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


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """instructblip_vision_model"""

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=14_08  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=61_44  ,				__SCREAMING_SNAKE_CASE			:   List[str]=39  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=16  ,				__SCREAMING_SNAKE_CASE			:   List[str]=2_24  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=14  ,				__SCREAMING_SNAKE_CASE			:   Dict="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Any=1E-6  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.0  ,				__SCREAMING_SNAKE_CASE			:   Dict=1E-10  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				**__SCREAMING_SNAKE_CASE			:   Tuple  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __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 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Any							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	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":
    __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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """instructblip_qformer"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Tuple=3_05_22  ,				__SCREAMING_SNAKE_CASE			:   str=7_68  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Tuple=5_12  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=1E-12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]="absolute"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=2  ,				__SCREAMING_SNAKE_CASE			:   List[str]=14_08  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __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 		_UpperCAmelCase (		cls			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Dict							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	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":
    __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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """instructblip"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=32  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   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(**__SCREAMING_SNAKE_CASE							)
   __a							=	InstructBlipQFormerConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	text_config["model_type"] if "model_type" in text_config else "opt"
   __a							=	CONFIG_MAPPING[text_model_type](**__SCREAMING_SNAKE_CASE							)

   __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 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   InstructBlipVisionConfig  ,				__SCREAMING_SNAKE_CASE			:   InstructBlipQFormerConfig  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				**__SCREAMING_SNAKE_CASE			:   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 		_UpperCAmelCase (		self			:   Tuple							):
   __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



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


if __name__ == "__main__":
  print(f'''{solution() = }''')


