

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						=logging.get_logger(__name__)

a						={
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}






class 			A_						(			__snake_case							):
							_UpperCAmelCase       :    Optional[Any]		 = 'instructblip_vision_model'
							def __init__(						self						:     str  ,SCREAMING_SNAKE_CASE__						:     Tuple=1_4_0_8  ,SCREAMING_SNAKE_CASE__						:     str=6_1_4_4  ,SCREAMING_SNAKE_CASE__						:     List[Any]=3_9  ,SCREAMING_SNAKE_CASE__						:     str=1_6  ,SCREAMING_SNAKE_CASE__						:     str=2_2_4  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=1_4  ,SCREAMING_SNAKE_CASE__						:     Optional[Any]="gelu"  ,SCREAMING_SNAKE_CASE__						:     List[Any]=1E-6  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=0.0  ,SCREAMING_SNAKE_CASE__						:     str=1E-10  ,SCREAMING_SNAKE_CASE__						:     Tuple=True  ,**SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,):
								super().__init__(**A_)

								__lowerCamelCase							:							Tuple   			=					hidden_size
								__lowerCamelCase							:							int   			=					intermediate_size
								__lowerCamelCase							:							str   			=					num_hidden_layers
								__lowerCamelCase							:							Any   			=					num_attention_heads
								__lowerCamelCase							:							Union[str, Any]   			=					patch_size
								__lowerCamelCase							:							int   			=					image_size
								__lowerCamelCase							:							List[str]   			=					initializer_range
								__lowerCamelCase							:							Dict   			=					attention_dropout
								__lowerCamelCase							:							str   			=					layer_norm_eps
								__lowerCamelCase							:							Any   			=					hidden_act
								__lowerCamelCase							:							Any   			=					qkv_bias


							@classmethod
							def        lowerCAmelCase	(						cls						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Tuple  ,**SCREAMING_SNAKE_CASE__						:     List[Any]):
								cls._set_token_in_kwargs(A_)

								__lowerCamelCase							:							Any   			=					cls.get_config_dict(A_  ,**A_)

								# get the vision config dict if we are loading from InstructBlipConfig
								if config_dict.get('model_type') == "instructblip":
									__lowerCamelCase							:							Tuple   			=					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(A_  ,**A_)






class 			A_						(			__snake_case							):
							_UpperCAmelCase       :    Dict		 = 'instructblip_qformer'
							def __init__(						self						:     int  ,SCREAMING_SNAKE_CASE__						:     Dict=3_0_5_2_2  ,SCREAMING_SNAKE_CASE__						:     List[str]=7_6_8  ,SCREAMING_SNAKE_CASE__						:     str=1_2  ,SCREAMING_SNAKE_CASE__						:     Dict=1_2  ,SCREAMING_SNAKE_CASE__						:     Any=3_0_7_2  ,SCREAMING_SNAKE_CASE__						:     Optional[int]="gelu"  ,SCREAMING_SNAKE_CASE__						:     int=0.1  ,SCREAMING_SNAKE_CASE__						:     int=0.1  ,SCREAMING_SNAKE_CASE__						:     List[Any]=5_1_2  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=0.02  ,SCREAMING_SNAKE_CASE__						:     str=1E-12  ,SCREAMING_SNAKE_CASE__						:     List[str]=0  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]="absolute"  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]=2  ,SCREAMING_SNAKE_CASE__						:     Dict=1_4_0_8  ,**SCREAMING_SNAKE_CASE__						:     str  ,):
								super().__init__(pad_token_id=A_  ,**A_)

