


"""simple docstring"""







from __future__ import annotations

from collections import deque




class  UpperCamelCase_	:

	"""simple docstring"""





	def __init__( self			:				str			,			UpperCAmelCase__			:				list[str]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				[]
						self.adlist.append(
						    {"value": "", "next_states": [], "fail_state": 0, "output": []}   )

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




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				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[Any]			,			UpperCAmelCase__			:				str   )       ->	None:
						__SCREAMING_SNAKE_CASE						   =				0
						for character in keyword:
											__SCREAMING_SNAKE_CASE						   =				self.find_next_state(UpperCAmelCase__			,			UpperCAmelCase__   )
											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   )
																__SCREAMING_SNAKE_CASE						   =				len(self.adlist   ) - 1
											else:
																__SCREAMING_SNAKE_CASE						   =				next_state
						self.adlist[current_state]["output"].append(UpperCAmelCase__   )




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




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


if __name__ == "__main__":
		import doctest

		doctest.testmod()






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

