optional[Any]       ,	):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      a  :    List[str]       =     copy.deepcopy(self.__dict__    )
      a  :    Union[str, Any]       =     self.vision_config.to_dict()
      a  :    Optional[int]       =     self.qformer_config.to_dict()
      a  :    Tuple       =     self.text_config.to_dict()
      a  :    Optional[int]       =     self.__class__.model_type
      return output












"""simple docstring"""


import math

from numpy import inf
from scipy.integrate import quad




def     snake_case						(A_   :float   ):







  '''simple docstring'''

  if num <= 0:
    raise ValueError('math domain error'   )

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




def     snake_case						(A_   :float			,    A_   :float   ):







  '''simple docstring'''

  return math.pow(A_			,    z - 1   ) * math.exp(-x   )


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

   testmod()




