






"""simple docstring"""




from __future__ import annotations

import math
import random






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int		,				UpperCamelCase     :	int    ):
  A__											=							d * e - 1
  A__											=							0
  A__											=							0
  while p == 0:
    A__											=							random.randint(2		,				n - 1    )
    A__											=							k
    while True:
      if t % 2 == 0:
        A__											=							t // 2
        A__											=							(g**t) % n
        A__											=							math.gcd(x - 1		,				UpperCamelCase    )
        if x > 1 and y > 1:
          A__											=							y
          A__											=							n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()












"""simple docstring"""




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

lowerCamelCase__			=   logging.get_logger(__name__)

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

lowerCamelCase__			=   {
    "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"
        ),
    },
}

lowerCamelCase__			=   {
    "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,
}

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






def 						_SCREAMING_SNAKE_CASE							( ):
  A__											=							(
      list(range(ord("""!"""    )		,				ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )		,				ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )		,				ord("""ÿ"""    ) + 1    )    )
  )
  A__											=							bs[:]
  A__											=							0
  for b in range(2**8    ):
    if b not in bs:
      bs.append(UpperCamelCase    )
      cs.append(2**8 + n    )
      n += 1
  A__											=							[chr(UpperCamelCase    ) for n in cs]
  return dict(zip(UpperCamelCase		,				UpperCamelCase    )    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str    ):
  A__											=							set()
  A__											=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    A__											=							char
  return pairs







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask", "token_type_ids"]






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__="replace"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[UNK]"						,      lowerCamelCase__="[PAD]"						,      lowerCamelCase__="[MASK]"						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else bos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else eos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else sep_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else cls_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else unk_token
      A__											=							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
      A__											=							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__						,      )
      A__											=							add_bos_token

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      A__											=							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   ):


      """simple docstring"""
      return len(self.encoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.encoder						,      **self.added_tokens_encoder   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if token in self.cache:
        return self.cache[token]
      A__											=							tuple(lowerCamelCase__   )
      A__											=							get_pairs(lowerCamelCase__   )

      if not pairs:
        return token

      while True:
        A__											=							min(lowerCamelCase__						,      key=lambda lowerCamelCase__   : self.bpe_ranks.get(lowerCamelCase__						,      float("""inf"""   )   )   )
        if bigram not in self.bpe_ranks:
          break
        A__						,A__											=							bigram
        A__											=							[]
        A__											=							0
        while i < len(lowerCamelCase__   ):
          try:
            A__											=							word.index(lowerCamelCase__						,      lowerCamelCase__   )
          except ValueError:
            new_word.extend(word[i:]   )
            break
          else:
            new_word.extend(word[i:j]   )
            A__											=							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
        A__											=							tuple(lowerCamelCase__   )
        A__											=							new_word
        if len(lowerCamelCase__   ) == 1:
          break
        else:
          A__											=							get_pairs(lowerCamelCase__   )
      A__											=							""" """.join(lowerCamelCase__   )
      A__											=							word
      return word






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      A__											=							[self.cls_token_id]
      A__											=							[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False   ):


      """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						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      A__											=							[self.sep_token_id]
      A__											=							[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						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for token in re.findall(self.pat						,      lowerCamelCase__   ):
        A__											=							"""""".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						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.encoder.get(lowerCamelCase__						,      self.encoder.get(self.unk_token   )   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.decoder.get(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							"""""".join(lowerCamelCase__   )
      A__											=							bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""						,      errors=self.errors   )
      return text






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if not os.path.isdir(lowerCamelCase__   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							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"""   )

      A__											=							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!"""   )
            A__											=							token_index
          writer.write(""" """.join(lowerCamelCase__   ) + """\n"""   )
          index += 1

      return vocab_file, merge_file






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=False						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							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()):
        A__											=							""" """ + text
      return (text, kwargs)






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      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:
        A__											=							input_ids[-self.model_max_length :]
      return input_ids




