code: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
   from transformers.models.albert.modeling_tf_albert import (
       TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFAlbertForMaskedLM,
       TFAlbertForMultipleChoice,
       TFAlbertForPreTraining,
       TFAlbertForQuestionAnswering,
       TFAlbertForSequenceClassification,
       TFAlbertForTokenClassification,
       TFAlbertModel,
   )
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					str=13     ,		UpperCamelCase					:					List[Any]=7     ,		UpperCamelCase					:					Optional[int]=True     ,		UpperCamelCase					:					Dict=True     ,		UpperCamelCase					:					List[Any]=True     ,		UpperCamelCase					:					Dict=True     ,		UpperCamelCase					:					Optional[int]=99     ,		UpperCamelCase					:					List[str]=16     ,		UpperCamelCase					:					int=32     ,		UpperCamelCase					:					Tuple=2     ,		UpperCamelCase					:					List[str]=4     ,		UpperCamelCase					:					Tuple=37     ,		UpperCamelCase					:					Optional[Any]="gelu"     ,		UpperCamelCase					:					Optional[Any]=0.1     ,		UpperCamelCase					:					Optional[Any]=0.1     ,		UpperCamelCase					:					Optional[Any]=512     ,		UpperCamelCase					:					Tuple=16     ,		UpperCamelCase					:					Tuple=2     ,		UpperCamelCase					:					Union[str, Any]=0.02     ,		UpperCamelCase					:					Optional[Any]=3     ,		UpperCamelCase					:					Any=4     ,		UpperCamelCase					:					List[str]=None     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				parent
               lowercase__       =				13
               lowercase__       =				7
               lowercase__       =				True
               lowercase__       =				True
               lowercase__       =				True
               lowercase__       =				True
               lowercase__       =				99
               lowercase__       =				16
               lowercase__       =				32
               lowercase__       =				2
               lowercase__       =				4
               lowercase__       =				37
               lowercase__       =				'''gelu'''
               lowercase__       =				0.1
               lowercase__       =				0.1
               lowercase__       =				512
               lowercase__       =				16
               lowercase__       =				2
               lowercase__       =				0.02
               lowercase__       =				3
               lowercase__       =				4
               lowercase__       =				None
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size				)
  
               lowercase__       =				None
               if self.use_input_mask:
                      lowercase__       =				random_attention_mask([self.batch_size, self.seq_length]				)
  
               lowercase__       =				None
               if self.use_token_type_ids:
                      lowercase__       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size				)
  
               lowercase__       =				None
               lowercase__       =				None
               lowercase__       =				None
               if self.use_labels:
                      lowercase__       =				ids_tensor([self.batch_size]     ,		self.type_sequence_label_size				)
                      lowercase__       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels				)
                      lowercase__       =				ids_tensor([self.batch_size]     ,		self.num_choices				)
  
               lowercase__       =				AlbertConfig(
                   vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		embedding_size=self.embedding_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		type_vocab_size=self.type_vocab_size     ,		initializer_range=self.initializer_range     ,		)
  
               return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				TFAlbertModel(config=UpperCamelCase				)
               # inputs = {'input_ids': input_ids,
               #           'attention_mask': input_mask,
               #           'token_type_ids': token_type_ids}
               # sequence_output, pooled_output = model(**inputs)
               lowercase__       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               lowercase__       =				model(UpperCamelCase				)
  
               lowercase__       =				[input_ids, input_mask]
               lowercase__       =				model(UpperCamelCase				)
  
               lowercase__       =				model(UpperCamelCase				)
  
               self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)				)
               self.parent.assertEqual(result.pooler_output.shape     ,		(self.batch_size, self.hidden_size)				)
  
  
  
        def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.num_labels
               lowercase__       =				TFAlbertForPreTraining(config=UpperCamelCase				)
               lowercase__       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertEqual(result.prediction_logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)				)
               self.parent.assertEqual(result.sop_logits.shape     ,		(self.batch_size, self.num_labels)				)
  
  
  
        def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				TFAlbertForMaskedLM(config=UpperCamelCase				)
               lowercase__       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.num_labels
               lowercase__       =				TFAlbertForSequenceClassification(config=UpperCamelCase				)
               lowercase__       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)				)
  
  
  
