




'''simple docstring'''






import copy
import random

from transformers import CLIPTokenizer






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Union[str, Any]     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					Tuple			):
           super().__init__(*__lowerCamelCase     ,							**__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    {}





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Dict     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Tuple			):
           snake_case__       :   int	      =    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     :					Optional[int]     ,							__lowerCamelCase     :					List[str]     ,							*__lowerCamelCase     :					str     ,							__lowerCamelCase     :					int=1     ,							**__lowerCamelCase     :					Dict			):
           snake_case__       :   Dict	      =    []
           if num_vec_per_token == 1:
                 self.try_adding_tokens(__lowerCamelCase     ,							*__lowerCamelCase     ,							**__lowerCamelCase			)
                 output.append(__lowerCamelCase			)
           else:
                 snake_case__       :   Optional[int]	      =    []
                 for i in range(__lowerCamelCase			):
                       snake_case__       :   Dict	      =    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"			)
           snake_case__       :   Union[str, Any]	      =    output





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[int]=False     ,							__lowerCamelCase     :					str=1.0			):
           if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   Dict	      =    []
                 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:
                       snake_case__       :   List[str]	      =    self.token_map[placeholder_token]
                       snake_case__       :   List[Any]	      =    tokens[: 1 + int(len(__lowerCamelCase			) * prop_tokens_to_load			)]
                       if vector_shuffle:
                             snake_case__       :   Optional[Any]	      =    copy.copy(__lowerCamelCase			)
                             random.shuffle(__lowerCamelCase			)
                       snake_case__       :   Union[str, Any]	      =    text.replace(__lowerCamelCase     ,							' '.join(__lowerCamelCase			)			)
           return text





     def __call__(     self     :					str     ,							__lowerCamelCase     :					Dict     ,							*__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[Any]=False     ,							__lowerCamelCase     :					int=1.0     ,							**__lowerCamelCase     :					int			):
           return super().__call__(
               self.replace_placeholder_tokens_in_text(
                   __lowerCamelCase     ,							vector_shuffle=__lowerCamelCase     ,							prop_tokens_to_load=__lowerCamelCase			)     ,							*__lowerCamelCase     ,							**__lowerCamelCase     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							*__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					int=False     ,							__lowerCamelCase     :					Optional[int]=1.0     ,							**__lowerCamelCase     :					Dict			):
           return super().encode(
               self.replace_placeholder_tokens_in_text(
                   __lowerCamelCase     ,							vector_shuffle=__lowerCamelCase     ,							prop_tokens_to_load=__lowerCamelCase			)     ,							*__lowerCamelCase     ,							**__lowerCamelCase     ,							)






'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
