


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_snake_case				=			logging.get_logger(__name__)

_snake_case				=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_snake_case				=			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

_snake_case				=			{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}







@lru_cache()
def 	A (		):
     UpperCAmelCase    :					Optional[int]			=      (
         list(range(ord("!"			)		,		ord("~"			) + 1			)			) + list(range(ord("¡"			)		,		ord("¬"			) + 1			)			) + list(range(ord("®"			)		,		ord("ÿ"			) + 1			)			)
     )
     UpperCAmelCase    :					str			=      bs[:]
     UpperCAmelCase    :					Dict			=      0
     for b in range(2**8			):
          if b not in bs:
               bs.append(A__			)
               cs.append(2**8 + n			)
               n += 1
     UpperCAmelCase    :					int			=      [chr(A__			) for n in cs]
     return dict(zip(A__		,		A__			)			)







def 	A (		A__			):
     UpperCAmelCase    :					Any			=      set()
     UpperCAmelCase    :					Optional[Any]			=      word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)			)
          UpperCAmelCase    :					Tuple			=      char
     return pairs




class  A_ (			a_  ):







 """simple docstring"""





 _UpperCamelCase					:			Any     	=       VOCAB_FILES_NAMES
 _UpperCamelCase					:			str     	=       PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase					:			Any     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase					:			Any     	=       ['''input_ids''', '''attention_mask''']
 def __init__(      self       :			str							,     _a       :			Union[str, Any]							,     _a       :			Dict							,     _a       :			Dict="replace"							,     _a       :			Union[str, Any]="<s>"							,     _a       :			Dict="</s>"							,     _a       :			Any="</s>"							,     _a       :			Any="<s>"							,     _a       :			Tuple="<unk>"							,     _a       :			Union[str, Any]="<pad>"							,     _a       :			List[str]="<mask>"							,     _a       :			int=False							,     **_a       :			Optional[Any]							,     )     ->      Dict:
      UpperCAmelCase    :					Tuple			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else bos_token
      UpperCAmelCase    :					Tuple			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else eos_token
      UpperCAmelCase    :					Union[str, Any]			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else sep_token
      UpperCAmelCase    :					Any			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else cls_token
      UpperCAmelCase    :					str			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else unk_token
      UpperCAmelCase    :					List[str]			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCAmelCase    :					List[Any]			=      AddedToken(_a							,     lstrip=_a							,     rstrip=_a					) if isinstance(_a							,     _a					) else mask_token

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

      with open(_a							,     encoding="utf-8"					) as vocab_handle:
           UpperCAmelCase    :					Tuple			=      json.load(_a					)
      UpperCAmelCase    :					List[Any]			=      {v: k for k, v in self.encoder.items()}
      UpperCAmelCase    :					List[str]			=      errors  # how to handle errors in decoding
      UpperCAmelCase    :					int			=      bytes_to_unicode()
      UpperCAmelCase    :					Optional[int]			=      {v: k for k, v in self.byte_encoder.items()}
      with open(_a							,     encoding="utf-8"					) as merges_handle:
           UpperCAmelCase    :					Any			=      merges_handle.read().split("\n"					)[1:-1]
      UpperCAmelCase    :					Union[str, Any]			=      [tuple(merge.split()					) for merge in bpe_merges]
      UpperCAmelCase    :					Optional[int]			=      dict(zip(_a							,     range(len(_a					)					)					)					)
      UpperCAmelCase    :					Dict			=      {}
      UpperCAmelCase    :					int			=      add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      UpperCAmelCase    :					Tuple			=      re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"					)
 @property
 def 	_UpperCamelCase     (      self       :			Optional[int]					)     ->      int:
      return len(self.encoder					)
 def 	_UpperCamelCase     (      self       :			str					)     ->      List[Any]:
      return dict(self.encoder							,     **self.added_tokens_encoder					)
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			Dict					)     ->      Dict:
      if token in self.cache:
           return self.cache[token]
      UpperCAmelCase    :					Dict			=      tuple(_a					)
      UpperCAmelCase    :					Union[str, Any]			=      get_pairs(_a					)