								__lowerCamelCase							:							List[Any]   			=					vocab_size
								__lowerCamelCase							:							Tuple   			=					hidden_size
								__lowerCamelCase							:							str   			=					num_hidden_layers
								__lowerCamelCase							:							List[str]   			=					num_attention_heads
								__lowerCamelCase							:							Any   			=					hidden_act
								__lowerCamelCase							:							Tuple   			=					intermediate_size
								__lowerCamelCase							:							Any   			=					hidden_dropout_prob
								__lowerCamelCase							:							Dict   			=					attention_probs_dropout_prob
								__lowerCamelCase							:							Dict   			=					max_position_embeddings
								__lowerCamelCase							:							int   			=					initializer_range
								__lowerCamelCase							:							Optional[Any]   			=					layer_norm_eps
								__lowerCamelCase							:							List[Any]   			=					position_embedding_type
								__lowerCamelCase							:							Any   			=					cross_attention_frequency
								__lowerCamelCase							:							str   			=					encoder_hidden_size


							@classmethod
							def        lowerCAmelCase	(						cls						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     int  ,**SCREAMING_SNAKE_CASE__						:     Any):
								cls._set_token_in_kwargs(A_)

								__lowerCamelCase							:							Optional[Any]   			=					cls.get_config_dict(A_  ,**A_)

								# get the qformer config dict if we are loading from InstructBlipConfig
								if config_dict.get('model_type') == "instructblip":
									__lowerCamelCase							:							Any   			=					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(A_  ,**A_)






class 			A_						(			__snake_case							):
							_UpperCAmelCase       :    Union[str, Any]		 = 'instructblip'
							_UpperCAmelCase       :    Union[str, Any]		 = True
							def __init__(						self						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=None  ,SCREAMING_SNAKE_CASE__						:     Dict=None  ,SCREAMING_SNAKE_CASE__						:     List[str]=None  ,SCREAMING_SNAKE_CASE__						:     Dict=3_2  ,**SCREAMING_SNAKE_CASE__						:     Optional[Any]):
								super().__init__(**A_)

								if vision_config is None:
									__lowerCamelCase							:							str   			=					{}
									logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.')

								if qformer_config is None:
									__lowerCamelCase							:							int   			=					{}
									logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.')

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

								__lowerCamelCase							:							Union[str, Any]   			=					InstructBlipVisionConfig(**A_)
								__lowerCamelCase							:							Union[str, Any]   			=					InstructBlipQFormerConfig(**A_)
								__lowerCamelCase							:							Any   			=					text_config["model_type"] if "model_type" in text_config else "opt"
								__lowerCamelCase							:							Dict   			=					CONFIG_MAPPING[text_model_type](**A_)

								__lowerCamelCase							:							Union[str, Any]   			=					self.text_config.tie_word_embeddings
								__lowerCamelCase							:							Tuple   			=					self.text_config.is_encoder_decoder

								__lowerCamelCase							:							List[str]   			=					num_query_tokens
								__lowerCamelCase							:							List[str]   			=					self.vision_config.hidden_size
								__lowerCamelCase							:							Dict   			=					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
								__lowerCamelCase							:							str   			=					1.0
								__lowerCamelCase							:							Dict   			=					0.02
							@classmethod
							def        lowerCAmelCase	(						cls						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     int  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     List[str]  ,**SCREAMING_SNAKE_CASE__						:     List[Any]  ,):
								return cls(
								    vision_config=vision_config.to_dict()  ,qformer_config=qformer_config.to_dict()  ,text_config=text_config.to_dict()  ,**A_  ,)


							def        lowerCAmelCase	(						self						:     str):
								__lowerCamelCase							:							int   			=					copy.deepcopy(self.__dict__)
								__lowerCamelCase							:							int   			=					self.vision_config.to_dict()
								__lowerCamelCase							:							Any   			=					self.qformer_config.to_dict()
								__lowerCamelCase							:							Dict   			=					self.text_config.to_dict()
								__lowerCamelCase							:							str   			=					self.__class__.model_type
								return output











__lowerCamelCase			:	Any					    =	9.8_0_6_6_5



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = g	)		->					float:

 if fluid_density <= 0:
  raise ValueError("Impossible fluid density"	)
 if volume < 0:
  raise ValueError("Impossible Object volume"	)
 if gravity <= 0:
  raise ValueError("Impossible Gravity"	)

 return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()
