'''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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''blip_2_vision_model'''




  def __init__(		self      :       Optional[int]   ,				lowercase_      :       List[Any]=1_408   ,				lowercase_      :       Any=6_144   ,				lowercase_      :       int=39   ,				lowercase_      :       int=16   ,				lowercase_      :       int=224   ,				lowercase_      :       Any=14   ,				lowercase_      :       Dict="gelu"   ,				lowercase_      :       Optional[Any]=0.0_0_0_0_1   ,				lowercase_      :       Union[str, Any]=0.0   ,				lowercase_      :       Tuple=1e-10   ,				lowercase_      :       Optional[int]=True   ,				**lowercase_      :       str   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )

    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 		lowerCamelCase__  (		cls      :       List[str]   ,				lowercase_      :       Union[str, os.PathLike]   ,				**lowercase_      :       Tuple      ):







    '''simple docstring'''


    cls._set_token_in_kwargs(lowercase_      )

    lowercase_     ,	lowercase_     =       cls.get_config_dict(lowercase_   ,				**lowercase_      )

    # get the vision config dict if we are loading from Blip2Config
    if config_dict.get("""model_type"""      ) == "blip-2":
      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(lowercase_   ,				**lowercase_      )






class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''blip_2_qformer'''




  def __init__(		self      :       List[str]   ,				lowercase_      :       List[str]=30_522   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       int=12   ,				lowercase_      :       Optional[int]=12   ,				lowercase_      :       str=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       Union[str, Any]=0.1   ,				lowercase_      :       int=0.1   ,				lowercase_      :       Any=512   ,				lowercase_      :       Dict=0.0_2   ,				lowercase_      :       List[Any]=1e-12   ,				lowercase_      :       Any=0   ,				lowercase_      :       Dict="absolute"   ,				lowercase_      :       Tuple=2   ,				lowercase_      :       List[Any]=1_408   ,				**lowercase_      :       Any   ,				):







    '''simple docstring'''


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

    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 		lowerCamelCase__  (		cls      :       List[Any]   ,				lowercase_      :       Union[str, os.PathLike]   ,				**lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    cls._set_token_in_kwargs(lowercase_      )

    lowercase_     ,	lowercase_     =       cls.get_config_dict(lowercase_   ,				**lowercase_      )

    # get the qformer config dict if we are loading from Blip2Config
    if config_dict.get("""model_type"""      ) == "blip-2":
      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(lowercase_   ,				**lowercase_      )






class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''blip-2'''
  A_    							=						True




  def __init__(		self      :       List[str]   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Any=None   ,				lowercase_      :       Optional[Any]=32   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


    super().__init__(**lowercase_      )

    if vision_config is None:
      lowercase_     =       {}
      logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""      )

    if qformer_config is None:
      lowercase_     =       {}
      logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""      )

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

    lowercase_     =       BlipaVisionConfig(**lowercase_      )
    lowercase_     =       BlipaQFormerConfig(**lowercase_      )
    lowercase_     =       text_config["""model_type"""] if """model_type""" in text_config else """opt"""
    lowercase_     =       CONFIG_MAPPING[text_model_type](**lowercase_      )

    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_     =       0.0_2




  @classmethod
  def 		lowerCamelCase__  (		cls      :       Union[str, Any]   ,				lowercase_      :       BlipaVisionConfig   ,				lowercase_      :       BlipaQFormerConfig   ,				lowercase_      :       PretrainedConfig   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


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





  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    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


'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