      if not pairs:
           return token

      while True:
           UpperCAmelCase    :					List[Any]			=      min(_a							,     key=lambda _a					: self.bpe_ranks.get(_a							,     float("inf"					)					)					)
           if bigram not in self.bpe_ranks:
                break
           UpperCAmelCase	,			UpperCAmelCase    :					str			=      bigram
           UpperCAmelCase    :					str			=      []
           UpperCAmelCase    :					Tuple			=      0
           while i < len(_a					):
                try:
                     UpperCAmelCase    :					Optional[int]			=      word.index(_a							,     _a					)
                except ValueError:
                     new_word.extend(word[i:]					)
                     break
                else:
                     new_word.extend(word[i:j]					)
                     UpperCAmelCase    :					Union[str, Any]			=      j

                if word[i] == first and i < len(_a					) - 1 and word[i + 1] == second:
                     new_word.append(first + second					)
                     i += 2
                else:
                     new_word.append(word[i]					)
                     i += 1
           UpperCAmelCase    :					int			=      tuple(_a					)
           UpperCAmelCase    :					List[str]			=      new_word
           if len(_a					) == 1:
                break
           else:
                UpperCAmelCase    :					Tuple			=      get_pairs(_a					)
      UpperCAmelCase    :					Union[str, Any]			=      " ".join(_a					)
      UpperCAmelCase    :					Union[str, Any]			=      word
      return word
 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			Union[str, Any]					)     ->      List[Any]:
      UpperCAmelCase    :					Any			=      []
      for token in re.findall(self.pat							,     _a					):
           UpperCAmelCase    :					Optional[int]			=      "".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(_a					).split(" "					)					)
      return bpe_tokens
 def 	_UpperCamelCase     (      self       :			List[str]							,     _a       :			List[str]					)     ->      int:
      return self.encoder.get(_a							,     self.encoder.get(self.unk_token					)					)
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			Optional[Any]					)     ->      List[Any]:
      return self.decoder.get(_a					)
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			List[Any]					)     ->      str:
      UpperCAmelCase    :					str			=      "".join(_a					)
      UpperCAmelCase    :					Optional[int]			=      bytearray([self.byte_decoder[c] for c in text]					).decode("utf-8"							,     errors=self.errors					)
      return text
 def 	_UpperCamelCase     (      self       :			Optional[int]							,     _a       :			str							,     _a       :			Optional[str] = None					)     ->      Tuple[str]:
      if not os.path.isdir(_a					):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
           return
      UpperCAmelCase    :					int			=      os.path.join(
          _a							,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
      UpperCAmelCase    :					Tuple			=      os.path.join(
          _a							,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

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

      UpperCAmelCase    :					Tuple			=      0
      with open(_a							,     "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 _a					: 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!"					)
                     UpperCAmelCase    :					List[str]			=      token_index
                writer.write(" ".join(_a					) + "\n"					)
                index += 1

      return vocab_file, merge_file
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			List[int]							,     _a       :			Optional[List[int]] = None					)     ->      List[int]:
      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCAmelCase    :					Any			=      [self.cls_token_id]
      UpperCAmelCase    :					Optional[Any]			=      [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			List[int]							,     _a       :			Optional[List[int]] = None							,     _a       :			bool = False					)     ->      List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_a							,     token_ids_a=_a							,     already_has_special_tokens=_a					)

      if token_ids_a is None:
           return [1] + ([0] * len(_a					)) + [1]
      return [1] + ([0] * len(_a					)) + [1, 1] + ([0] * len(_a					)) + [1]
 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			List[int]							,     _a       :			Optional[List[int]] = None					)     ->      List[int]:
      UpperCAmelCase    :					Dict			=      [self.sep_token_id]
      UpperCAmelCase    :					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 + sep + token_ids_a + sep					) * [0]

 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			Union[str, Any]							,     _a       :			Union[str, Any]=False							,     **_a       :			Dict					)     ->      Optional[int]:
      UpperCAmelCase    :					List[str]			=      kwargs.pop("add_prefix_space"							,     self.add_prefix_space					)
      if (is_split_into_words or add_prefix_space) and (len(_a					) > 0 and not text[0].isspace()):
           UpperCAmelCase    :					Tuple			=      " " + text
      return (text, kwargs)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



