



"""simple docstring"""







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin





@dataclass
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       torch.FloatTensor







class   UpperCamelCase	(     snake_case					,   snake_case       ):




	"""simple docstring"""






	@register_to_config
	def __init__(     self				,UpperCAmelCase_ = 16				,UpperCAmelCase_ = 88				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = 1				,UpperCAmelCase_ = 0.0				,UpperCAmelCase_ = 32				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = "geglu"				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False				,UpperCAmelCase_ = False				,UpperCAmelCase_ = False				,UpperCAmelCase_ = "layer_norm"				,UpperCAmelCase_ = True				,):
			super().__init__()
			_lowercase				: Tuple     =   use_linear_projection
			_lowercase				: Union[str, Any]     =   num_attention_heads
			_lowercase				: str     =   attention_head_dim
			_lowercase				: Optional[Any]     =   num_attention_heads * attention_head_dim

			# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
			# Define whether input is continuous or discrete depending on configuration
			_lowercase				: List[str]     =   (in_channels is not None) and (patch_size is None)
			_lowercase				: Any     =   num_vector_embeds is not None
			_lowercase				: Optional[Any]     =   in_channels is not None and patch_size is not None

			if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
					_lowercase				: Any     =   (
					    f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
					    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
					    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
					    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
					    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
					)
					deprecate("""norm_type!=num_embeds_ada_norm"""				,"""1.0.0"""				,UpperCAmelCase_				,standard_warn=UpperCAmelCase_   )
					_lowercase				: Optional[int]     =   """ada_norm"""

			if self.is_input_continuous and self.is_input_vectorized:
					raise ValueError(
					    f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
					    """ sure that either `in_channels` or `num_vector_embeds` is None."""   )
			elif self.is_input_vectorized and self.is_input_patches:
					raise ValueError(
					    f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
					    """ sure that either `num_vector_embeds` or `num_patches` is None."""   )
			elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
					raise ValueError(
					    f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
					    f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""   )

			# 2. Define input layers
			if self.is_input_continuous:
					_lowercase				: int     =   in_channels

					_lowercase				: int     =   torch.nn.GroupNorm(num_groups=UpperCAmelCase_				,num_channels=UpperCAmelCase_				,eps=1E-6				,affine=UpperCAmelCase_   )
					if use_linear_projection:
							_lowercase				: Tuple     =   nn.Linear(UpperCAmelCase_				,UpperCAmelCase_   )
					else:
							_lowercase				: Tuple     =   nn.Convad(UpperCAmelCase_				,UpperCAmelCase_				,kernel_size=1				,stride=1				,padding=0   )
			elif self.is_input_vectorized:
					assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
					assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

					_lowercase				: int     =   sample_size
					_lowercase				: Dict     =   sample_size
					_lowercase				: List[str]     =   num_vector_embeds
					_lowercase				: str     =   self.height * self.width

					_lowercase				: Any     =   ImagePositionalEmbeddings(
					    num_embed=UpperCAmelCase_				,embed_dim=UpperCAmelCase_				,height=self.height				,width=self.width   )
			elif self.is_input_patches:
					assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

					_lowercase				: List[str]     =   sample_size
					_lowercase				: str     =   sample_size

					_lowercase				: Tuple     =   patch_size
					_lowercase				: List[str]     =   PatchEmbed(
					    height=UpperCAmelCase_				,width=UpperCAmelCase_				,patch_size=UpperCAmelCase_				,in_channels=UpperCAmelCase_				,embed_dim=UpperCAmelCase_				,)

			# 3. Define transformers blocks
			_lowercase				: Tuple     =   nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,dropout=UpperCAmelCase_				,cross_attention_dim=UpperCAmelCase_				,activation_fn=UpperCAmelCase_				,num_embeds_ada_norm=UpperCAmelCase_				,attention_bias=UpperCAmelCase_				,only_cross_attention=UpperCAmelCase_				,upcast_attention=UpperCAmelCase_				,norm_type=UpperCAmelCase_				,norm_elementwise_affine=UpperCAmelCase_				,)
			        for d in range(UpperCAmelCase_   )
			    ]   )

