






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__ =			{
    "vocab_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
        ),
        "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
        "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-openqa": (
            "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-reader": (
            "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-openqa": (
            "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-reader": (
            "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__ =			{
    "google/realm-cc-news-pretrained-embedder": 512,
    "google/realm-cc-news-pretrained-encoder": 512,
    "google/realm-cc-news-pretrained-scorer": 512,
    "google/realm-cc-news-pretrained-openqa": 512,
    "google/realm-orqa-nq-openqa": 512,
    "google/realm-orqa-nq-reader": 512,
    "google/realm-orqa-wq-openqa": 512,
    "google/realm-orqa-wq-reader": 512,
}

UpperCamelCase__ =			{
    "google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-reader": {"do_lower_case": True},
    "google/realm-orqa-wq-openqa": {"do_lower_case": True},
    "google/realm-orqa-wq-reader": {"do_lower_case": True},
}


class        _UpperCAmelCase						(		_A			):
    __lowerCamelCase:   Any		       =    VOCAB_FILES_NAMES
    __lowerCamelCase:   Optional[int]		       =    PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:   Tuple		       =    PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase:   Tuple		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:   List[Any]		       =    RealmTokenizer




    def __init__(			self  :       Dict  ,     a  :       Tuple=None  ,     a  :       Dict=None  ,     a  :       Union[str, Any]=True  ,     a  :       List[Any]="[UNK]"  ,     a  :       List[str]="[SEP]"  ,     a  :       int="[PAD]"  ,     a  :       Optional[Any]="[CLS]"  ,     a  :       Dict="[MASK]"  ,     a  :       Any=True  ,     a  :       Optional[int]=None  ,     **a  :       str  ,     ):






       '''simple docstring'''






       super().__init__(
           a  ,     tokenizer_file=a  ,     do_lower_case=a  ,     unk_token=a  ,     sep_token=a  ,     pad_token=a  ,     cls_token=a  ,     mask_token=a  ,     tokenize_chinese_chars=a  ,     strip_accents=a  ,     **a  ,     )

       lowercase_	:    List[str]           =     json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
       if (
           normalizer_state.get("lowercase"  ,     a			) != do_lower_case
           or normalizer_state.get("strip_accents"  ,     a			) != strip_accents
           or normalizer_state.get("handle_chinese_chars"  ,     a			) != tokenize_chinese_chars
       ):
          lowercase_	:    str           =     getattr(a  ,     normalizer_state.pop("type"			)			)
          lowercase_	:    Tuple           =     do_lower_case
          lowercase_	:    Union[str, Any]           =     strip_accents
          lowercase_	:    List[Any]           =     tokenize_chinese_chars
          lowercase_	:    Any           =     normalizer_class(**a			)

       lowercase_	:    Any           =     do_lower_case




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     **a  :       int			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     PaddingStrategy.MAX_LENGTH

       lowercase_	:    Any           =     text
       lowercase_	:    List[Any]           =     kwargs.pop("text_pair"  ,     a			)
       lowercase_	:    Optional[int]           =     kwargs.pop("return_tensors"  ,     a			)

       lowercase_	:    List[Any]           =     {
           "input_ids": [],
           "attention_mask": [],
           "token_type_ids": [],
       }

       for idx, candidate_text in enumerate(a			):
          if batch_text_pair is not None:
             lowercase_	:    Tuple           =     batch_text_pair[idx]
          else:
             lowercase_	:    str           =     None

          lowercase_	:    Optional[Any]           =     super().__call__(a  ,     a  ,     return_tensors=a  ,     **a			)

          lowercase_	:    Union[str, Any]           =     encoded_candidates.get("input_ids"			)
          lowercase_	:    Optional[Any]           =     encoded_candidates.get("attention_mask"			)
          lowercase_	:    int           =     encoded_candidates.get("token_type_ids"			)

          if encoded_input_ids is not None:
             output_data["input_ids"].append(a			)
          if encoded_attention_mask is not None:
             output_data["attention_mask"].append(a			)
          if encoded_token_type_ids is not None:
             output_data["token_type_ids"].append(a			)

       lowercase_	:    str           =     {key: item for key, item in output_data.items() if len(a			) != 0}

       return BatchEncoding(a  ,     tensor_type=a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       List[Any]  ,     a  :       Dict=None			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       List[Any]  ,     a  :       List[Any] = None			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     [self.sep_token_id]
       lowercase_	:    str           =     [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 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       List[str] = None			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self._tokenizer.model.save(a  ,     name=a			)
       return tuple(a			)












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      lowercase_	:    List[str]           =     True
      for i in range(0						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Union[str, Any]           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    Any           =     False

      for i in range(1						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Tuple           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    List[Any]           =     False
   return input_list


if __name__ == "__main__":
 print('Enter list to be sorted')
 UpperCamelCase__ =			[int(x) for x in input().split()]
 # inputing elements of the list in one line
 UpperCamelCase__ =			odd_even_sort(input_list)
 print('The sorted list is')
 print(sorted_list)




