

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A      		=     logging.get_logger(__name__)

__A      		=     {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		List[Any]										=    "bridgetower_vision_model"



      def __init__(      self				:    List[Any]      , A__				:    Tuple=768      , A__				:    int=12      , A__				:    Any=3      , A__				:    List[str]=16      , A__				:    Union[str, Any]=288      , A__				:    Tuple=1      , A__				:    Tuple=1e-05      , A__				:    Optional[int]=False      , A__				:    Union[str, Any]=True      , A__				:    int=False      , **A__				:    Any      , )   ->					Any:
        super().__init__(**A__		)
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_channels
        _snake_case						=							patch_size
        _snake_case						=							image_size
        _snake_case						=							initializer_factor
        _snake_case						=							layer_norm_eps
        _snake_case						=							stop_gradient
        _snake_case						=							share_layernorm
        _snake_case						=							remove_last_layer







      @classmethod
      def 						UpperCamelCase_		(      cls				:    str      , A__				:    Union[str, os.PathLike]      , **A__				:    List[Any]		)   ->					"PretrainedConfig":
        _snake_case,					_snake_case						=							cls.get_config_dict(A__      , **A__		)

        if config_dict.get('''model_type'''		) == "bridgetower":
          _snake_case						=							config_dict['''text_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   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    "bridgetower_text_model"



      def __init__(      self				:    Any      , A__				:    Optional[int]=50265      , A__				:    Tuple=768      , A__				:    List[Any]=12      , A__				:    List[Any]=12      , A__				:    int=1      , A__				:    str=3072      , A__				:    Dict="gelu"      , A__				:    int=0.1      , A__				:    int=0.1      , A__				:    Dict=514      , A__				:    Optional[int]=1      , A__				:    int=1e-05      , A__				:    Tuple=1      , A__				:    Any=0      , A__				:    Dict=2      , A__				:    List[str]="absolute"      , A__				:    Union[str, Any]=True      , **A__				:    str      , )   ->					Dict:
        super().__init__(**A__		)

        _snake_case						=							vocab_size
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							hidden_act
        _snake_case						=							initializer_factor
        _snake_case						=							intermediate_size
        _snake_case						=							hidden_dropout_prob
        _snake_case						=							attention_probs_dropout_prob
        _snake_case						=							max_position_embeddings
        _snake_case						=							type_vocab_size
        _snake_case						=							layer_norm_eps
        _snake_case						=							position_embedding_type
        _snake_case						=							use_cache
        _snake_case						=							pad_token_id
        _snake_case						=							bos_token_id
        _snake_case						=							eos_token_id







      @classmethod
      def 						UpperCamelCase_		(      cls				:    int      , A__				:    Union[str, os.PathLike]      , **A__				:    int		)   ->					"PretrainedConfig":
        _snake_case,					_snake_case						=							cls.get_config_dict(A__      , **A__		)

        if config_dict.get('''model_type'''		) == "bridgetower":
          _snake_case						=							config_dict['''text_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   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Union[str, Any]										=    "bridgetower"



      def __init__(      self				:    Union[str, Any]      , A__				:    str=True      , A__				:    List[str]="gelu"      , A__				:    List[str]=768      , A__				:    int=1      , A__				:    Any=1e-05      , A__				:    str=False      , A__				:    Dict="add"      , A__				:    Dict=12      , A__				:    Tuple=6      , A__				:    Union[str, Any]=False      , A__				:    Any=False      , A__				:    List[Any]=None      , A__				:    Dict=None      , **A__				:    Optional[int]      , )   ->					str:
        # TODO: remove this once the Hub files are updated.
        _snake_case						=							kwargs.pop('''text_config_dict'''      , A__		)
        _snake_case						=							kwargs.pop('''vision_config_dict'''      , A__		)

        super().__init__(**A__		)
        _snake_case						=							share_cross_modal_transformer_layers
        _snake_case						=							hidden_act
        _snake_case						=							hidden_size
        _snake_case						=							initializer_factor
        _snake_case						=							layer_norm_eps
        _snake_case						=							share_link_tower_layers
        _snake_case						=							link_tower_type
        _snake_case						=							num_attention_heads
        _snake_case						=							num_hidden_layers
        _snake_case						=							tie_word_embeddings
        _snake_case						=							init_layernorm_from_vision_encoder

        if text_config is None:
          _snake_case						=							{}
          logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''		)

        if vision_config is None:
          _snake_case						=							{}
          logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''		)

        _snake_case						=							BridgeTowerTextConfig(**A__		)
        _snake_case						=							BridgeTowerVisionConfig(**A__		)



      @classmethod
      def 						UpperCamelCase_		(      cls				:    Tuple      , A__				:    BridgeTowerTextConfig      , A__				:    BridgeTowerVisionConfig      , **A__				:    Dict		)   ->					int:
        return cls(text_config=text_config.to_dict()      , vision_config=vision_config.to_dict()      , **A__		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Union[str, Any]:
        _snake_case						=							copy.deepcopy(self.__dict__		)
        _snake_case						=							self.text_config.to_dict()
        _snake_case						=							self.vision_config.to_dict()
        _snake_case						=							self.__class__.model_type
        return output




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

