





def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE__						) - ngram_size + 1						)]


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

     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


_A		       =						logging.get_logger(__name__)

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






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    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_					,   )  ->       Tuple:
         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 _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  "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_=0.02					,   A_=1E-12					,   A_=0					,   A_="absolute"					,   A_=2					,   A_=1408					,   **A_					,   )  ->       Optional[Any]:
         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 _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip"
    UpperCAmelCase__						:		Optional[Any]      =  True






    def __init__(			self					,   A_=None					,   A_=None					,   A_=None					,   A_=32					,   **A_    )  ->       List[str]:
         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 _a					(			cls					,   A_					,   A_					,   A_					,   **A_					,   )  ->       Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()					,   qformer_config=qformer_config.to_dict()					,   text_config=text_config.to_dict()					,   **A_					,   )






    def _a					(			self    )  ->       Optional[Any]:
         __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





