





from __future__ import annotations

from collections import deque





class 					A_					:







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						list[str]	)    ->       List[str]:
         _lowercase						    =			[]
         self.adlist.append(
             {'value': '', 'next_states': [], 'fail_state': 0, 'output': []}	)

         for keyword in keywords:
           self.add_keyword(__A	)
         self.set_fail_transitions()







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						int    ,__A						:						str	)    ->       int | None:
         for state in self.adlist[current_state]["next_states"]:
           if char == self.adlist[state]["value"]:
             return state
         return None







       def  __UpperCAmelCase			(     self						:						Optional[int]    ,__A						:						str	)    ->       None:
         _lowercase						    =			0
         for character in keyword:
           _lowercase						    =			self.find_next_state(__A    ,__A	)
           if next_state is None:
             self.adlist.append(
                 {
                     'value': character,
                     'next_states': [],
                     'fail_state': 0,
                     'output': [],
                 }	)
             self.adlist[current_state]["next_states"].append(len(self.adlist	) - 1	)
             _lowercase						    =			len(self.adlist	) - 1
           else:
             _lowercase						    =			next_state
         self.adlist[current_state]["output"].append(__A	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       None:
         _lowercase						    =			deque()
         for node in self.adlist[0]["next_states"]:
           q.append(__A	)
           _lowercase						    =			0
         while q:
           _lowercase						    =			q.popleft()
           for child in self.adlist[r]["next_states"]:
             q.append(__A	)
             _lowercase						    =			self.adlist[r]['fail_state']
             while (
                 self.find_next_state(__A    ,self.adlist[child]['value']	) is None
                 and state != 0
             ):
               _lowercase						    =			self.adlist[state]['fail_state']
             _lowercase						    =			self.find_next_state(
                 __A    ,self.adlist[child]['value']	)
             if self.adlist[child]["fail_state"] is None:
               _lowercase						    =			0
             _lowercase						    =			(
                 self.adlist[child]['output']
                 + self.adlist[self.adlist[child]['fail_state']]['output']
             )







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						str	)    ->       dict[str, list[int]]:
         _lowercase						    =			{}  # returns a dict with keywords and list of its occurrences
         _lowercase						    =			0
         for i in range(len(__A	)	):
           while (
               self.find_next_state(__A    ,string[i]	) is None
               and current_state != 0
           ):
             _lowercase						    =			self.adlist[current_state]['fail_state']
           _lowercase						    =			self.find_next_state(__A    ,string[i]	)
           if next_state is None:
             _lowercase						    =			0
           else:
             _lowercase						    =			next_state
             for key in self.adlist[current_state]["output"]:
               if key not in result:
                 _lowercase						    =			[]
               result[key].append(i - len(__A	) + 1	)
         return result


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



