


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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

UpperCamelCase			:					str         =							logging.get_logger(__name__)

UpperCamelCase			:					Dict         =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

UpperCamelCase			:					int         =							{
    '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			:					List[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			:					Dict         =							{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}
def 				UpperCamelCase_	(					)->   Optional[Any]:




						'''simple docstring'''
						__magic_name__					=      (
						    list(range(ord('!'  )						,      ord('~'  ) + 1  )  ) + list(range(ord('¡'  )						,      ord('¬'  ) + 1  )  ) + list(range(ord('®'  )						,      ord('ÿ'  ) + 1  )  )
						)
						__magic_name__					=      bs[:]
						__magic_name__					=      0
						for b in range(2**8  ):
												if b not in bs:
																		bs.append(UpperCAmelCase_  )
																		cs.append(2**8 + n  )
																		n += 1
						__magic_name__					=      [chr(UpperCAmelCase_  ) for n in cs]
						return dict(zip(UpperCAmelCase_						,      UpperCAmelCase_  )  )
def 				UpperCamelCase_	(					__snake_case      :    str  )->   List[Any]:




						'''simple docstring'''
						__magic_name__					=      set()
						__magic_name__					=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)  )
												__magic_name__					=      char
						return pairs




class      __snake_case				(   _lowerCamelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:       Dict								=			PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:       Union[str, Any]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:       Any								=			["input_ids", "attention_mask", "token_type_ids"]
					def __init__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Tuple="replace"	,      _lowerCAmelCase       :						Any="[CLS]"	,      _lowerCAmelCase       :						Any="[SEP]"	,      _lowerCAmelCase       :						Optional[Any]="[SEP]"	,      _lowerCAmelCase       :						List[str]="[CLS]"	,      _lowerCAmelCase       :						Union[str, Any]="[UNK]"	,      _lowerCAmelCase       :						Optional[Any]="[PAD]"	,      _lowerCAmelCase       :						str="[MASK]"	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						List[str]=False	,      **_lowerCAmelCase       :						Any	,      )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else bos_token
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else eos_token
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else sep_token
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else cls_token
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else unk_token
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											__magic_name__					=      AddedToken(_lowerCAmelCase	,      lstrip=_lowerCAmelCase	,      rstrip=_lowerCAmelCase  ) if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else mask_token

											super().__init__(
											    errors=_lowerCAmelCase	,      bos_token=_lowerCAmelCase	,      eos_token=_lowerCAmelCase	,      unk_token=_lowerCAmelCase	,      sep_token=_lowerCAmelCase	,      cls_token=_lowerCAmelCase	,      pad_token=_lowerCAmelCase	,      mask_token=_lowerCAmelCase	,      add_prefix_space=_lowerCAmelCase	,      add_bos_token=_lowerCAmelCase	,      **_lowerCAmelCase	,      )
											__magic_name__					=      add_bos_token

											with open(_lowerCAmelCase	,      encoding='utf-8'  ) as vocab_handle:
																	__magic_name__					=      json.load(_lowerCAmelCase  )
											__magic_name__					=      {v: k for k, v in self.encoder.items()}
											__magic_name__					=      errors  # how to handle errors in decoding
											__magic_name__					=      bytes_to_unicode()
											__magic_name__					=      {v: k for k, v in self.byte_encoder.items()}
											with open(_lowerCAmelCase	,      encoding='utf-8'  ) as merges_handle:
																	__magic_name__					=      merges_handle.read().split('\n'  )[1:-1]
											__magic_name__					=      [tuple(merge.split()  ) for merge in bpe_merges]
											__magic_name__					=      dict(zip(_lowerCAmelCase	,      range(len(_lowerCAmelCase  )  )  )  )
											__magic_name__					=      {}
											__magic_name__					=      add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											__magic_name__					=      re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'  )
					@property
					# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
					def 	a			(						self       :						int  )							->					int:



											'''simple docstring'''





											return len(self.encoder  )
					def 	a			(						self       :						List[Any]  )							->					int:



											'''simple docstring'''





											return dict(self.encoder	,      **self.added_tokens_encoder  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						Tuple  )							->					Union[str, Any]:



