"""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:
 lowerCAmelCase_:       Any			     =       None

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

lowerCAmelCase_:       Tuple			     =       '''[INST]''', '''[/INST]'''
lowerCAmelCase_:       Optional[Any]			     =       '''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
lowerCAmelCase_:       List[Any]			     =       '''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  a__     ( A_						):
   snake_case_				=   VOCAB_FILES_NAMES
   snake_case_				=   LlamaTokenizer
   snake_case_				=   "left"
   snake_case_				=   ["input_ids", "attention_mask"]


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase=False,					_UpperCAmelCase="<unk>",					_UpperCAmelCase="<s>",					_UpperCAmelCase="</s>",					_UpperCAmelCase=True,					_UpperCAmelCase=False,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(
            vocab_file=__lowerCamelCase,					tokenizer_file=__lowerCamelCase,					clean_up_tokenization_spaces=__lowerCamelCase,					unk_token=__lowerCamelCase,					bos_token=__lowerCamelCase,					eos_token=__lowerCamelCase,					**__lowerCamelCase,					)
        lowercase__								=				add_bos_token
        lowercase__								=				add_eos_token
        self.update_post_processor()

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.bos_token
        lowercase__								=				self.bos_token_id

        lowercase__								=				self.eos_token
        lowercase__								=				self.eos_token_id

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

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


   @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,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				value
        self.update_post_processor()


   @add_bos_token.setter
   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				value
        self.update_post_processor()


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = 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(__lowerCamelCase	):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
             return
        lowercase__								=				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 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				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...)"	)

        lowercase__								=				[]
        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]:
                  lowercase__								=				(
                      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]:
             lowercase__								=				(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





"""simple docstring"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



