


'''simple docstring'''
def 			a      (			__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= [1]

					UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= 0, 0, 0
					UpperCamelCase__   :List[Any]												= ugly_nums[ia] * 2
					UpperCamelCase__   :str												= ugly_nums[ia] * 3
					UpperCamelCase__   :List[Any]												= ugly_nums[ia] * 5

					for _ in range(1 ,					_snake_case   ):
										UpperCamelCase__   :Optional[int]												= min(_snake_case ,					_snake_case ,					_snake_case   )
										ugly_nums.append(_snake_case   )
										if next_num == next_a:
															ia += 1
															UpperCamelCase__   :str												= ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCamelCase__   :Union[str, Any]												= ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCamelCase__   :Dict												= ugly_nums[ia] * 5
					return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F"""{ugly_numbers(200) = }""")


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


_lowerCAmelCase					:			List[str]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_vision_model'





			def __init__(		self					,  __snake_case=1408					,  __snake_case=6144					,  __snake_case=39					,  __snake_case=16					,  __snake_case=224					,  __snake_case=14					,  __snake_case="gelu"					,  __snake_case=1e-6					,  __snake_case=0.0					,  __snake_case=1e-10					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(**__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 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_qformer'





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=2					,  __snake_case=1408					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  **__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 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=32					,  **__snake_case					)   -> str:




						'''simple docstring'''
						super().__init__(**__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(**__snake_case					)
						__a        =InstructBlipQFormerConfig(**__snake_case					)
						__a        =text_config['model_type'] if 'model_type' in text_config else 'opt'
						__a        =CONFIG_MAPPING[text_model_type](**__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 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **__snake_case					,  )





			def 		__magic_name__    (		self					)   -> List[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






