

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_						:     List[Any]			  =				None

A_						:     List[str]			  =				logging.get_logger(__name__)
A_						:     str			  =				{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

A_  ,  A_						:     Optional[int]			  =				'[INST]', '[/INST]'
A_  ,  A_						:     List[Any]			  =				'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
A_						:     Optional[int]			  =				'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_				( _a  ):


    '''simple docstring'''






    a__       							=    VOCAB_FILES_NAMES
    a__       							=    LlamaTokenizer
    a__       							=    "left"
    a__       							=    ["input_ids", "attention_mask"]


    def __init__(self       , lowercase__=None       , lowercase__=None       , lowercase__=False       , lowercase__="<unk>"       , lowercase__="<s>"       , lowercase__="</s>"       , lowercase__=True       , lowercase__=False       , **lowercase__       , )       -> str:
        super().__init__(
            vocab_file=lowercase__       , tokenizer_file=lowercase__       , clean_up_tokenization_spaces=lowercase__       , unk_token=lowercase__       , bos_token=lowercase__       , eos_token=lowercase__       , **lowercase__       , )
        __UpperCAmelCase			   =     add_bos_token
        __UpperCAmelCase			   =     add_eos_token
        self.update_post_processor()

        __UpperCAmelCase			   =     vocab_file
        __UpperCAmelCase			   =     False if not self.vocab_file else True


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     self.bos_token
        __UpperCAmelCase			   =     self.bos_token_id

        __UpperCAmelCase			   =     self.eos_token
        __UpperCAmelCase			   =     self.eos_token_id

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

        __UpperCAmelCase			   =     []
        if self.add_bos_token:
            special_tokens.append((bos, bos_token_id)					)
        if self.add_eos_token:
            special_tokens.append((eos, eos_token_id)					)
        __UpperCAmelCase			   =     processors.TemplateProcessing(
            single=lowercase__       , pair=lowercase__       , special_tokens=lowercase__					)


    @property
    def        lowerCAmelCase_  (self					)       -> str:
        return self._add_eos_token


    @property
    def        lowerCAmelCase_  (self					)       -> int:
        return self._add_bos_token


    @add_eos_token.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> int:
        __UpperCAmelCase			   =     value
        self.update_post_processor()


    @add_bos_token.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> Dict:
        __UpperCAmelCase			   =     value
        self.update_post_processor()


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = 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(lowercase__					):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
            return
        __UpperCAmelCase			   =     os.path.join(
            lowercase__       , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

        return (out_vocab_file,)






    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[Any]:
        __UpperCAmelCase			   =     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...)'''					)

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







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    {
        "sld_warmup_steps": 15,
        "sld_guidance_scale": 20,
        "sld_threshold": 0.0,
        "sld_momentum_scale": 0.0,
        "sld_mom_beta": 0.0,
    }
    a__       							=    {
        "sld_warmup_steps": 10,
        "sld_guidance_scale": 10_00,
        "sld_threshold": 0.01,
        "sld_momentum_scale": 0.3,
        "sld_mom_beta": 0.4,
    }
    a__       							=    {
        "sld_warmup_steps": 7,
        "sld_guidance_scale": 20_00,
        "sld_threshold": 0.0_25,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }
    a__       							=    {
        "sld_warmup_steps": 0,
        "sld_guidance_scale": 50_00,
        "sld_threshold": 1.0,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }






@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    42
    a__       							=    42
    a__       							=    42
    a__       							=    42


if is_transformers_available() and is_torch_available():
       from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
       from .safety_checker import SafeStableDiffusionSafetyChecker




