


'''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

lowerCAmelCase  =				logging.get_logger(__name__)

lowerCAmelCase  =				{"""vocab_file""": """tokenizer.model"""}

lowerCAmelCase  =				{
    """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""",
    },
}
lowerCAmelCase  =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
lowerCAmelCase  =				"""▁"""

lowerCAmelCase  =				"""[INST]""", """[/INST]"""
lowerCAmelCase  =				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
lowerCAmelCase  =				"""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 					lowerCAmelCase_     (    UpperCamelCase__  ):
       __UpperCAmelCase				=VOCAB_FILES_NAMES
       __UpperCAmelCase				=PRETRAINED_VOCAB_FILES_MAP
       __UpperCAmelCase				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCAmelCase				=["""input_ids""", """attention_mask"""]






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase="<unk>" ,    _UpperCamelCase="<s>" ,    _UpperCamelCase="</s>" ,    _UpperCamelCase=None ,    _UpperCamelCase = None ,    _UpperCamelCase=True ,    _UpperCamelCase=False ,    _UpperCamelCase=False ,    _UpperCamelCase=True ,    **_UpperCamelCase ,    )->			Optional[Any]:
            _A				=       {} if sp_model_kwargs is None else sp_model_kwargs
            _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 unk_token
            _A				=       AddedToken(lowerCAmelCase__ ,    lstrip=lowerCAmelCase__ ,    rstrip=lowerCAmelCase__      ) if isinstance(lowerCAmelCase__ ,    lowerCAmelCase__      ) else pad_token
            super().__init__(
                bos_token=lowerCAmelCase__ ,    eos_token=lowerCAmelCase__ ,    unk_token=lowerCAmelCase__ ,    pad_token=lowerCAmelCase__ ,    add_bos_token=lowerCAmelCase__ ,    add_eos_token=lowerCAmelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    clean_up_tokenization_spaces=lowerCAmelCase__ ,    legacy=lowerCAmelCase__ ,    **lowerCAmelCase__ ,    )
            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'      )
            _A				=       legacy
            _A				=       vocab_file
            _A				=       add_bos_token
            _A				=       add_eos_token
            _A				=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
            self.sp_model.Load(lowerCAmelCase__      )






       def __getstate__(   self      )->			int:
            _A				=       self.__dict__.copy()
            _A				=       None
            _A				=       self.sp_model.serialized_model_proto()
            return state






       def __setstate__(   self ,    _UpperCamelCase      )->			Union[str, Any]:
            _A				=       d
            _A				=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






       @property
       def      UpperCamelCase	(   self      )->			str:
            return self.sp_model.get_piece_size()






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       {self.convert_ids_to_tokens(lowerCAmelCase__      ): i for i in range(self.vocab_size      )}
            vocab.update(self.added_tokens_encoder      )
            return vocab






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    **_UpperCamelCase      )->			List[str]:




            if not self.legacy:
                 _A				=       SPIECE_UNDERLINE + text.replace(lowerCAmelCase__ ,    ' '      )
            return super().tokenize(lowerCAmelCase__ ,    **lowerCAmelCase__      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[Any]:




            if not self.legacy:
                 _A				=       text.startswith(lowerCAmelCase__      )
                 if is_first:
                      _A				=       text[1:]

            _A				=       self.sp_model.encode(lowerCAmelCase__ ,    out_type=lowerCAmelCase__      )

            if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(lowerCAmelCase__      ):
                 _A				=       ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
            return tokens






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Optional[Any]:
            return self.sp_model.piece_to_id(lowerCAmelCase__      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Optional[Any]:
            _A				=       self.sp_model.IdToPiece(lowerCAmelCase__      )
            return token






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Union[str, Any]:
            _A				=       []
            _A				=       ""
            _A				=       False
            for i, token in enumerate(lowerCAmelCase__      ):
                 # 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(lowerCAmelCase__      ) + token
                      _A				=       True
                      _A				=       []
                 else:
                      current_sub_tokens.append(lowerCAmelCase__      )
                      _A				=       False
            out_string += self.sp_model.decode(lowerCAmelCase__      )
            return out_string






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase = None      )->			Tuple[str]:




            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']      )

            if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCAmelCase__      ) and os.path.isfile(self.vocab_file      ):
                 copyfile(self.vocab_file ,    lowerCAmelCase__      )
            elif not os.path.isfile(self.vocab_file      ):
                 with open(lowerCAmelCase__ ,    'wb'      ) as fi:
                      _A				=       self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__      )

            return (out_vocab_file,)






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			str:
            _A				=       [self.bos_token_id] if self.add_bos_token else []
            _A				=       [self.eos_token_id] if self.add_eos_token else []

            _A				=       bos_token_id + token_ids_a + eos_token_id

            if token_ids_a is not None:
                 _A				=       output + bos_token_id + token_ids_a + eos_token_id

            return output






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase = None ,    _UpperCamelCase = False      )->			List[int]:




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

            _A				=       [1] if self.add_bos_token else []
            _A				=       [1] if self.add_eos_token else []

            if token_ids_a is None:
                 return bos_token_id + ([0] * len(lowerCAmelCase__      )) + eos_token_id
            return (
                bos_token_id
                + ([0] * len(lowerCAmelCase__      ))
                + eos_token_id
                + bos_token_id
                + ([0] * len(lowerCAmelCase__      ))
                + eos_token_id
            )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase = None      )->			List[int]:
            _A				=       [self.bos_token_id] if self.add_bos_token else []
            _A				=       [self.eos_token_id] if self.add_eos_token else []

            _A				=       [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      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[int]:
            _A				=       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...)'      )

            _A				=       []
            if len(conversation.past_user_inputs      ) > 0:
                 if not conversation.past_user_inputs[0].startswith(lowerCAmelCase__      ) or E_SYS not in conversation.past_user_inputs[0]:
                      _A				=       (
                          B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                      )
            elif not dialogue[0][1].startswith(lowerCAmelCase__      ) or E_SYS not in dialogue[0][1]:
                 _A				=       (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=lowerCAmelCase__      )
                    + [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=lowerCAmelCase__      )
            return dialog_tokens





'''simple docstring'''
from __future__ import annotations

def 				lowerCamelCase_   (   __UpperCamelCase				:					list[int | float]		)				->  int | float:







     """simple docstring"""
     if len(__UpperCamelCase		) == 0:
          raise ValueError('find_max() arg is an empty sequence'		)
     _A				=       nums[0]
     for x in nums:
          if x > max_num:
               _A				=       x
     return max_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)





