





"""simple docstring"""





import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class       __lowerCamelCase	(   A__    ):
   __UpperCamelCase									=   42
   __UpperCamelCase									=   None






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Optional[int]=0.9_9_9   ,	snake_case_  :  Optional[int]="cosine"   ,	)							->	List[str]:






   """simple docstring"""


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(snake_case_  :  Optional[Any]   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case_  :  List[Any]   ):
         return math.exp(t * -1_2.0   )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

   _lowerCAmelCase					     =		[]
   for i in range(snake_case_   ):
      _lowerCAmelCase					     =		i / num_diffusion_timesteps
      _lowerCAmelCase					     =		(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case_   ) / alpha_bar_fn(snake_case_   )   ,	snake_case_   )   )
   return torch.tensor(snake_case_   ,	dtype=torch.floataa   )



class       __lowerCamelCase	(   A__   ,							A__    ):
   __UpperCamelCase									=   [e.name for e in KarrasDiffusionSchedulers]
   __UpperCamelCase									=   1







   @register_to_config
   def __init__(self       ,	lowerCamelCase = 1_000       ,	lowerCamelCase = 0.0001       ,	lowerCamelCase = 0.02       ,	lowerCamelCase = "linear"       ,	lowerCamelCase = None       ,	lowerCamelCase = False       ,	lowerCamelCase = "epsilon"       ,	lowerCamelCase = "linspace"       ,	lowerCamelCase = 0       ,	):




      '''simple docstring'''




      if trained_betas is not None:
         _lowerCAmelCase					     =		torch.tensor(_lowerCAmelCase       ,	dtype=torch.floataa				)
      elif beta_schedule == "linear":
         _lowerCAmelCase					     =		torch.linspace(_lowerCAmelCase       ,	_lowerCAmelCase       ,	_lowerCAmelCase       ,	dtype=torch.floataa				)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _lowerCAmelCase					     =		(
             torch.linspace(beta_start**0.5       ,	beta_end**0.5       ,	_lowerCAmelCase       ,	dtype=torch.floataa				) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCAmelCase					     =		betas_for_alpha_bar(_lowerCAmelCase				)
      else:
         raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

      _lowerCAmelCase					     =		1.0 - self.betas
      _lowerCAmelCase					     =		torch.cumprod(self.alphas       ,	dim=0				)

      _lowerCAmelCase					     =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
      _lowerCAmelCase					     =		np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
      _lowerCAmelCase					     =		torch.from_numpy(_lowerCAmelCase				)

      # setable values
      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		use_karras_sigmas
      self.set_timesteps(_lowerCAmelCase       ,	_lowerCAmelCase				)
      _lowerCAmelCase					     =		[]
      _lowerCAmelCase					     =		False







   @property
   def 							A__						(self				):




      '''simple docstring'''




      if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      if isinstance(_lowerCAmelCase       ,	torch.Tensor				):
         _lowerCAmelCase					     =		timestep.to(self.timesteps.device				)
      _lowerCAmelCase					     =		(self.timesteps == timestep).nonzero().item()
      _lowerCAmelCase					     =		self.sigmas[step_index]
      _lowerCAmelCase					     =		sample / ((sigma**2 + 1) ** 0.5)
      _lowerCAmelCase					     =		True
      return sample







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      def lms_derivative(lowerCamelCase				):
         _lowerCAmelCase					     =		1.0
         for k in range(_lowerCAmelCase				):
            if current_order == k:
               continue
            prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
         return prod

