



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


UpperCAmelCase_									=							logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCAmelCase_									=							{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase_									=							{
    "RUCAIBox/mvp": 1_0_2_4,
}





class 		UpperCAmelCase						(						snake_case_				):
   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''']
   SCREAMING_SNAKE_CASE__							=     MvpTokenizer





   def __init__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase="replace"       ,     _lowerCAmelCase="<s>"       ,     _lowerCAmelCase="</s>"       ,     _lowerCAmelCase="</s>"       ,     _lowerCAmelCase="<s>"       ,     _lowerCAmelCase="<unk>"       ,     _lowerCAmelCase="<pad>"       ,     _lowerCAmelCase="<mask>"       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase=True       ,     **_lowerCAmelCase       ,     ):
         super().__init__(
             _lowerCAmelCase       ,     _lowerCAmelCase       ,     tokenizer_file=_lowerCAmelCase       ,     errors=_lowerCAmelCase       ,     bos_token=_lowerCAmelCase       ,     eos_token=_lowerCAmelCase       ,     sep_token=_lowerCAmelCase       ,     cls_token=_lowerCAmelCase       ,     unk_token=_lowerCAmelCase       ,     pad_token=_lowerCAmelCase       ,     mask_token=_lowerCAmelCase       ,     add_prefix_space=_lowerCAmelCase       ,     trim_offsets=_lowerCAmelCase       ,     **_lowerCAmelCase       ,     )

         _lowerCAmelCase 			=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
         if pre_tok_state.get('''add_prefix_space'''       ,     _lowerCAmelCase			) != add_prefix_space:
               _lowerCAmelCase 			=  getattr(_lowerCAmelCase       ,     pre_tok_state.pop('''type'''			)			)
               _lowerCAmelCase 			=  add_prefix_space
               _lowerCAmelCase 			=  pre_tok_class(**_lowerCAmelCase			)

         _lowerCAmelCase 			=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         _lowerCAmelCase 			=  '''post_processor'''
         _lowerCAmelCase 			=  getattr(self.backend_tokenizer       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)
         if tokenizer_component_instance:
               _lowerCAmelCase 			=  json.loads(tokenizer_component_instance.__getstate__()			)

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     _lowerCAmelCase 			=  tuple(state['''sep''']			)
               if "cls" in state:
                     _lowerCAmelCase 			=  tuple(state['''cls''']			)

               _lowerCAmelCase 			=  False

               if state.get('''add_prefix_space'''       ,     _lowerCAmelCase			) != add_prefix_space:
                     _lowerCAmelCase 			=  add_prefix_space
                     _lowerCAmelCase 			=  True

               if state.get('''trim_offsets'''       ,     _lowerCAmelCase			) != trim_offsets:
                     _lowerCAmelCase 			=  trim_offsets
                     _lowerCAmelCase 			=  True

               if changes_to_apply:
                     _lowerCAmelCase 			=  getattr(_lowerCAmelCase       ,     state.pop('''type'''			)			)
                     _lowerCAmelCase 			=  component_class(**_lowerCAmelCase			)
                     setattr(self.backend_tokenizer       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  AddedToken(_lowerCAmelCase       ,     lstrip=_lowerCAmelCase       ,     rstrip=_lowerCAmelCase			) if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			) else value
         _lowerCAmelCase 			=  value





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.get('''is_split_into_words'''       ,     _lowerCAmelCase			)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   '''to use it with pretokenized inputs.'''			)

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





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.get('''is_split_into_words'''       ,     _lowerCAmelCase			)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   '''to use it with pretokenized inputs.'''			)

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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  self._tokenizer.model.save(_lowerCAmelCase       ,     name=_lowerCAmelCase			)
         return tuple(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         _lowerCAmelCase 			=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  [self.sep_token_id]
         _lowerCAmelCase 			=  [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 + sep + token_ids_a + sep			) * [0]




from __future__ import annotations
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list				)->list:
      if len(_SCREAMING_SNAKE_CASE				) == 0:
            return []
      _lowerCAmelCase			,   _lowerCAmelCase 			=  min(_SCREAMING_SNAKE_CASE				), max(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  int(max_value - min_value				) + 1
      _lowerCAmelCase 			=  [[] for _ in range(_SCREAMING_SNAKE_CASE				)]

      for i in my_list:
            buckets[int(i - min_value				)].append(_SCREAMING_SNAKE_CASE				)

      return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE				)]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]