'''simple docstring'''






import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
   from .tokenization_llama import LlamaTokenizer
else:
   _UpperCAmelCase						:    List[Any]     					=					None

_UpperCAmelCase						:    Optional[int]     					=					logging.get_logger(__name__)
_UpperCAmelCase						:    int     					=					{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCAmelCase					,     _UpperCAmelCase						:    Any     					=					'''[INST]''', '''[/INST]'''
_UpperCAmelCase					,     _UpperCAmelCase						:    int     					=					'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
_UpperCAmelCase						:    Optional[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 lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      VOCAB_FILES_NAMES
   __lowerCAmelCase    						=      LlamaTokenizer
   __lowerCAmelCase    						=      "left"
   __lowerCAmelCase    						=      ["input_ids", "attention_mask"]
   def __init__(			self			: Tuple,   UpperCamelCase__			: List[str]=None,   UpperCamelCase__			: int=None,   UpperCamelCase__			: int=False,   UpperCamelCase__			: List[Any]="<unk>",   UpperCamelCase__			: List[Any]="<s>",   UpperCamelCase__			: Dict="</s>",   UpperCamelCase__			: List[Any]=True,   UpperCamelCase__			: Optional[int]=False,   **UpperCamelCase__			: Union[str, Any],   )     -> Dict:
        super().__init__(
            vocab_file=UpperCamelCase__,   tokenizer_file=UpperCamelCase__,   clean_up_tokenization_spaces=UpperCamelCase__,   unk_token=UpperCamelCase__,   bos_token=UpperCamelCase__,   eos_token=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     add_bos_token
        _A								=     add_eos_token
        self.update_post_processor()

        _A								=     vocab_file
        _A								=     False if not self.vocab_file else True
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Dict:
        _A								=     self.bos_token
        _A								=     self.bos_token_id

        _A								=     self.eos_token
        _A								=     self.eos_token_id

        _A								=     f'{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'
        _A								=     f'{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'

        _A								=     []
        if self.add_bos_token:
             special_tokens.append((bos, bos_token_id)    )
        if self.add_eos_token:
             special_tokens.append((eos, eos_token_id)    )
        _A								=     processors.TemplateProcessing(
            single=UpperCamelCase__,   pair=UpperCamelCase__,   special_tokens=UpperCamelCase__    )
   @property
   def   __UpperCAmelCase      (			self			: int    )     -> Tuple:
        return self._add_eos_token
   @property
   def   __UpperCAmelCase      (			self			: int    )     -> List[Any]:
        return self._add_bos_token
   @add_eos_token.setter
   def   __UpperCAmelCase      (			self			: int,   UpperCamelCase__			: List[Any]    )     -> Any:
        _A								=     value
        self.update_post_processor()
   @add_bos_token.setter
   def   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: Tuple    )     -> str:
        _A								=     value
        self.update_post_processor()
   def   __UpperCAmelCase      (			self			: Any,   UpperCamelCase__			: str,   UpperCamelCase__			: Optional[str] = None    )     -> Tuple[str]:
        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'    )

        if not os.path.isdir(UpperCamelCase__    ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
             return
        _A								=     os.path.join(
            UpperCamelCase__,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

        return (out_vocab_file,)

   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: "Conversation"    )     -> 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(UpperCamelCase__    ) 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(UpperCamelCase__    ) 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=UpperCamelCase__    )
                + [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=UpperCamelCase__    )
        return dialog_tokens




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase						:    Dict     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



