





"""simple docstring"""




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   :	Union[str, Any] 					=     logging.get_logger(__name__)

_a   :	Tuple 					=     {
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class 		__A							(				__UpperCAmelCase	):
     _UpperCamelCase							:    Optional[int]	      =	"instructblip_vision_model"






     def __init__(	self       ,  a__=1408       ,  a__=6144       ,  a__=39       ,  a__=16       ,  a__=224       ,  a__=14       ,  a__="gelu"       ,  a__=1e-6       ,  a__=0.0       ,  a__=1e-10       ,  a__=True       ,  **a__       ,  ):
       super().__init__(**lowerCAmelCase_						)

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






     @classmethod
     def 					__A						(	cls       ,  a__       ,  **a__						):
       cls._set_token_in_kwargs(lowerCAmelCase_						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  cls.get_config_dict(lowerCAmelCase_       ,  **lowerCAmelCase_						)

       # get the vision config dict if we are loading from InstructBlipConfig
       if config_dict.get("""model_type"""						) == "instructblip":
         _lowerCAmelCase		: Optional[Any]						    =  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(lowerCAmelCase_       ,  **lowerCAmelCase_						)







class 		__A							(				__UpperCAmelCase	):
     _UpperCamelCase							:    Optional[int]	      =	"instructblip_qformer"






     def __init__(	self       ,  a__=30522       ,  a__=768       ,  a__=12       ,  a__=12       ,  a__=3072       ,  a__="gelu"       ,  a__=0.1       ,  a__=0.1       ,  a__=512       ,  a__=0.0_2       ,  a__=1e-12       ,  a__=0       ,  a__="absolute"       ,  a__=2       ,  a__=1408       ,  **a__       ,  ):
       super().__init__(pad_token_id=lowerCAmelCase_       ,  **lowerCAmelCase_						)

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






     @classmethod
     def 					__A						(	cls       ,  a__       ,  **a__						):
       cls._set_token_in_kwargs(lowerCAmelCase_						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  cls.get_config_dict(lowerCAmelCase_       ,  **lowerCAmelCase_						)

       # get the qformer config dict if we are loading from InstructBlipConfig
       if config_dict.get("""model_type"""						) == "instructblip":
         _lowerCAmelCase		: Optional[int]						    =  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(lowerCAmelCase_       ,  **lowerCAmelCase_						)







class 		__A							(				__UpperCAmelCase	):
     _UpperCamelCase							:    List[Any]	      =	"instructblip"
     _UpperCamelCase							:    Optional[Any]	      =	True






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

       if vision_config is None:
         _lowerCAmelCase		: Optional[Any]						    =  {}
         logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""						)

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

       if text_config is None:
         _lowerCAmelCase		: List[str]						    =  {}
         logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""						)

       _lowerCAmelCase		: Union[str, Any]						    =  InstructBlipVisionConfig(**lowerCAmelCase_						)
       _lowerCAmelCase		: List[str]						    =  InstructBlipQFormerConfig(**lowerCAmelCase_						)
       _lowerCAmelCase		: Tuple						    =  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
       _lowerCAmelCase		: Optional[int]						    =  CONFIG_MAPPING[text_model_type](**lowerCAmelCase_						)

       _lowerCAmelCase		: str						    =  self.text_config.tie_word_embeddings
       _lowerCAmelCase		: Dict						    =  self.text_config.is_encoder_decoder

       _lowerCAmelCase		: Dict						    =  num_query_tokens
       _lowerCAmelCase		: Any						    =  self.vision_config.hidden_size
       _lowerCAmelCase		: Union[str, Any]						    =  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
       _lowerCAmelCase		: Optional[int]						    =  1.0
       _lowerCAmelCase		: Dict						    =  0.0_2






     @classmethod
     def 					__A						(	cls       ,  a__       ,  a__       ,  a__       ,  **a__       ,  ):
       return cls(
           vision_config=vision_config.to_dict()       ,  qformer_config=qformer_config.to_dict()       ,  text_config=text_config.to_dict()       ,  **lowerCAmelCase_       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  copy.deepcopy(self.__dict__						)
       _lowerCAmelCase		: List[Any]						    =  self.vision_config.to_dict()
       _lowerCAmelCase		: int						    =  self.qformer_config.to_dict()
       _lowerCAmelCase		: List[str]						    =  self.text_config.to_dict()
       _lowerCAmelCase		: Optional[int]						    =  self.__class__.model_type
       return output





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