        def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				TFAlbertForQuestionAnswering(config=UpperCamelCase				)
               lowercase__       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertEqual(result.start_logits.shape     ,		(self.batch_size, self.seq_length)				)
               self.parent.assertEqual(result.end_logits.shape     ,		(self.batch_size, self.seq_length)				)
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.num_choices
               lowercase__       =				TFAlbertForMultipleChoice(config=UpperCamelCase				)
               lowercase__       =				tf.tile(tf.expand_dims(UpperCamelCase     ,		1				)     ,		(1, self.num_choices, 1)				)
               lowercase__       =				tf.tile(tf.expand_dims(UpperCamelCase     ,		1				)     ,		(1, self.num_choices, 1)				)
               lowercase__       =				tf.tile(tf.expand_dims(UpperCamelCase     ,		1				)     ,		(1, self.num_choices, 1)				)
               lowercase__       =				{
                   '''input_ids''': multiple_choice_inputs_ids,
                   '''attention_mask''': multiple_choice_input_mask,
                   '''token_type_ids''': multiple_choice_token_type_ids,
               }
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertListEqual(list(result['''logits'''].shape				)     ,		[self.batch_size, self.num_choices]				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.num_labels
               lowercase__       =				TFAlbertForTokenClassification(config=UpperCamelCase				)
               lowercase__       =				{
                   '''input_ids''': input_ids,
                   '''attention_mask''': input_mask,
                   '''token_type_ids''': token_type_ids,
               }
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertListEqual(list(result['''logits'''].shape				)     ,		[self.batch_size, self.seq_length, self.num_labels]				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.prepare_config_and_inputs()
               (
                   (
                   lowercase__
               )    ,(
                   lowercase__
               )    ,(
                   lowercase__
               )    ,(
                   lowercase__
               )    ,(
                   lowercase__
               )    ,(
                   lowercase__
               )    ,(
                   lowercase__
               )    ,
               )       =				config_and_inputs
               lowercase__       =				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
               return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class 						__lowerCAmelCase		(lowercase_						,  lowercase_						,  unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Dict						      =							(
            (
                TFAlbertModel,
                TFAlbertForPreTraining,
                TFAlbertForMaskedLM,
                TFAlbertForSequenceClassification,
                TFAlbertForQuestionAnswering,
                TFAlbertForTokenClassification,
                TFAlbertForMultipleChoice,
            )
            if is_tf_available()
            else ()
        )
        lowerCAmelCase__      :  Union[str, Any]						      =							(
            {
                """feature-extraction""": TFAlbertModel,
                """fill-mask""": TFAlbertForMaskedLM,
                """question-answering""": TFAlbertForQuestionAnswering,
                """text-classification""": TFAlbertForSequenceClassification,
                """token-classification""": TFAlbertForTokenClassification,
                """zero-shot""": TFAlbertForSequenceClassification,
            }
            if is_tf_available()
            else {}
        )
        lowerCAmelCase__      :  str						      =							False
        lowerCAmelCase__      :  Optional[Any]						      =							False
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Union[str, Any]=False				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				super()._prepare_for_class(UpperCamelCase     ,		UpperCamelCase     ,		return_labels=UpperCamelCase				)
  
               if return_labels:
                      if model_class in get_values(UpperCamelCase				):
                             lowercase__       =				tf.zeros(self.model_tester.batch_size     ,		dtype=tf.intaa				)
  
               return inputs_dict
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				TFAlbertModelTester(self				)
               lowercase__       =				ConfigTester(self     ,		config_class=UpperCamelCase     ,		hidden_size=37				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.config_tester.run_common_tests()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_model(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_for_pretraining(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_for_masked_lm(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_for_multiple_choice(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_for_sequence_classification(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_albert_for_question_answering(*UpperCamelCase				)
  
  
  
  
  
  
        @slow
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                      lowercase__       =				TFAlbertModel.from_pretrained(UpperCamelCase				)
                      self.assertIsNotNone(UpperCamelCase				)
  
  
  
  
  
  
  @require_tf
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @slow
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				TFAlbertForPreTraining.from_pretrained('''albert-base-v2'''				)
               lowercase__       =				tf.constant([[0, 1, 2, 3, 4, 5]]				)
               lowercase__       =				model(UpperCamelCase				)[0]
  
               lowercase__       =				[1, 6, 30000]
               self.assertEqual(output.shape     ,		UpperCamelCase				)
  
               lowercase__       =				tf.constant(
                   [
                       [
                           [4.59_56_68, 0.74_46_27_54, -1.81_81_47],
                           [4.5_95_43_47, 0.7_45_41_84, -1.8_18_82_58],
                           [4.5_95_49_05, 0.7_44_82_35, -1.8_18_23_16],
                       ]
                   ]				)
               tf.debugging.assert_near(output[:, :3, :3]     ,		UpperCamelCase     ,		atol=1E-4				)
  
  
  
  
  
style_context: 
  
  
  '''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							:     Optional[int]								=			None
  
  lowerCamelCase							:     List[Any]								=			logging.get_logger(__name__)
  lowerCamelCase							:     Dict								=			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
  lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'
  
  # fmt: off
  lowerCamelCase							:     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 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
        lowerCAmelCase__      :  Tuple						      =							"""left"""
        lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Dict="<unk>"     ,		UpperCamelCase					:					int="<s>"     ,		UpperCamelCase					:					Dict="</s>"     ,		UpperCamelCase					:					Any=True     ,		UpperCamelCase					:					Tuple=False     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(
                   vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               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    UpperCamelCase__	(self					:					Tuple				):
  
               '''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=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_eos_token
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_bos_token
  
  
  
        @add_eos_token.setter
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        @add_bos_token.setter
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = 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(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				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				):
                      copyfile(self.vocab_file     ,		UpperCamelCase				)
  
               return (out_vocab_file,)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					"Conversation"				):
  
               '''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(UpperCamelCase				) 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(UpperCamelCase				) 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=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
  
  
  
  
  