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:
 a     :			Any					    =						None

a     :			str					    =						logging.get_logger(__name__)
a     :			Union[str, Any]					    =						{"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

a    ,   a     :			Union[str, Any]					    =						"""[INST]""", """[/INST]"""
a    ,   a     :			Optional[int]					    =						"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
a     :			List[str]					    =						"""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'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						LlamaTokenizer
    SCREAMING_SNAKE_CASE:							Optional[int]        =						'left'
    SCREAMING_SNAKE_CASE:							Tuple        =						['input_ids', 'attention_mask']




    def __init__(						self					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__=False					,	lowerCamelCase__="<unk>"					,	lowerCamelCase__="<s>"					,	lowerCamelCase__="</s>"					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	**lowerCamelCase__					,	):
       super().__init__(
           vocab_file=lowerCamelCase__					,	tokenizer_file=lowerCamelCase__					,	clean_up_tokenization_spaces=lowerCamelCase__					,	unk_token=lowerCamelCase__					,	bos_token=lowerCamelCase__					,	eos_token=lowerCamelCase__					,	**lowerCamelCase__					,	)
       lowerCAmelCase_:       Dict         =  add_bos_token
       lowerCAmelCase_:       Tuple         =  add_eos_token
       self.update_post_processor()

       lowerCAmelCase_:       List[Any]         =  vocab_file
       lowerCAmelCase_:       Tuple         =  False if not self.vocab_file else True




    def     _a						(						self   ):
       lowerCAmelCase_:       Optional[int]         =  self.bos_token
       lowerCAmelCase_:       List[str]         =  self.bos_token_id

       lowerCAmelCase_:       Union[str, Any]         =  self.eos_token
       lowerCAmelCase_:       str         =  self.eos_token_id

       lowerCAmelCase_:       int         =  F'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
       lowerCAmelCase_:       List[str]         =  F'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

       lowerCAmelCase_:       Optional[Any]         =  []
       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=lowerCamelCase__					,	pair=lowerCamelCase__					,	special_tokens=lowerCamelCase__   )




    @property
    def     _a						(						self   ):
       return self._add_eos_token




    @property
    def     _a						(						self   ):
       return self._add_bos_token




    @add_eos_token.setter
    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       int         =  value
       self.update_post_processor()




    @add_bos_token.setter
    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       int         =  value
       self.update_post_processor()




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
       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(lowerCamelCase__   ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
          return
       lowerCAmelCase_:       Any         =  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__   ):
          copyfile(self.vocab_file					,	lowerCamelCase__   )

       return (out_vocab_file,)




    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       List[str]         =  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_:       Tuple         =  []
       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]:
             lowerCAmelCase_:       int         =  (
                 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]:
          lowerCAmelCase_:       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=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
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase				)
   except (ValueError, OSError, KeyError):
      lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
      try:
         lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass
      try:
         lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor