
"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

_A      :	int       		=	logging.get_logger(__name__)

_A      :	int       		=	{"""vocab_file""": """tokenizer.model"""}

_A      :	Optional[int]       		=	{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
_A      :	Tuple       		=	{
    """hf-internal-testing/llama-tokenizer""": 20_48,
}
_A      :	str       		=	"""▁"""

_A      :	Dict       		=	"""[INST]""", """[/INST]"""
_A      :	str       		=	"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
_A      :	int       		=	"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""

class 		a__						(				a_						):
   __lowerCAmelCase						=    VOCAB_FILES_NAMES
   __lowerCAmelCase						=    PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase						=    ["""input_ids""", """attention_mask"""]
   def __init__(      self     ,   _a     ,   _a="<unk>"     ,   _a="<s>"     ,   _a="</s>"     ,   _a=None     ,   _a = None     ,   _a=True     ,   _a=False     ,   _a=False     ,   _a=True     ,   **_a     ,   ):
          lowercase   :    Optional[int]			=							{} if sp_model_kwargs is None else sp_model_kwargs
          lowercase   :    int			=							AddedToken(_a     ,   lstrip=_a     ,   rstrip=_a    ) if isinstance(_a     ,   _a    ) else bos_token
          lowercase   :    int			=							AddedToken(_a     ,   lstrip=_a     ,   rstrip=_a    ) if isinstance(_a     ,   _a    ) else eos_token
          lowercase   :    Optional[int]			=							AddedToken(_a     ,   lstrip=_a     ,   rstrip=_a    ) if isinstance(_a     ,   _a    ) else unk_token
          lowercase   :    Dict			=							AddedToken(_a     ,   lstrip=_a     ,   rstrip=_a    ) if isinstance(_a     ,   _a    ) else pad_token
          super().__init__(
              bos_token=_a     ,   eos_token=_a     ,   unk_token=_a     ,   pad_token=_a     ,   add_bos_token=_a     ,   add_eos_token=_a     ,   sp_model_kwargs=self.sp_model_kwargs     ,   clean_up_tokenization_spaces=_a     ,   legacy=_a     ,   **_a     ,   )
          if legacy:
                 logger.warning_once(
                     f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
                     " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"    )
          lowercase   :    List[Any]			=							legacy
          lowercase   :    Dict			=							vocab_file
          lowercase   :    List[Any]			=							add_bos_token
          lowercase   :    Optional[int]			=							add_eos_token
          lowercase   :    Optional[int]			=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.Load(_a    )
   def __getstate__(      self    ):
          lowercase   :    str			=							self.__dict__.copy()
          lowercase   :    Any			=							None
          lowercase   :    Optional[Any]			=							self.sp_model.serialized_model_proto()
          return state
   def __setstate__(      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							d
          lowercase   :    Dict			=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
   @property
   def       __magic_name__      (      self    ):
          return self.sp_model.get_piece_size()
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							{self.convert_ids_to_tokens(_a    ): i for i in range(self.vocab_size    )}
          vocab.update(self.added_tokens_encoder    )
          return vocab
   def       __magic_name__      (      self     ,   _a     ,   **_a    ):
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
                 lowercase   :    Optional[int]			=							SPIECE_UNDERLINE + text.replace(_a     ,   " "    )
          return super().tokenize(_a     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          if not self.legacy:
                 lowercase   :    List[Any]			=							text.startswith(_a    )
                 if is_first:
                        lowercase   :    int			=							text[1:]

          lowercase   :    Any			=							self.sp_model.encode(_a     ,   out_type=_a    )

          if not self.legacy and not is_first and not text.startswith(" "    ) and tokens[0].startswith(_a    ):
                 lowercase   :    Tuple			=							([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
          return tokens
   def       __magic_name__      (      self     ,   _a    ):
          return self.sp_model.piece_to_id(_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Optional[int]			=							self.sp_model.IdToPiece(_a    )
          return token
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    int			=							[]
          lowercase   :    Union[str, Any]			=							""
          lowercase   :    Optional[Any]			=							False
          for i, token in enumerate(_a    ):
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        if not prev_is_special and i != 0:
                               out_string += " "
                        out_string += self.sp_model.decode(_a    ) + token
                        lowercase   :    Union[str, Any]			=							True
                        lowercase   :    List[Any]			=							[]
                 else:
                        current_sub_tokens.append(_a    )
                        lowercase   :    int			=							False
          out_string += self.sp_model.decode(_a    )
          return out_string
   def       __magic_name__      (      self     ,   _a     ,   _a = None    ):
          if not os.path.isdir(_a    ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
          lowercase   :    Union[str, Any]			=							os.path.join(
              _a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

          if os.path.abspath(self.vocab_file    ) != os.path.abspath(_a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file     ,   _a    )
          elif not os.path.isfile(self.vocab_file    ):
                 with open(_a     ,   "wb"    ) as fi:
                        lowercase   :    str			=							self.sp_model.serialized_model_proto()
                        fi.write(_a    )

          return (out_vocab_file,)
   def       __magic_name__      (      self     ,   _a     ,   _a=None    ):
          lowercase   :    Union[str, Any]			=							[self.bos_token_id] if self.add_bos_token else []
          lowercase   :    str			=							[self.eos_token_id] if self.add_eos_token else []

          lowercase   :    Tuple			=							bos_token_id + token_ids_a + eos_token_id

          if token_ids_a is not None:
                 lowercase   :    int			=							output + bos_token_id + token_ids_a + eos_token_id

          return output
   def       __magic_name__      (      self     ,   _a     ,   _a = None     ,   _a = False    ):
          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a     ,   token_ids_a=_a     ,   already_has_special_tokens=_a    )

          lowercase   :    Dict			=							[1] if self.add_bos_token else []
          lowercase   :    Any			=							[1] if self.add_eos_token else []

          if token_ids_a is None:
                 return bos_token_id + ([0] * len(_a    )) + eos_token_id
          return (
              bos_token_id
              + ([0] * len(_a    ))
              + eos_token_id
              + bos_token_id
              + ([0] * len(_a    ))
              + eos_token_id
          )
   def       __magic_name__      (      self     ,   _a     ,   _a = None    ):
          lowercase   :    Dict			=							[self.bos_token_id] if self.add_bos_token else []
          lowercase   :    Union[str, Any]			=							[self.eos_token_id] if self.add_eos_token else []

          lowercase   :    List[Any]			=							[0] * len(bos_token_id + token_ids_a + eos_token_id    )

          if token_ids_a is not None:
                 output += [1] * len(bos_token_id + token_ids_a + eos_token_id    )

          return output

   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Dict			=							list(conversation.iter_texts()    )
          if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
              [not is_user for is_user, msg in dialogue[1::2]]    ):
                 raise ValueError(
                     "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"    )

          lowercase   :    List[int]			=							[]
          if len(conversation.past_user_inputs    ) > 0:
                 if not conversation.past_user_inputs[0].startswith(_a    ) or E_SYS not in conversation.past_user_inputs[0]:
                        lowercase   :    List[str]			=							(
                            B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                        )
          elif not dialogue[0][1].startswith(_a    ) or E_SYS not in dialogue[0][1]:
                 lowercase   :    int			=							(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

          dialog_tokens += sum(
              [
                  [self.bos_token_id]
                  + self.encode(
                      f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """     ,   add_special_tokens=_a    )
                  + [self.eos_token_id]
                  for prompt, answer in zip(dialogue[::2]     ,   dialogue[1::2]    )
              ]     ,   []     ,   )
          if not (dialogue[-1][0]):
                 raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""    )
          dialog_tokens += [self.bos_token_id] + self.encode(
              f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""     ,   add_special_tokens=_a    )
          return dialog_tokens




"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase   :    Tuple			=							["I", "m", "V@@", "R@@", "r", "e@@"]
          lowercase   :    List[Any]			=							dict(zip(_a     ,   range(len(_a    )    )    )    )
          lowercase   :    Union[str, Any]			=							["#version: 0.2", "a m</w>"]
          lowercase   :    List[Any]			=							{"unk_token": "<unk>"}

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]    )
          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 for token in vocab_tokens:
                        fp.write(f"""{token} {vocab_tokens[token]}\n"""    )
          with open(self.merges_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("\n".join(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