      _lowerCAmelCase					     =		integrate.quad(_lowerCAmelCase       ,	self.sigmas[t]       ,	self.sigmas[t + 1]       ,	epsrel=1e-4				)[0]

      return integrated_coeff







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
         _lowerCAmelCase					     =		np.linspace(0       ,	self.config.num_train_timesteps - 1       ,	_lowerCAmelCase       ,	dtype=_lowerCAmelCase				)[
             ::-1
         ].copy()
      elif self.config.timestep_spacing == "leading":
         _lowerCAmelCase					     =		self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         _lowerCAmelCase					     =		(np.arange(0       ,	_lowerCAmelCase				) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase				)
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         _lowerCAmelCase					     =		self.config.num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         _lowerCAmelCase					     =		(np.arange(self.config.num_train_timesteps       ,	0       ,	-step_ratio				)).round().copy().astype(_lowerCAmelCase				)
         timesteps -= 1
      else:
         raise ValueError(
             f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

      _lowerCAmelCase					     =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
      _lowerCAmelCase					     =		np.log(_lowerCAmelCase				)
      _lowerCAmelCase					     =		np.interp(_lowerCAmelCase       ,	np.arange(0       ,	len(_lowerCAmelCase				)				)       ,	_lowerCAmelCase				)

      if self.use_karras_sigmas:
         _lowerCAmelCase					     =		self._convert_to_karras(in_sigmas=_lowerCAmelCase				)
         _lowerCAmelCase					     =		np.array([self._sigma_to_t(_lowerCAmelCase       ,	_lowerCAmelCase				) for sigma in sigmas]				)

      _lowerCAmelCase					     =		np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)

      _lowerCAmelCase					     =		torch.from_numpy(_lowerCAmelCase				).to(device=_lowerCAmelCase				)
      if str(_lowerCAmelCase				).startswith("""mps"""				):
         # mps does not support float64
         _lowerCAmelCase					     =		torch.from_numpy(_lowerCAmelCase				).to(_lowerCAmelCase       ,	dtype=torch.floataa				)
      else:
         _lowerCAmelCase					     =		torch.from_numpy(_lowerCAmelCase				).to(device=_lowerCAmelCase				)

      _lowerCAmelCase					     =		[]







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		np.log(_lowerCAmelCase				)

      # get distribution
      _lowerCAmelCase					     =		log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      _lowerCAmelCase					     =		np.cumsum((dists >= 0)       ,	axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
      _lowerCAmelCase					     =		low_idx + 1

      _lowerCAmelCase					     =		log_sigmas[low_idx]
      _lowerCAmelCase					     =		log_sigmas[high_idx]

      # interpolate sigmas
      _lowerCAmelCase					     =		(low - log_sigma) / (low - high)
      _lowerCAmelCase					     =		np.clip(_lowerCAmelCase       ,	0       ,	1				)

      # transform interpolation to time range
      _lowerCAmelCase					     =		(1 - w) * low_idx + w * high_idx
      _lowerCAmelCase					     =		t.reshape(sigma.shape				)
      return t







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		in_sigmas[-1].item()
      _lowerCAmelCase					     =		in_sigmas[0].item()

      _lowerCAmelCase					     =		7.0  # 7.0 is the value used in the paper
      _lowerCAmelCase					     =		np.linspace(0       ,	1       ,	self.num_inference_steps				)
      _lowerCAmelCase					     =		sigma_min ** (1 / rho)
      _lowerCAmelCase					     =		sigma_max ** (1 / rho)
      _lowerCAmelCase					     =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase = 4       ,	lowerCamelCase = True       ,	):




      '''simple docstring'''




      if not self.is_scale_input_called:
         warnings.warn(
             """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
             """See `StableDiffusionPipeline` for a usage example."""				)

      if isinstance(_lowerCAmelCase       ,	torch.Tensor				):
         _lowerCAmelCase					     =		timestep.to(self.timesteps.device				)
      _lowerCAmelCase					     =		(self.timesteps == timestep).nonzero().item()
      _lowerCAmelCase					     =		self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         _lowerCAmelCase					     =		sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         _lowerCAmelCase					     =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
         _lowerCAmelCase					     =		model_output
      else:
         raise ValueError(
             f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

