





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


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''instructblip_vision_model'''






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







					"""simple docstring"""






					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        a_	(      cls       :   Dict							,     __lowerCAmelCase       :   Optional[Any]							,     **__lowerCAmelCase       :   Tuple       )     ->     "PretrainedConfig":







					"""simple docstring"""






					cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE       )

					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      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[Any]          =   '''instructblip_qformer'''






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







					"""simple docstring"""






					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        a_	(      cls       :   Optional[Any]							,     __lowerCAmelCase       :   List[str]							,     **__lowerCAmelCase       :   Any       )     ->     "PretrainedConfig":







					"""simple docstring"""






					cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE       )

					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      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   '''instructblip'''
			__lowerCamelCase     :	Any          =   True






			def __init__(      self       :   str							,     __lowerCAmelCase       :   str=None							,     __lowerCAmelCase       :   Optional[Any]=None							,     __lowerCAmelCase       :   Tuple=None							,     __lowerCAmelCase       :   Optional[Any]=32							,     **__lowerCAmelCase       :   Any       )     ->     int:







					"""simple docstring"""






					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__  				=       0.0_2






			@classmethod
			def        a_	(      cls       :   Dict							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   int							,     **__lowerCAmelCase       :   Any							,     )     ->     str:







					"""simple docstring"""






					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       :   Any       )     ->     Dict:







					"""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






import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


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



