
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


UpperCamelCase_	:    str  					=       logging.get_logger(__name__)

UpperCamelCase_	:    str  					=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_	:    Dict  					=       {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

UpperCamelCase_	:    Any  					=       {
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

UpperCamelCase_	:    Optional[int]  					=       {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}




class      __lowercase	(	__snake_case					):
	_A   			=      VOCAB_FILES_NAMES
	_A   			=      PRETRAINED_VOCAB_FILES_MAP
	_A   			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A   			=      ["input_ids", "attention_mask", "token_type_ids"]
	_A   			=      DebertaTokenizer






	def __init__(self							:   Optional[int]			,  snake_case							:   int=None			,  snake_case							:   int=None			,  snake_case							:   Union[str, Any]=None			,  snake_case							:   Dict="replace"			,  snake_case							:   str="[CLS]"			,  snake_case							:   Optional[int]="[SEP]"			,  snake_case							:   Dict="[SEP]"			,  snake_case							:   Optional[int]="[CLS]"			,  snake_case							:   int="[UNK]"			,  snake_case							:   Dict="[PAD]"			,  snake_case							:   Union[str, Any]="[MASK]"			,  snake_case							:   List[str]=False			,  **snake_case							:   Union[str, Any]			,  )   -> int:
							super().__init__(
							    snake_case			,  snake_case			,  tokenizer_file=snake_case			,  errors=snake_case			,  bos_token=snake_case			,  eos_token=snake_case			,  unk_token=snake_case			,  sep_token=snake_case			,  cls_token=snake_case			,  pad_token=snake_case			,  mask_token=snake_case			,  add_prefix_space=snake_case			,  **snake_case			,  )
							_lowercase					:  List[str]    			=      kwargs.pop("add_bos_token"			,  snake_case						)

							_lowercase					:  List[Any]    			=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("add_prefix_space"			,  snake_case						) != add_prefix_space:
													_lowercase					:  Union[str, Any]    			=      getattr(snake_case			,  pre_tok_state.pop("type"						)						)
													_lowercase					:  Union[str, Any]    			=      add_prefix_space
													_lowercase					:  Optional[Any]    			=      pre_tok_class(**snake_case						)

							_lowercase					:  List[Any]    			=      add_prefix_space






	@property
	def 			_a(self							:   Optional[Any]						)   -> str:
							if self._mask_token is None:
													if self.verbose:
																			logger.error("Using mask_token, but it is not set yet."						)
													return None
							return str(self._mask_token						)






	@mask_token.setter
	def 			_a(self							:   Dict			,  snake_case							:   Any						)   -> Any:
							_lowercase					:  Any    			=      AddedToken(snake_case			,  lstrip=snake_case			,  rstrip=snake_case						) if isinstance(snake_case			,  snake_case						) else value
							_lowercase					:  Optional[int]    			=      value






	def 			_a(self							:   Tuple			,  snake_case							:   List[int]			,  snake_case							:   Optional[List[int]] = None						)   -> List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowercase					:  List[str]    			=      [self.cls_token_id]
							_lowercase					:  Optional[Any]    			=      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep






	def 			_a(self							:   Any			,  snake_case							:   List[int]			,  snake_case							:   Optional[List[int]] = None						)   -> List[int]:
							_lowercase					:  List[str]    			=      [self.sep_token_id]
							_lowercase					:  Optional[Any]    			=      [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






	def 			_a(self							:   Any			,  *snake_case							:   Optional[int]			,  **snake_case							:   List[str]						)   -> BatchEncoding:
							_lowercase					:  Dict    			=      kwargs.get("is_split_into_words"			,  snake_case						)
							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*snake_case			,  **snake_case						)






	def 			_a(self							:   Any			,  *snake_case							:   Dict			,  **snake_case							:   str						)   -> BatchEncoding:
							_lowercase					:  Tuple    			=      kwargs.get("is_split_into_words"			,  snake_case						)

							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*snake_case			,  **snake_case						)






	def 			_a(self							:   int			,  snake_case							:   str			,  snake_case							:   Optional[str] = None						)   -> Tuple[str]:
							_lowercase					:  Tuple    			=      self._tokenizer.model.save(snake_case			,  name=snake_case						)
							return tuple(snake_case						)






	def 			_a(self							:   Optional[int]			,  snake_case							:   "Conversation"						)   -> List[int]:
							_lowercase					:  Any    			=      []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(snake_case			,  add_special_tokens=snake_case						) + [self.eos_token_id]						)

							if len(snake_case						) > self.model_max_length:
													_lowercase					:  int    			=      input_ids[-self.model_max_length :]
							return input_ids






from __future__ import annotations



def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
						while start_ind <= end_ind:
												if array[start_ind] == search_item:
																		return start_ind
												elif array[end_ind] == search_item:
																		return end_ind
												else:
																		start_ind += 1
																		end_ind -= 1
    # returns -1 if search_item is not found in array
						return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(100)), 40))




