



"""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


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''instructblip_vision_model'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  Dict=1408		,    _UpperCAmelCase      :  List[Any]=6144		,    _UpperCAmelCase      :  List[Any]=39		,    _UpperCAmelCase      :  str=16		,    _UpperCAmelCase      :  List[str]=224		,    _UpperCAmelCase      :  List[str]=14		,    _UpperCAmelCase      :  Tuple="gelu"		,    _UpperCAmelCase      :  Dict=1e-6		,    _UpperCAmelCase      :  Any=0.0		,    _UpperCAmelCase      :  Dict=1e-10		,    _UpperCAmelCase      :  Optional[Any]=True		,    **_UpperCAmelCase      :  int		,    )			->						Tuple:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase   )

             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 					lowercase__    (							cls      :  Dict		,    _UpperCAmelCase      :  Union[str, os.PathLike]		,    **_UpperCAmelCase      :  Union[str, Any]   )			->						"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase   )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							cls.get_config_dict(_UpperCAmelCase		,    **_UpperCAmelCase   )

             # 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(_UpperCAmelCase		,    **_UpperCAmelCase   )



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''instructblip_qformer'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  str=30522		,    _UpperCAmelCase      :  Any=768		,    _UpperCAmelCase      :  List[Any]=12		,    _UpperCAmelCase      :  Optional[int]=12		,    _UpperCAmelCase      :  Optional[int]=3072		,    _UpperCAmelCase      :  List[Any]="gelu"		,    _UpperCAmelCase      :  List[str]=0.1		,    _UpperCAmelCase      :  Dict=0.1		,    _UpperCAmelCase      :  str=512		,    _UpperCAmelCase      :  Optional[Any]=0.02		,    _UpperCAmelCase      :  Dict=1e-12		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  Union[str, Any]="absolute"		,    _UpperCAmelCase      :  List[str]=2		,    _UpperCAmelCase      :  int=1408		,    **_UpperCAmelCase      :  List[str]		,    )			->						List[str]:


             '''simple docstring'''




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

             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 					lowercase__    (							cls      :  Union[str, Any]		,    _UpperCAmelCase      :  Union[str, os.PathLike]		,    **_UpperCAmelCase      :  Optional[Any]   )			->						"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase   )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							cls.get_config_dict(_UpperCAmelCase		,    **_UpperCAmelCase   )

             # 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(_UpperCAmelCase		,    **_UpperCAmelCase   )



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''instructblip'''
       UpperCamelCase						=					True



       def __init__(							self      :  Tuple		,    _UpperCAmelCase      :  Union[str, Any]=None		,    _UpperCAmelCase      :  Tuple=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Dict=32		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase   )

             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(**_UpperCAmelCase   )
             UpperCAmelCase_      			=							InstructBlipQFormerConfig(**_UpperCAmelCase   )
             UpperCAmelCase_      			=							text_config["model_type"] if "model_type" in text_config else "opt"
             UpperCAmelCase_      			=							CONFIG_MAPPING[text_model_type](**_UpperCAmelCase   )

             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 					lowercase__    (							cls      :  Union[str, Any]		,    _UpperCAmelCase      :  InstructBlipVisionConfig		,    _UpperCAmelCase      :  InstructBlipQFormerConfig		,    _UpperCAmelCase      :  PretrainedConfig		,    **_UpperCAmelCase      :  int		,    )			->						List[str]:


             '''simple docstring'''




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







       def 					lowercase__    (							self      :  Dict   )			->						Optional[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 Iterable
from typing import Generic, TypeVar

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


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

  testmod()




