




'''simple docstring'''


import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_      :  Optional[Any]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  Optional[int]				     =  {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

lowerCAmelCase_      :  Any				     =  {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

lowerCAmelCase_      :  Optional[int]				     =  {
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     a_     :							List[str]     						=  set()
     a_     :							Dict     						=  word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          a_     :							Any     						=  char

     a_     :							Optional[Any]     						=  set(UpperCamelCase__				)
     return pairs







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Optional[int]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       Union[str, Any]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Optional[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






      def __init__( self   :  List[str]						,						lowercase__   :  Any						,						lowercase__   :  List[str]						,						lowercase__   :  Optional[int]="<s>"						,						lowercase__   :  List[str]="</s>"						,						lowercase__   :  Union[str, Any]="</s>"						,						lowercase__   :  str="<s>"						,						lowercase__   :  List[str]="<unk>"						,						lowercase__   :  List[str]="<pad>"						,						lowercase__   :  Dict="<mask>"						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           super().__init__(
               bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						**lowercase__						,						)

           a_     :							str     						=  vocab_file
           a_     :							Any     						=  merges_file

           a_     :							List[Any]     						=  {}
           a_     :							str     						=  0
           a_     :							Optional[int]     						=  1
           a_     :							Union[str, Any]     						=  2
           a_     :							Any     						=  3

           self.add_from_file(lowercase__       )

           a_     :							Any     						=  {v: k for k, v in self.encoder.items()}

           with open(lowercase__						,						encoding="""utf-8"""       ) as merges_handle:
                a_     :							Union[str, Any]     						=  merges_handle.read().split("""\n"""       )[:-1]
           a_     :							str     						=  [tuple(merge.split()[:-1]       ) for merge in merges]
           a_     :							List[Any]     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )
           a_     :							List[str]     						=  {}






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           a_     :							Dict     						=  [self.cls_token_id]
           a_     :							Optional[int]     						=  [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None						,						lowercase__   :  bool = False       ):


           '''simple docstring'''

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

           if token_ids_a is None:
                return [1] + ([0] * len(lowercase__       )) + [1]
           return [1] + ([0] * len(lowercase__       )) + [1, 1] + ([0] * len(lowercase__       )) + [1]






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Any     						=  [self.sep_token_id]
           a_     :							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]






      @property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return len(self.encoder       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return dict(self.encoder						,						**self.added_tokens_encoder       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if token in self.cache:
                return self.cache[token]
           a_     :							Any     						=  tuple(lowercase__       )
           a_     :							Tuple     						=  tuple(list(word[:-1]       ) + [word[-1] + """</w>"""]       )
           a_     :							str     						=  get_pairs(lowercase__       )

           if not pairs:
                return token

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

                     if word[i] == first and i < len(lowercase__       ) - 1 and word[i + 1] == second:
                          new_word.append(first + second       )
                          i += 2
                     else:
                          new_word.append(word[i]       )
                          i += 1
                a_     :							List[str]     						=  tuple(lowercase__       )
                a_     :							Optional[Any]     						=  new_word
                if len(lowercase__       ) == 1:
                     break
                else:
                     a_     :							str     						=  get_pairs(lowercase__       )
           a_     :							Tuple     						=  """@@ """.join(lowercase__       )
           a_     :							Any     						=  word[:-4]
           a_     :							List[Any]     						=  word
           return word






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  []

           a_     :							Any     						=  re.findall(r"""\S+\n?"""						,						lowercase__       )

           for token in words:
                split_tokens.extend(list(self.bpe(lowercase__       ).split(""" """       )       )       )
           return split_tokens






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           return self.encoder.get(lowercase__						,						self.encoder.get(self.unk_token       )       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           return self.decoder.get(lowercase__						,						self.unk_token       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							Dict     						=  """ """.join(lowercase__       ).replace("""@@ """						,						""""""       ).strip()
           return out_string






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							int     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
           a_     :							List[str]     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ):
                copyfile(self.vocab_file						,						lowercase__       )

           if os.path.abspath(self.merges_file       ) != os.path.abspath(lowercase__       ):
                copyfile(self.merges_file						,						lowercase__       )

           return out_vocab_file, out_merge_file






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           if isinstance(lowercase__						,						lowercase__       ):
                try:
                     with open(lowercase__						,						"""r"""						,						encoding="""utf-8"""       ) as fd:
                          self.add_from_file(lowercase__       )
                except FileNotFoundError as fnfe:
                     raise fnfe
                except UnicodeError:
                     raise Exception(F"Incorrect encoding detected in {f}, please rebuild the dataset"       )
                return

           a_     :							Optional[Any]     						=  f.readlines()
           for lineTmp in lines:
                a_     :							List[str]     						=  lineTmp.strip()
                a_     :							int     						=  line.rfind(""" """       )
                if idx == -1:
                     raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'"""       )
                a_     :							Tuple     						=  line[:idx]
                a_     :							Tuple     						=  len(self.encoder       )






'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