			# 4. Define output layers
			_lowercase				: Optional[Any]     =   in_channels if out_channels is None else out_channels
			if self.is_input_continuous:
					# TODO: should use out_channels for continuous projections
					if use_linear_projection:
							_lowercase				: Optional[Any]     =   nn.Linear(UpperCAmelCase_				,UpperCAmelCase_   )
					else:
							_lowercase				: List[str]     =   nn.Convad(UpperCAmelCase_				,UpperCAmelCase_				,kernel_size=1				,stride=1				,padding=0   )
			elif self.is_input_vectorized:
					_lowercase				: Tuple     =   nn.LayerNorm(UpperCAmelCase_   )
					_lowercase				: Optional[Any]     =   nn.Linear(UpperCAmelCase_				,self.num_vector_embeds - 1   )
			elif self.is_input_patches:
					_lowercase				: str     =   nn.LayerNorm(UpperCAmelCase_				,elementwise_affine=UpperCAmelCase_				,eps=1E-6   )
					_lowercase				: str     =   nn.Linear(UpperCAmelCase_				,2 * inner_dim   )
					_lowercase				: str     =   nn.Linear(UpperCAmelCase_				,patch_size * patch_size * self.out_channels   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = True				,):
			# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
			#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
			#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
			# expects mask of shape:
			#   [batch, key_tokens]
			# adds singleton query_tokens dimension:
			#   [batch,                    1, key_tokens]
			# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
			#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
			#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
			if attention_mask is not None and attention_mask.ndim == 2:
					# assume that mask is expressed as:
					#   (1 = keep,      0 = discard)
					# convert mask into a bias that can be added to attention scores:
					#       (keep = +0,     discard = -10000.0)
					_lowercase				: Dict     =   (1 - attention_mask.to(hidden_states.dtype   )) * -10000.0
					_lowercase				: Any     =   attention_mask.unsqueeze(1   )

			# convert encoder_attention_mask to a bias the same way we do for attention_mask
			if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
					_lowercase				: List[str]     =   (1 - encoder_attention_mask.to(hidden_states.dtype   )) * -10000.0
					_lowercase				: List[Any]     =   encoder_attention_mask.unsqueeze(1   )

			# 1. Input
			if self.is_input_continuous:
					_lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase				: Optional[int]     =   hidden_states.shape
					_lowercase				: str     =   hidden_states

					_lowercase				: Tuple     =   self.norm(UpperCAmelCase_   )
					if not self.use_linear_projection:
							_lowercase				: Tuple     =   self.proj_in(UpperCAmelCase_   )
							_lowercase				: Any     =   hidden_states.shape[1]
							_lowercase				: Tuple     =   hidden_states.permute(0				,2				,3				,1   ).reshape(UpperCAmelCase_				,height * width				,UpperCAmelCase_   )
					else:
							_lowercase				: List[str]     =   hidden_states.shape[1]
							_lowercase				: Optional[int]     =   hidden_states.permute(0				,2				,3				,1   ).reshape(UpperCAmelCase_				,height * width				,UpperCAmelCase_   )
							_lowercase				: Union[str, Any]     =   self.proj_in(UpperCAmelCase_   )
			elif self.is_input_vectorized:
					_lowercase				: Dict     =   self.latent_image_embedding(UpperCAmelCase_   )
			elif self.is_input_patches:
					_lowercase				: int     =   self.pos_embed(UpperCAmelCase_   )

			# 2. Blocks
			for block in self.transformer_blocks:
					_lowercase				: Any     =   block(
					    UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,encoder_hidden_states=UpperCAmelCase_				,encoder_attention_mask=UpperCAmelCase_				,timestep=UpperCAmelCase_				,cross_attention_kwargs=UpperCAmelCase_				,class_labels=UpperCAmelCase_				,)

			# 3. Output
			if self.is_input_continuous:
					if not self.use_linear_projection:
							_lowercase				: Optional[Any]     =   hidden_states.reshape(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ).permute(0				,3				,1				,2   ).contiguous()
							_lowercase				: Optional[Any]     =   self.proj_out(UpperCAmelCase_   )
					else:
							_lowercase				: List[str]     =   self.proj_out(UpperCAmelCase_   )
							_lowercase				: Optional[int]     =   hidden_states.reshape(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ).permute(0				,3				,1				,2   ).contiguous()

					_lowercase				: List[str]     =   hidden_states + residual
			elif self.is_input_vectorized:
					_lowercase				: int     =   self.norm_out(UpperCAmelCase_   )
					_lowercase				: List[str]     =   self.out(UpperCAmelCase_   )
					# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
					_lowercase				: List[Any]     =   logits.permute(0				,2				,1   )

