




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:
   lowercase__   :    List[str] 					=      None

lowercase__   :    Optional[Any] 					=      logging.get_logger(__name__)
lowercase__   :    List[Any] 					=      {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

lowercase__					,       lowercase__   :    Optional[Any] 					=      "[INST]", "[/INST]"
lowercase__					,       lowercase__   :    Any 					=      "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowercase__   :    Dict 					=      "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."




class     a__ (      UpperCamelCase__   ):
     a	:    List[str]	      =			VOCAB_FILES_NAMES
     a	:    Any	      =			LlamaTokenizer
     a	:    Any	      =			"""left"""
     a	:    Union[str, Any]	      =			["""input_ids""", """attention_mask"""]







     def __init__(			self						,	A=None						,	A=None						,	A=False						,	A="<unk>"						,	A="<s>"						,	A="</s>"						,	A=True						,	A=False						,	**A						,	)						->					Optional[int]:



          '''simple docstring'''



          super().__init__(
              vocab_file=A						,	tokenizer_file=A						,	clean_up_tokenization_spaces=A						,	unk_token=A						,	bos_token=A						,	eos_token=A						,	**A						,	)
          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     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          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=A						,	pair=A						,	special_tokens=A				)







     @property
     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          return self._add_eos_token







     @property
     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          return self._add_bos_token







     @add_eos_token.setter
     def     lowerCAmelCase_       (			self						,	A				)						->					List[Any]:



          '''simple docstring'''



          a											=   value
          self.update_post_processor()







     @add_bos_token.setter
     def     lowerCAmelCase_       (			self						,	A				)						->					List[Any]:



          '''simple docstring'''



          a											=   value
          self.update_post_processor()







     def     lowerCAmelCase_       (			self						,	A						,	A = None				)						->					Tuple[str]:



          '''simple docstring'''



          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(A				):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
               return
          a											=   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				):
               copyfile(self.vocab_file						,	A				)

          return (out_vocab_file,)







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[int]:



          '''simple docstring'''



          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(A				) 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(A				) 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=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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

