



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  :				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__ (				A__    ):
		lowercase   :							int        ='''instructblip_vision_model'''






		def __init__(    self   :  Union[str, Any]					,		UpperCamelCase__   :  Tuple=14_08					,		UpperCamelCase__   :  List[str]=61_44					,		UpperCamelCase__   :  List[Any]=39					,		UpperCamelCase__   :  Tuple=16					,		UpperCamelCase__   :  Dict=2_24					,		UpperCamelCase__   :  str=14					,		UpperCamelCase__   :  Optional[int]="gelu"					,		UpperCamelCase__   :  List[Any]=1e-6					,		UpperCamelCase__   :  Tuple=0.0					,		UpperCamelCase__   :  Tuple=1e-1_0					,		UpperCamelCase__   :  Any=True					,		**UpperCamelCase__   :  List[Any]					,		)					->       int:
								'''simple docstring'''







								super().__init__(**__a   )

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







		@classmethod
		def SCREAMING_SNAKE_CASE_      (    cls   :  List[Any]					,		UpperCamelCase__   :  Any					,		**UpperCamelCase__   :  Dict   )					->       "PretrainedConfig":
								'''simple docstring'''







								cls._set_token_in_kwargs(__a   )

								UpperCAmelCase							,		UpperCAmelCase								=  cls.get_config_dict(__a					,		**__a   )

								# get the vision config dict if we are loading from InstructBlipConfig
								if config_dict.get("model_type"   ) == "instructblip":
														UpperCAmelCase								=  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 			__magic_name__ (				A__    ):
		lowercase   :							List[str]        ='''instructblip_qformer'''






		def __init__(    self   :  str					,		UpperCamelCase__   :  List[str]=3_05_22					,		UpperCamelCase__   :  Dict=7_68					,		UpperCamelCase__   :  Optional[Any]=12					,		UpperCamelCase__   :  Dict=12					,		UpperCamelCase__   :  Tuple=30_72					,		UpperCamelCase__   :  Union[str, Any]="gelu"					,		UpperCamelCase__   :  List[Any]=0.1					,		UpperCamelCase__   :  str=0.1					,		UpperCamelCase__   :  Optional[int]=5_12					,		UpperCamelCase__   :  Dict=0.02					,		UpperCamelCase__   :  int=1e-1_2					,		UpperCamelCase__   :  str=0					,		UpperCamelCase__   :  Any="absolute"					,		UpperCamelCase__   :  Union[str, Any]=2					,		UpperCamelCase__   :  List[Any]=14_08					,		**UpperCamelCase__   :  Union[str, Any]					,		)					->       Dict:
								'''simple docstring'''







								super().__init__(pad_token_id=__a					,		**__a   )

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







		@classmethod
		def SCREAMING_SNAKE_CASE_      (    cls   :  Tuple					,		UpperCamelCase__   :  Dict					,		**UpperCamelCase__   :  Any   )					->       "PretrainedConfig":
								'''simple docstring'''







								cls._set_token_in_kwargs(__a   )

								UpperCAmelCase							,		UpperCAmelCase								=  cls.get_config_dict(__a					,		**__a   )

								# get the qformer config dict if we are loading from InstructBlipConfig
								if config_dict.get("model_type"   ) == "instructblip":
														UpperCAmelCase								=  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 			__magic_name__ (				A__    ):
		lowercase   :							Optional[int]        ='''instructblip'''
		lowercase   :							List[str]        =True






		def __init__(    self   :  Union[str, Any]					,		UpperCamelCase__   :  List[str]=None					,		UpperCamelCase__   :  Dict=None					,		UpperCamelCase__   :  Optional[int]=None					,		UpperCamelCase__   :  Union[str, Any]=32					,		**UpperCamelCase__   :  Optional[int]   )					->       List[str]:
								'''simple docstring'''







								super().__init__(**__a   )

								if vision_config is None:
														UpperCAmelCase								=  {}
														logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."   )

								if qformer_config is None:
														UpperCAmelCase								=  {}
														logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."   )

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

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

								UpperCAmelCase								=  self.text_config.tie_word_embeddings
								UpperCAmelCase								=  self.text_config.is_encoder_decoder

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






		@classmethod
		def SCREAMING_SNAKE_CASE_      (    cls   :  List[str]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  List[Any]					,		UpperCamelCase__   :  Union[str, Any]					,		**UpperCamelCase__   :  Dict					,		)					->       int:
								'''simple docstring'''







								return cls(
								    vision_config=vision_config.to_dict()					,		qformer_config=qformer_config.to_dict()					,		text_config=text_config.to_dict()					,		**__a					,		)







		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  copy.deepcopy(self.__dict__   )
								UpperCAmelCase								=  self.vision_config.to_dict()
								UpperCAmelCase								=  self.qformer_config.to_dict()
								UpperCAmelCase								=  self.text_config.to_dict()
								UpperCAmelCase								=  self.__class__.model_type
								return output





"""simple docstring"""





from collections.abc import Callable

import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.array:


					"""simple docstring"""





					_UpperCamelCase									=    int(np.ceil((x_end - xa) / step_size      )      )
					_UpperCamelCase									=    np.zeros((n + 1,)      )
					_UpperCamelCase									=    ya
					_UpperCamelCase									=    xa

					for k in range(__snake_case      ):
										_UpperCamelCase									=    y[k] + step_size * ode_func(__snake_case,	y[k]      )
										_UpperCamelCase									=    y[k] + (
										    (step_size / 2) * (ode_func(__snake_case,	y[k]      ) + ode_func(x + step_size,	__snake_case      ))
										)
										x += step_size

					return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()






