

"""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       =							logging.get_logger(__name__)

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




class 					_lowerCAmelCase  (	__lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					List[str]   		=	"instructblip_vision_model"


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

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



 @classmethod
 def 					_lowercase (			cls							:			Tuple,					UpperCAmelCase__							:			Union[str, os.PathLike],					**UpperCAmelCase__							:			Optional[int]	):
    cls._set_token_in_kwargs(snake_case_	)

    __lowercase								=						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":
       __lowercase								=						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 					_lowerCAmelCase  (	__lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	"instructblip_qformer"


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

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



 @classmethod
 def 					_lowercase (			cls							:			List[str],					UpperCAmelCase__							:			Union[str, os.PathLike],					**UpperCAmelCase__							:			Optional[Any]	):
    cls._set_token_in_kwargs(snake_case_	)

    __lowercase								=						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":
       __lowercase								=						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 					_lowerCAmelCase  (	__lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	"instructblip"
 __UpperCAmelCase	:					List[Any]   		=	True


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int]=None,					UpperCAmelCase__							:			Any=None,					UpperCAmelCase__							:			Dict=None,					UpperCAmelCase__							:			List[Any]=3_2,					**UpperCAmelCase__							:			Optional[Any]	):
    super().__init__(**snake_case_	)

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

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

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

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

    __lowercase								=						self.text_config.tie_word_embeddings
    __lowercase								=						self.text_config.is_encoder_decoder

    __lowercase								=						num_query_tokens
    __lowercase								=						self.vision_config.hidden_size
    __lowercase								=						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
    __lowercase								=						1.0
    __lowercase								=						0.02


 @classmethod
 def 					_lowercase (			cls							:			Optional[int],					UpperCAmelCase__							:			InstructBlipVisionConfig,					UpperCAmelCase__							:			InstructBlipQFormerConfig,					UpperCAmelCase__							:			PretrainedConfig,					**UpperCAmelCase__							:			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 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						copy.deepcopy(self.__dict__	)
    __lowercase								=						self.vision_config.to_dict()
    __lowercase								=						self.qformer_config.to_dict()
    __lowercase								=						self.text_config.to_dict()
    __lowercase								=						self.__class__.model_type
    return output






from __future__ import annotations




def    __UpperCamelCase     (				lowerCAmelCase__							:				list[float]  ,   lowerCAmelCase__							:				list[float]  ):
 __a :  Dict						=  sorted(numsa + numsa  )
 __a				,       __a :  Optional[Any]						=  divmod(len(lowerCAmelCase__  )  ,   2  )
 if mod == 1:
  return all_numbers[div]
 else:
  return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__       =[float(x) for x in input('Enter the elements of first array: ').split()]
      lowercase__       =[float(x) for x in input('Enter the elements of second array: ').split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
