'''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			:      Tuple					      =				logging.get_logger(__name__)

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







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					"""instructblip_vision_model"""
 def __init__(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=14_08      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=61_44      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=39      ,       SCREAMING_SNAKE_CASE__   :	List[str]=16      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=2_24      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=14      ,       SCREAMING_SNAKE_CASE__   :	List[str]="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Dict=1e-6      ,       SCREAMING_SNAKE_CASE__   :	Any=0.0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=1e-1_0      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       **SCREAMING_SNAKE_CASE__   :	Tuple      ,       )  ->    str:
  super().__init__(**SCREAMING_SNAKE_CASE__							)

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





 @classmethod
 def a					(      cls   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, os.PathLike]      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    "PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						cls.get_config_dict(SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

  # get the vision config dict if we are loading from InstructBlipConfig
  if config_dict.get("""model_type"""							) == "instructblip":
   __lowerCAmelCase											=						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(SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					"""instructblip_qformer"""
 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=3_05_22      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=7_68      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=12      ,       SCREAMING_SNAKE_CASE__   :	str=12      ,       SCREAMING_SNAKE_CASE__   :	str=30_72      ,       SCREAMING_SNAKE_CASE__   :	Dict="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=5_12      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	int=1e-1_2      ,       SCREAMING_SNAKE_CASE__   :	Any=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple="absolute"      ,       SCREAMING_SNAKE_CASE__   :	str=2      ,       SCREAMING_SNAKE_CASE__   :	Dict=14_08      ,       **SCREAMING_SNAKE_CASE__   :	int      ,       )  ->    int:
  super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

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





 @classmethod
 def a					(      cls   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Union[str, os.PathLike]      ,       **SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    "PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						cls.get_config_dict(SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

  # get the qformer config dict if we are loading from InstructBlipConfig
  if config_dict.get("""model_type"""							) == "instructblip":
   __lowerCAmelCase											=						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(SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					"""instructblip"""
 _SCREAMING_SNAKE_CASE     :       Dict      	=					True
 def __init__(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Dict=None      ,       SCREAMING_SNAKE_CASE__   :	Dict=None      ,       SCREAMING_SNAKE_CASE__   :	int=None      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=32      ,       **SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  super().__init__(**SCREAMING_SNAKE_CASE__							)

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

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

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

  __lowerCAmelCase											=						InstructBlipVisionConfig(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						InstructBlipQFormerConfig(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						text_config["""model_type"""] if """model_type""" in text_config else """opt"""
  __lowerCAmelCase											=						CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.text_config.tie_word_embeddings
  __lowerCAmelCase											=						self.text_config.is_encoder_decoder

  __lowerCAmelCase											=						num_query_tokens
  __lowerCAmelCase											=						self.vision_config.hidden_size
  __lowerCAmelCase											=						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
  __lowerCAmelCase											=						1.0
  __lowerCAmelCase											=						0.0_2
 @classmethod
 def a					(      cls   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	InstructBlipVisionConfig      ,       SCREAMING_SNAKE_CASE__   :	InstructBlipQFormerConfig      ,       SCREAMING_SNAKE_CASE__   :	PretrainedConfig      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]      ,       )  ->    int:
  return cls(
      vision_config=vision_config.to_dict()      ,       qformer_config=qformer_config.to_dict()      ,       text_config=text_config.to_dict()      ,       **SCREAMING_SNAKE_CASE__      ,       )





 def a					(      self   :	Tuple							)  ->    Dict:
  __lowerCAmelCase											=						copy.deepcopy(self.__dict__							)
  __lowerCAmelCase											=						self.vision_config.to_dict()
  __lowerCAmelCase											=						self.qformer_config.to_dict()
  __lowerCAmelCase											=						self.text_config.to_dict()
  __lowerCAmelCase											=						self.__class__.model_type
  return output







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       list[int]						,		snake_case_       :       list[int]      )					->     tuple[float, float]:






 '''simple docstring'''

 if not len(snake_case_      ) == len(snake_case_      ) == 3:
  raise ValueError("""Please enter a valid equation."""      )
 if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
  raise ValueError("""Both a & b of two equations can't be zero."""      )

 # Extract the coefficients
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa

 # Calculate the determinants of the matrices
 __lowerCAmelCase											=						aa * ba - aa * ba
 __lowerCAmelCase											=						ca * ba - ca * ba
 __lowerCAmelCase											=						aa * ca - aa * ca

 # Check if the system of linear equations has a solution (using Cramer's rule)
 if determinant == 0:
  if determinant_x == determinant_y == 0:
   raise ValueError("""Infinite solutions. (Consistent system)"""      )
  else:
   raise ValueError("""No solution. (Inconsistent system)"""      )
 else:
  if determinant_x == determinant_y == 0:
   # Trivial solution (Inconsistent system)
   return (0.0, 0.0)
  else:
   __lowerCAmelCase											=						determinant_x / determinant
   __lowerCAmelCase											=						determinant_y / determinant
   # Non-Trivial Solution (Consistent system)
   return (x, y)






