



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


a_       :Dict				  =				logging.get_logger(__name__)

a_       :Union[str, Any]				  =				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_       :Any				  =				{
    "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"
        ),
    },
}

a_       :int				  =				{
    "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,
}

a_       :Dict				  =				{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE            =     ["""input_ids""", """attention_mask""", """token_type_ids"""]
     _SCREAMING_SNAKE_CASE            =     DebertaTokenizer






     def __init__(	self  :  List[str],			_snake_case  :  Optional[int]=None,			_snake_case  :  Any=None,			_snake_case  :  int=None,			_snake_case  :  str="replace",			_snake_case  :  Any="[CLS]",			_snake_case  :  Any="[SEP]",			_snake_case  :  Tuple="[SEP]",			_snake_case  :  int="[CLS]",			_snake_case  :  Union[str, Any]="[UNK]",			_snake_case  :  Optional[Any]="[PAD]",			_snake_case  :  int="[MASK]",			_snake_case  :  str=False,			**_snake_case  :  Union[str, Any],			)       ->Dict:
            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,			)
            snake_case__							:     int            =     kwargs.pop('add_bos_token',			_snake_case )

            snake_case__							:     Optional[int]            =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space',			_snake_case ) != add_prefix_space:
                   snake_case__							:     Dict            =     getattr(_snake_case,			pre_tok_state.pop('type' ) )
                   snake_case__							:     Optional[Any]            =     add_prefix_space
                   snake_case__							:     List[Any]            =     pre_tok_class(**_snake_case )

            snake_case__							:     Any            =     add_prefix_space






     @property
     def        lowercase_       (	self  :  Dict )       ->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        lowercase_       (	self  :  Dict,			_snake_case  :  Union[str, Any] )       ->Union[str, Any]:
            snake_case__							:     Any            =     AddedToken(_snake_case,			lstrip=_snake_case,			rstrip=_snake_case ) if isinstance(_snake_case,			_snake_case ) else value
            snake_case__							:     Dict            =     value






     def        lowercase_       (	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]
            snake_case__							:     Dict            =     [self.cls_token_id]
            snake_case__							:     str            =     [self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  List[int],			_snake_case  :  Optional[List[int]] = None )       ->List[int]:
            snake_case__							:     List[Any]            =     [self.sep_token_id]
            snake_case__							:     int            =     [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        lowercase_       (	self  :  Any,			*_snake_case  :  Union[str, Any],			**_snake_case  :  int )       ->BatchEncoding:
            snake_case__							:     List[Any]            =     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        lowercase_       (	self  :  Union[str, Any],			*_snake_case  :  str,			**_snake_case  :  Any )       ->BatchEncoding:
            snake_case__							:     Any            =     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        lowercase_       (	self  :  int,			_snake_case  :  str,			_snake_case  :  Optional[str] = None )       ->Tuple[str]:
            snake_case__							:     List[Any]            =     self._tokenizer.model.save(_snake_case,			name=_snake_case )
            return tuple(_snake_case )






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  "Conversation" )       ->List[int]:
            snake_case__							:     Dict            =     []
            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:
                   snake_case__							:     Union[str, Any]            =     input_ids[-self.model_max_length :]
            return input_ids











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






