

from __future__ import annotations





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







       '''simple docstring'''






       if days_between_payments <= 0:
              raise ValueError('days_between_payments must be > 0'   )
       if daily_interest_rate < 0:
              raise ValueError('daily_interest_rate must be >= 0'   )
       if principal <= 0:
              raise ValueError('principal must be > 0'   )
       return principal * daily_interest_rate * days_between_payments





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							):







       '''simple docstring'''






       if number_of_compounding_periods <= 0:
              raise ValueError('number_of_compounding_periods must be > 0'   )
       if nominal_annual_interest_rate_percentage < 0:
              raise ValueError('nominal_annual_interest_rate_percentage must be >= 0'   )
       if principal <= 0:
              raise ValueError('principal must be > 0'   )

       return principal * (
           (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
           - 1
       )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							):







       '''simple docstring'''






       if number_of_years <= 0:
              raise ValueError('number_of_years must be > 0'   )
       if nominal_annual_percentage_rate < 0:
              raise ValueError('nominal_annual_percentage_rate must be >= 0'   )
       if principal <= 0:
              raise ValueError('principal must be > 0'   )

       return compound_interest(
           UpperCamelCase__     ,							nominal_annual_percentage_rate / 365     ,							number_of_years * 365   )


if __name__ == "__main__":
     import doctest

     doctest.testmod()







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


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

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







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip_2_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=0.0_00_01   ,     snake_case=0.0   ,     snake_case=1e-1_0   ,     snake_case=True   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

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




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the vision config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''blip_2_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-1_2   ,     snake_case=0   ,     snake_case="absolute"   ,     snake_case=2   ,     snake_case=1408   ,     **snake_case   ,     ):
											super().__init__(pad_token_id=snake_case   ,     **snake_case )

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




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the qformer config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip-2'''
				__SCREAMING_SNAKE_CASE      :				Dict     =    True




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=32   ,     **snake_case ):
											super().__init__(**snake_case )

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

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

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

											snake_case_           =					BlipaVisionConfig(**snake_case )
											snake_case_           =					BlipaQFormerConfig(**snake_case )
											snake_case_           =					text_config['model_type'] if 'model_type' in text_config else 'opt'
											snake_case_           =					CONFIG_MAPPING[text_model_type](**snake_case )

											snake_case_           =					self.text_config.tie_word_embeddings
											snake_case_           =					self.text_config.is_encoder_decoder

											snake_case_           =					num_query_tokens
											snake_case_           =					self.vision_config.hidden_size
											snake_case_           =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
											snake_case_           =					1.0
											snake_case_           =					0.02




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case   ,     snake_case   ,     **snake_case   ,     ):
											return cls(
											    vision_config=vision_config.to_dict()   ,     qformer_config=qformer_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **snake_case   ,     )




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.vision_config.to_dict()
											snake_case_           =					self.qformer_config.to_dict()
											snake_case_           =					self.text_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output