      # 2. Convert to an ODE derivative
      _lowerCAmelCase					     =		(sample - pred_original_sample) / sigma
      self.derivatives.append(_lowerCAmelCase				)
      if len(self.derivatives				) > order:
         self.derivatives.pop(0				)

      # 3. Compute linear multistep coefficients
      _lowerCAmelCase					     =		min(step_index + 1       ,	_lowerCAmelCase				)
      _lowerCAmelCase					     =		[self.get_lms_coefficient(_lowerCAmelCase       ,	_lowerCAmelCase       ,	_lowerCAmelCase				) for curr_order in range(_lowerCAmelCase				)]

      # 4. Compute previous sample based on the derivatives path
      _lowerCAmelCase					     =		sample + sum(
          coeff * derivative for coeff, derivative in zip(_lowerCAmelCase       ,	reversed(self.derivatives				)				)				)

      if not return_dict:
         return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase       ,	pred_original_sample=_lowerCAmelCase				)







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.sigmas.to(device=original_samples.device       ,	dtype=original_samples.dtype				)
      if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase				):
         # mps does not support float64
         _lowerCAmelCase					     =		self.timesteps.to(original_samples.device       ,	dtype=torch.floataa				)
         _lowerCAmelCase					     =		timesteps.to(original_samples.device       ,	dtype=torch.floataa				)
      else:
         _lowerCAmelCase					     =		self.timesteps.to(original_samples.device				)
         _lowerCAmelCase					     =		timesteps.to(original_samples.device				)

      _lowerCAmelCase					     =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      _lowerCAmelCase					     =		sigmas[step_indices].flatten()
      while len(sigma.shape				) < len(original_samples.shape				):
         _lowerCAmelCase					     =		sigma.unsqueeze(-1				)

      _lowerCAmelCase					     =		original_samples + noise * sigma
      return noisy_samples







   def __len__(self				):




      '''simple docstring'''




      return self.config.num_train_timesteps




"""simple docstring"""





import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE				:     Optional[int]          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{'''vocab_file''': '''tokenizer.model'''}

SCREAMING_SNAKE_CASE				:     str          =	{
    '''vocab_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model''',
    },
    '''tokenizer_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json''',
    },
}
SCREAMING_SNAKE_CASE				:     Tuple          =	{
    '''hf-internal-testing/llama-tokenizer''': 2_0_4_8,
}
SCREAMING_SNAKE_CASE				:     Tuple          =	'''▁'''

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

# fmt: off
SCREAMING_SNAKE_CASE				:     Optional[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       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   VOCAB_FILES_NAMES
   __UpperCamelCase									=   PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase									=   ['input_ids', 'attention_mask']







   def __init__(self       ,	lowerCamelCase       ,	lowerCamelCase="<unk>"       ,	lowerCamelCase="<s>"       ,	lowerCamelCase="</s>"       ,	lowerCamelCase=None       ,	lowerCamelCase = None       ,	lowerCamelCase=True       ,	lowerCamelCase=False       ,	lowerCamelCase=False       ,	lowerCamelCase=True       ,	**lowerCamelCase       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		{} if sp_model_kwargs is None else sp_model_kwargs
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else bos_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else eos_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else unk_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else pad_token
      super().__init__(
          bos_token=lowerCamelCase       ,	eos_token=lowerCamelCase       ,	unk_token=lowerCamelCase       ,	pad_token=lowerCamelCase       ,	add_bos_token=lowerCamelCase       ,	add_eos_token=lowerCamelCase       ,	sp_model_kwargs=self.sp_model_kwargs       ,	clean_up_tokenization_spaces=lowerCamelCase       ,	legacy=lowerCamelCase       ,	**lowerCamelCase       ,	)
      if legacy:
         logger.warning_once(
             f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
             """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""				)
      _lowerCAmelCase					     =		legacy
      _lowerCAmelCase					     =		vocab_file
      _lowerCAmelCase					     =		add_bos_token
      _lowerCAmelCase					     =		add_eos_token
      _lowerCAmelCase					     =		spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(lowerCamelCase				)







   def __getstate__(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.__dict__.copy()
      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		self.sp_model.serialized_model_proto()
      return state







   def __setstate__(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		d
      _lowerCAmelCase					     =		spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)







