'''simple docstring'''

import copy
import random

from transformers import CLIPTokenizer


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,*lowerCamelCase_       ,**lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         super().__init__(*lowerCamelCase_       ,**lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	{}




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,*lowerCamelCase_       ,**lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	super().add_tokens(lowerCamelCase_       ,*lowerCamelCase_       ,**lowerCamelCase_				)
         if num_added_tokens == 0:
            raise ValueError(
                f'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
                ''' `placeholder_token` that is not already in the tokenizer.'''				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,*lowerCamelCase_       ,lowerCamelCase_=1       ,**lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	[]
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase_       ,*lowerCamelCase_       ,**lowerCamelCase_				)
            output.append(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Tuple 					=	[]
            for i in range(lowerCamelCase_				):
               UpperCAmelCase__				:					Optional[int] 					=	placeholder_token + f'''_{i}'''
               self.try_adding_tokens(lowerCamelCase_       ,*lowerCamelCase_       ,**lowerCamelCase_				)
               output.append(lowerCamelCase_				)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   f'''The tokenizer already has placeholder token {token} that can get confused with'''
                   f''' {placeholder_token}keep placeholder tokens independent'''				)
         UpperCAmelCase__				:					Dict 					=	output




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=False       ,lowerCamelCase_=1.0				)      ->      int:



         '''simple docstring'''



         if isinstance(lowerCamelCase_       ,lowerCamelCase_				):
            UpperCAmelCase__				:					Union[str, Any] 					=	[]
            for i in range(len(lowerCamelCase_				)				):
               output.append(self.replace_placeholder_tokens_in_text(text[i]       ,vector_shuffle=lowerCamelCase_				)				)
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               UpperCAmelCase__				:					Optional[int] 					=	self.token_map[placeholder_token]
               UpperCAmelCase__				:					str 					=	tokens[: 1 + int(len(lowerCamelCase_				) * prop_tokens_to_load				)]
               if vector_shuffle:
                  UpperCAmelCase__				:					Optional[Any] 					=	copy.copy(lowerCamelCase_				)
                  random.shuffle(lowerCamelCase_				)
               UpperCAmelCase__				:					List[Any] 					=	text.replace(lowerCamelCase_       ,''' '''.join(lowerCamelCase_				)				)
         return text




      def __call__(					self       ,lowerCamelCase_       ,*lowerCamelCase_       ,lowerCamelCase_=False       ,lowerCamelCase_=1.0       ,**lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase_       ,vector_shuffle=lowerCamelCase_       ,prop_tokens_to_load=lowerCamelCase_				)       ,*lowerCamelCase_       ,**lowerCamelCase_       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,*lowerCamelCase_       ,lowerCamelCase_=False       ,lowerCamelCase_=1.0       ,**lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase_       ,vector_shuffle=lowerCamelCase_       ,prop_tokens_to_load=lowerCamelCase_				)       ,*lowerCamelCase_       ,**lowerCamelCase_       ,)





'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase )
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= field(default='''question-answering-extractive'''		,metadata={'''include_in_asdict_even_if_is_default''': True} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features(
          {
              '''answers''': Sequence(
                  {
                      '''text''': Value('''string''' ),
                      '''answer_start''': Value('''int32''' ),
                  } )
          } )
      UpperCAmelCase_   :   str   				= "question"
      UpperCAmelCase_   :   str   				= "context"
      UpperCAmelCase_   :   str   				= "answers"




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict[str, str]:



         '''simple docstring'''



         return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}




