


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:
   _snake_case					=					None

_snake_case					=					logging.get_logger(__name__)
_snake_case					=					{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case				,	_snake_case					=					'''[INST]''', '''[/INST]'''
_snake_case				,	_snake_case					=					'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
_snake_case					=					'''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      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Optional[Any]							=		VOCAB_FILES_NAMES
   __A			:				Optional[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						,    ):






     """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						,    )
     lowerCamelCase			: Any   =    add_bos_token
     lowerCamelCase			: Optional[Any]   =    add_eos_token
     self.update_post_processor()

     lowerCamelCase			: List[str]   =    vocab_file
     lowerCamelCase			: Dict   =    False if not self.vocab_file else True






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.bos_token
     lowerCamelCase			: List[str]   =    self.bos_token_id

     lowerCamelCase			: str   =    self.eos_token
     lowerCamelCase			: List[Any]   =    self.eos_token_id

     lowerCamelCase			: Dict   =    F"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
     lowerCamelCase			: Any   =    F"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

     lowerCamelCase			: Tuple   =    []
     if self.add_bos_token:
       special_tokens.append((bos, bos_token_id)			)
     if self.add_eos_token:
       special_tokens.append((eos, eos_token_id)			)
     lowerCamelCase			: Any   =    processors.TemplateProcessing(
         single=__A						,    pair=__A						,    special_tokens=__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     return self._add_eos_token






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     return self._add_bos_token






   @add_eos_token.setter
   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Any   =    value
     self.update_post_processor()






   @add_bos_token.setter
   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: int   =    value
     self.update_post_processor()






   def _snake_case   (		self						,    __A						,    __A = None			):






     """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
     lowerCamelCase			: int   =    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 _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: 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...)"			)

     lowerCamelCase			: Union[str, Any]   =    []
     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]:
         lowerCamelCase			: 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]:
       lowerCamelCase			: List[Any]   =    (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_tf_available, is_torch_available


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''HubertForCTC''',
       '''HubertForSequenceClassification''',
       '''HubertModel''',
       '''HubertPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFHubertForCTC''',
       '''TFHubertModel''',
       '''TFHubertPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