   @property
   def 							A__						(self				):




      '''simple docstring'''




      return self.sp_model.get_piece_size()







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{self.convert_ids_to_tokens(lowerCamelCase				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab







   def 							A__						(self       ,	lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




      if not self.legacy:
         _lowerCAmelCase					     =		SPIECE_UNDERLINE + text.replace(lowerCamelCase       ,	""" """				)
      return super().tokenize(lowerCamelCase       ,	**lowerCamelCase				)







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      if not self.legacy:
         _lowerCAmelCase					     =		text.startswith(lowerCamelCase				)
         if is_first:
            _lowerCAmelCase					     =		text[1:]

      _lowerCAmelCase					     =		self.sp_model.encode(lowerCamelCase       ,	out_type=lowerCamelCase				)

      if not self.legacy and not is_first and not text.startswith(""" """				) and tokens[0].startswith(lowerCamelCase				):
         _lowerCAmelCase					     =		([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
      return tokens







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      return self.sp_model.piece_to_id(lowerCamelCase				)







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.sp_model.IdToPiece(lowerCamelCase				)
      return token







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[]
      _lowerCAmelCase					     =		""""""
      _lowerCAmelCase					     =		False
      for i, token in enumerate(lowerCamelCase				):
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            if not prev_is_special and i != 0:
               out_string += " "
            out_string += self.sp_model.decode(lowerCamelCase				) + token
            _lowerCAmelCase					     =		True
            _lowerCAmelCase					     =		[]
         else:
            current_sub_tokens.append(lowerCamelCase				)
            _lowerCAmelCase					     =		False
      out_string += self.sp_model.decode(lowerCamelCase				)
      return out_string







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




      if not os.path.isdir(lowerCamelCase				):
         logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
         return
      _lowerCAmelCase					     =		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				) and os.path.isfile(self.vocab_file				):
         copyfile(self.vocab_file       ,	lowerCamelCase				)
      elif not os.path.isfile(self.vocab_file				):
         with open(lowerCamelCase       ,	"""wb"""				) as fi:
            _lowerCAmelCase					     =		self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase				)

      return (out_vocab_file,)







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase=None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[self.bos_token_id] if self.add_bos_token else []
      _lowerCAmelCase					     =		[self.eos_token_id] if self.add_eos_token else []

      _lowerCAmelCase					     =		bos_token_id + token_ids_a + eos_token_id

      if token_ids_a is not None:
         _lowerCAmelCase					     =		output + bos_token_id + token_ids_a + eos_token_id

      return output







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None       ,	lowerCamelCase = False				):




      '''simple docstring'''




      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase       ,	token_ids_a=lowerCamelCase       ,	already_has_special_tokens=lowerCamelCase				)

      _lowerCAmelCase					     =		[1] if self.add_bos_token else []
      _lowerCAmelCase					     =		[1] if self.add_eos_token else []

      if token_ids_a is None:
         return bos_token_id + ([0] * len(lowerCamelCase				)) + eos_token_id
      return (
          bos_token_id
          + ([0] * len(lowerCamelCase				))
          + eos_token_id
          + bos_token_id
          + ([0] * len(lowerCamelCase				))
          + eos_token_id
      )







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[self.bos_token_id] if self.add_bos_token else []
      _lowerCAmelCase					     =		[self.eos_token_id] if self.add_eos_token else []

      _lowerCAmelCase					     =		[0] * len(bos_token_id + token_ids_a + eos_token_id				)

      if token_ids_a is not None:
         output += [1] * len(bos_token_id + token_ids_a + eos_token_id				)

      return output







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		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					     =		[]
      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					     =		(
                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					     =		(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