					# log(p(x_0))
					_lowercase				: List[Any]     =   F.log_softmax(logits.double()				,dim=1   ).float()
			elif self.is_input_patches:
					# TODO: cleanup!
					_lowercase				: List[Any]     =   self.transformer_blocks[0].norma.emb(
					    UpperCAmelCase_				,UpperCAmelCase_				,hidden_dtype=hidden_states.dtype   )
					_lowercase     ,  _lowercase				: Dict     =   self.proj_out_a(F.silu(UpperCAmelCase_   )   ).chunk(2				,dim=1   )
					_lowercase				: int     =   self.norm_out(UpperCAmelCase_   ) * (1 + scale[:, None]) + shift[:, None]
					_lowercase				: int     =   self.proj_out_a(UpperCAmelCase_   )

					# unpatchify
					_lowercase				: Optional[Any]     =   int(hidden_states.shape[1] ** 0.5   )
					_lowercase				: Union[str, Any]     =   hidden_states.reshape(
					    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)   )
					_lowercase				: List[Any]     =   torch.einsum("""nhwpqc->nchpwq"""				,UpperCAmelCase_   )
					_lowercase				: Optional[Any]     =   hidden_states.reshape(
					    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)   )

			if not return_dict:
					return (output,)

			return TransformeraDModelOutput(sample=UpperCAmelCase_   )








"""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

UpperCAmelCase:  str			     =				logging.get_logger(__name__)

UpperCAmelCase:  Tuple			     =				{"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase:  int			     =				{
    """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""",
    },
}
UpperCAmelCase:  Union[str, Any]			     =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase:  List[Any]			     =				"""▁"""

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

# fmt: off
UpperCAmelCase:  Optional[int]			     =				"""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	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       int						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Dict						       =  ["input_ids", "attention_mask"]






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_=None				,UpperCAmelCase_ = None				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,**UpperCAmelCase_				,):
			_lowercase				: List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs
			_lowercase				: List[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else bos_token
			_lowercase				: Union[str, Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else eos_token
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else unk_token
			_lowercase				: Optional[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_				,eos_token=UpperCAmelCase_				,unk_token=UpperCAmelCase_				,pad_token=UpperCAmelCase_				,add_bos_token=UpperCAmelCase_				,add_eos_token=UpperCAmelCase_				,sp_model_kwargs=self.sp_model_kwargs				,clean_up_tokenization_spaces=UpperCAmelCase_				,legacy=UpperCAmelCase_				,**UpperCAmelCase_				,)
			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"""   )
			_lowercase				: Tuple     =   legacy
			_lowercase				: Optional[int]     =   vocab_file
			_lowercase				: Any     =   add_bos_token
			_lowercase				: Union[str, Any]     =   add_eos_token
			_lowercase				: Union[str, Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






	def __getstate__(     self   ):
			_lowercase				: Union[str, Any]     =   self.__dict__.copy()
			_lowercase				: Optional[int]     =   None
			_lowercase				: Optional[Any]     =   self.sp_model.serialized_model_proto()
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   d
			_lowercase				: List[Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.sp_model.get_piece_size()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					_lowercase				: str     =   SPIECE_UNDERLINE + text.replace(UpperCAmelCase_				,""" """   )
			return super().tokenize(UpperCAmelCase_				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if not self.legacy:
					_lowercase				: Any     =   text.startswith(UpperCAmelCase_   )
					if is_first:
							_lowercase				: str     =   text[1:]

			_lowercase				: Dict     =   self.sp_model.encode(UpperCAmelCase_				,out_type=UpperCAmelCase_   )

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.piece_to_id(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Dict     =   """"""
			_lowercase				: List[Any]     =   False
			for i, token in enumerate(UpperCAmelCase_   ):
					# 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(UpperCAmelCase_   ) + token
							_lowercase				: Optional[Any]     =   True
							_lowercase				: str     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: Optional[Any]     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			_lowercase				: str     =   os.path.join(
			    UpperCAmelCase_				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

			if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file				,UpperCAmelCase_   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(UpperCAmelCase_				,"""wb"""   ) as fi:
							_lowercase				: List[Any]     =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Any     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: List[str]     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Union[str, Any]     =   bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					_lowercase				: int     =   output + bos_token_id + token_ids_a + eos_token_id

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False   ):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCAmelCase_				,token_ids_a=UpperCAmelCase_				,already_has_special_tokens=UpperCAmelCase_   )

			_lowercase				: Tuple     =   [1] if self.add_bos_token else []
			_lowercase				: Dict     =   [1] if self.add_eos_token else []

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Union[str, Any]     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: str     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Any     =   [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    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   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				: List[int]     =   []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							_lowercase				: Optional[int]     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					_lowercase				: List[str]     =   (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=UpperCAmelCase_   )
			        + [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=UpperCAmelCase_   )
			return dialog_tokens