											'''simple docstring'''





											if token in self.cache:
																	return self.cache[token]
											__magic_name__					=      tuple(_lowerCAmelCase  )
											__magic_name__					=      get_pairs(_lowerCAmelCase  )

											if not pairs:
																	return token

											while True:
																	__magic_name__					=      min(_lowerCAmelCase	,      key=lambda _lowerCAmelCase  : self.bpe_ranks.get(_lowerCAmelCase	,      float('inf'  )  )  )
																	if bigram not in self.bpe_ranks:
																							break
																	__magic_name__		,      __magic_name__					=      bigram
																	__magic_name__					=      []
																	__magic_name__					=      0
																	while i < len(_lowerCAmelCase  ):
																							try:
																													__magic_name__					=      word.index(_lowerCAmelCase	,      _lowerCAmelCase  )
																							except ValueError:
																													new_word.extend(word[i:]  )
																													break
																							else:
																													new_word.extend(word[i:j]  )
																													__magic_name__					=      j

																							if word[i] == first and i < len(_lowerCAmelCase  ) - 1 and word[i + 1] == second:
																													new_word.append(first + second  )
																													i += 2
																							else:
																													new_word.append(word[i]  )
																													i += 1
																	__magic_name__					=      tuple(_lowerCAmelCase  )
																	__magic_name__					=      new_word
																	if len(_lowerCAmelCase  ) == 1:
																							break
																	else:
																							__magic_name__					=      get_pairs(_lowerCAmelCase  )
											__magic_name__					=      ' '.join(_lowerCAmelCase  )
											__magic_name__					=      word
											return word
					def 	a			(						self       :						List[str]	,      _lowerCAmelCase       :						List[int]	,      _lowerCAmelCase       :						Optional[List[int]] = None  )							->					Dict:



											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__magic_name__					=      [self.cls_token_id]
											__magic_name__					=      [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep
					def 	a			(						self       :						int	,      _lowerCAmelCase       :						List[int]	,      _lowerCAmelCase       :						Optional[List[int]] = None	,      _lowerCAmelCase       :						bool = False  )							->					List[Any]:



											'''simple docstring'''





											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_lowerCAmelCase	,      token_ids_a=_lowerCAmelCase	,      already_has_special_tokens=_lowerCAmelCase  )

											if token_ids_a is None:
																	return [1] + ([0] * len(_lowerCAmelCase  )) + [1]
											return [1] + ([0] * len(_lowerCAmelCase  )) + [1] + ([0] * len(_lowerCAmelCase  )) + [1]
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						List[int]	,      _lowerCAmelCase       :						Optional[List[int]] = None  )							->					str:



											'''simple docstring'''





											__magic_name__					=      [self.sep_token_id]
											__magic_name__					=      [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       :						int	,      _lowerCAmelCase       :						List[Any]  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      []
											for token in re.findall(self.pat	,      _lowerCAmelCase  ):
																	__magic_name__					=      ''.join(
																	    self.byte_encoder[b] for b in token.encode('utf-8'  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																	bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase  ).split(' '  )  )
											return bpe_tokens
					def 	a			(						self       :						Union[str, Any]	,      _lowerCAmelCase       :						Optional[Any]  )							->					Union[str, Any]:



											'''simple docstring'''





											return self.encoder.get(_lowerCAmelCase	,      self.encoder.get(self.unk_token  )  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						List[str]  )							->					Optional[int]:



											'''simple docstring'''





											return self.decoder.get(_lowerCAmelCase  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						List[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      ''.join(_lowerCAmelCase  )
											__magic_name__					=      bytearray([self.byte_decoder[c] for c in text]  ).decode('utf-8'	,      errors=self.errors  )
											return text
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						Optional[str] = None  )							->					Any:



											'''simple docstring'''





											if not os.path.isdir(_lowerCAmelCase  ):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
																	return
											__magic_name__					=      os.path.join(
											    _lowerCAmelCase	,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
											__magic_name__					=      os.path.join(
											    _lowerCAmelCase	,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']  )

											with open(_lowerCAmelCase	,      'w'	,      encoding='utf-8'  ) as f:
																	f.write(json.dumps(self.encoder	,      indent=2	,      sort_keys=_lowerCAmelCase	,      ensure_ascii=_lowerCAmelCase  ) + '\n'  )

											__magic_name__					=      0
											with open(_lowerCAmelCase	,      'w'	,      encoding='utf-8'  ) as writer:
																	writer.write('#version: 0.2\n'  )
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,      key=lambda _lowerCAmelCase  : kv[1]  ):
																							if index != token_index:
																													logger.warning(
																													    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																													    ' Please check that the tokenizer is not corrupted!'  )
																													__magic_name__					=      token_index
																							writer.write(' '.join(_lowerCAmelCase  ) + '\n'  )
																							index += 1

											return vocab_file, merge_file
					def 	a			(						self       :						Any	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						str=False	,      **_lowerCAmelCase       :						List[str]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      kwargs.pop('add_prefix_space'	,      self.add_prefix_space  )
											if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase  ) > 0 and not text[0].isspace()):
																	__magic_name__					=      ' ' + text
											return (text, kwargs)







					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						"Conversation"  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      []
											for is_user, text in conversation.iter_texts():
																	input_ids.extend(self.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  ) + [self.eos_token_id]  )
											if len(_lowerCAmelCase  ) > self.model_max_length:
																	__magic_name__					=      input_ids[-self.model_max_length :]
											return input_ids




"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






