



'''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


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     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			'instructblip_vision_model'



 def __init__(self					,  A=1_408					,  A=6_144					,  A=39					,  A=16					,  A=224					,  A=14					,  A="gelu"					,  A=1E-6					,  A=0.0					,  A=1E-10					,  A=True					,  **A					,  )       ->	Union[str, Any]:


      """simple docstring"""



      super().__init__(**A			)

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



 @classmethod
 def    a__    (cls					,  A					,  **A			)       ->	"PretrainedConfig":


      """simple docstring"""



      cls._set_token_in_kwargs(A			)

      _a    ,       _a           =    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           =    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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			'instructblip_qformer'



 def __init__(self					,  A=30_522					,  A=768					,  A=12					,  A=12					,  A=3_072					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=0.02					,  A=1E-12					,  A=0					,  A="absolute"					,  A=2					,  A=1_408					,  **A					,  )       ->	Any:


      """simple docstring"""



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

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



 @classmethod
 def    a__    (cls					,  A					,  **A			)       ->	"PretrainedConfig":


      """simple docstring"""



      cls._set_token_in_kwargs(A			)

      _a    ,       _a           =    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           =    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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			'instructblip'
 __lowerCamelCase       :			str		=			True



 def __init__(self					,  A=None					,  A=None					,  A=None					,  A=32					,  **A			)       ->	Dict:


      """simple docstring"""



      super().__init__(**A			)

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

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

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

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

      _a           =    self.text_config.tie_word_embeddings
      _a           =    self.text_config.is_encoder_decoder

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



 @classmethod
 def    a__    (cls					,  A					,  A					,  A					,  **A					,  )       ->	List[str]:


      """simple docstring"""



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



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    copy.deepcopy(self.__dict__			)
      _a           =    self.vision_config.to_dict()
      _a           =    self.qformer_config.to_dict()
      _a           =    self.text_config.to_dict()
      _a           =    self.__class__.model_type
      return output








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



