

"""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


SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	str							  =						{
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}



class 					a						(       lowercase_				):



    """simple docstring"""



    UpperCAmelCase						 =					"blip_2_vision_model"

    def __init__(     self: Dict    ,						UpperCamelCase: Optional[Any]=14_08    ,						UpperCamelCase: Optional[int]=61_44    ,						UpperCamelCase: str=39    ,						UpperCamelCase: Union[str, Any]=16    ,						UpperCamelCase: str=2_24    ,						UpperCamelCase: Dict=14    ,						UpperCamelCase: Union[str, Any]="gelu"    ,						UpperCamelCase: List[str]=0.00_001    ,						UpperCamelCase: Any=0.0    ,						UpperCamelCase: List[str]=1e-1_0    ,						UpperCamelCase: Dict=True    ,						**UpperCamelCase: Optional[int]    ,						):






         """simple docstring"""


         super().__init__(**lowerCamelCase_       )

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





    @classmethod
    def 				UpperCamelCase	(     cls: List[Any]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: str       ):






         """simple docstring"""


         cls._set_token_in_kwargs(lowerCamelCase_       )

         A__										=  cls.get_config_dict(lowerCamelCase_    ,						**lowerCamelCase_       )

         # get the vision config dict if we are loading from Blip2Config
         if config_dict.get("""model_type"""       ) == "blip-2":
              A__										=  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						(       lowercase_				):



    """simple docstring"""



    UpperCAmelCase						 =					"blip_2_qformer"

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






         """simple docstring"""


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

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





    @classmethod
    def 				UpperCamelCase	(     cls: Union[str, Any]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: Any       ):






         """simple docstring"""


         cls._set_token_in_kwargs(lowerCamelCase_       )

         A__										=  cls.get_config_dict(lowerCamelCase_    ,						**lowerCamelCase_       )

         # get the qformer config dict if we are loading from Blip2Config
         if config_dict.get("""model_type"""       ) == "blip-2":
              A__										=  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						(       lowercase_				):



    """simple docstring"""



    UpperCAmelCase						 =					"blip-2"
    UpperCAmelCase						 =					True

    def __init__(     self: List[Any]    ,						UpperCamelCase: List[str]=None    ,						UpperCamelCase: Any=None    ,						UpperCamelCase: Dict=None    ,						UpperCamelCase: str=32    ,						**UpperCamelCase: Any       ):






         """simple docstring"""


         super().__init__(**lowerCamelCase_       )

         if vision_config is None:
              A__										=  {}
              logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""       )

         if qformer_config is None:
              A__										=  {}
              logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""       )

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

         A__										=  BlipaVisionConfig(**lowerCamelCase_       )
         A__										=  BlipaQFormerConfig(**lowerCamelCase_       )
         A__										=  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
         A__										=  CONFIG_MAPPING[text_model_type](**lowerCamelCase_       )

         A__										=  self.text_config.tie_word_embeddings
         A__										=  self.text_config.is_encoder_decoder

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

    @classmethod
    def 				UpperCamelCase	(     cls: int    ,						UpperCamelCase: BlipaVisionConfig    ,						UpperCamelCase: BlipaQFormerConfig    ,						UpperCamelCase: PretrainedConfig    ,						**UpperCamelCase: Tuple    ,						):






         """simple docstring"""


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





    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


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









"""simple docstring"""



from __future__ import annotations



class 					a						:



    """simple docstring"""



    def __init__(     self: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__			,				A__										=  text, pattern
         A__			,				A__										=  len(UpperCamelCase       ), len(UpperCamelCase       )

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if char == self.pattern[i]:
                   return i
         return -1

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: int       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if self.pattern[i] != self.text[current_pos + i]:
                   return current_pos + i
         return -1





    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  []
         for i in range(self.textLen - self.patLen + 1       ):
              A__										=  self.mismatch_in_text(UpperCamelCase       )
              if mismatch_index == -1:
                   positions.append(UpperCamelCase       )
              else:
                   A__										=  self.match_in_pattern(self.text[mismatch_index]       )
                   A__										=  (
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions

SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'ABAABA'
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'AB'
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE_					:	int							  =						bms.bad_character_heuristic()

if len(positions) == 0:
 print('No match found')
else:
 print('Pattern found in following positions: ')
 print(positions)






