



"""simple docstring"""







import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         DistilBertForMaskedLM,
         DistilBertForMultipleChoice,
         DistilBertForQuestionAnswering,
         DistilBertForSequenceClassification,
         DistilBertForTokenClassification,
         DistilBertModel,
     )







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_3    ,			SCREAMING_SNAKE_CASE_				:      Any=7    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=False    ,			SCREAMING_SNAKE_CASE_				:      List[str]=True    ,			SCREAMING_SNAKE_CASE_				:      List[str]=9_9    ,			SCREAMING_SNAKE_CASE_				:      str=3_2    ,			SCREAMING_SNAKE_CASE_				:      Tuple=5    ,			SCREAMING_SNAKE_CASE_				:      List[str]=4    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3_7    ,			SCREAMING_SNAKE_CASE_				:      Dict="gelu"    ,			SCREAMING_SNAKE_CASE_				:      Dict=0.1    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Tuple=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      int=1_6    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      Any=0.02    ,			SCREAMING_SNAKE_CASE_				:      Any=3    ,			SCREAMING_SNAKE_CASE_				:      Tuple=4    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			):
        lowerCAmelCase_   :				List[str]								= parent
        lowerCAmelCase_   :				Union[str, Any]								= batch_size
        lowerCAmelCase_   :				Dict								= seq_length
        lowerCAmelCase_   :				Optional[Any]								= is_training
        lowerCAmelCase_   :				int								= use_input_mask
        lowerCAmelCase_   :				Optional[int]								= use_token_type_ids
        lowerCAmelCase_   :				int								= use_labels
        lowerCAmelCase_   :				Dict								= vocab_size
        lowerCAmelCase_   :				Optional[Any]								= hidden_size
        lowerCAmelCase_   :				List[Any]								= num_hidden_layers
        lowerCAmelCase_   :				Union[str, Any]								= num_attention_heads
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				List[str]								= hidden_act
        lowerCAmelCase_   :				Dict								= hidden_dropout_prob
        lowerCAmelCase_   :				Any								= attention_probs_dropout_prob
        lowerCAmelCase_   :				List[Any]								= max_position_embeddings
        lowerCAmelCase_   :				Optional[int]								= type_vocab_size
        lowerCAmelCase_   :				int								= type_sequence_label_size
        lowerCAmelCase_   :				int								= initializer_range
        lowerCAmelCase_   :				Dict								= num_labels
        lowerCAmelCase_   :				Union[str, Any]								= num_choices
        lowerCAmelCase_   :				Any								= scope



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				List[str]								= ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size       )

        lowerCAmelCase_   :				Optional[int]								= None
        if self.use_input_mask:
          lowerCAmelCase_   :				Any								= random_attention_mask([self.batch_size, self.seq_length]       )

        lowerCAmelCase_   :				List[str]								= None
        lowerCAmelCase_   :				Union[str, Any]								= None
        lowerCAmelCase_   :				Optional[int]								= None
        if self.use_labels:
          lowerCAmelCase_   :				Dict								= ids_tensor([self.batch_size]    ,			self.type_sequence_label_size       )
          lowerCAmelCase_   :				List[Any]								= ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels       )
          lowerCAmelCase_   :				List[Any]								= ids_tensor([self.batch_size]    ,			self.num_choices       )

        lowerCAmelCase_   :				Optional[int]								= self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return DistilBertConfig(
            vocab_size=self.vocab_size    ,			dim=self.hidden_size    ,			n_layers=self.num_hidden_layers    ,			n_heads=self.num_attention_heads    ,			hidden_dim=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			dropout=self.hidden_dropout_prob    ,			attention_dropout=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			initializer_range=self.initializer_range    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Tuple								= DistilBertModel(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Optional[int]								= model(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= model(SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Optional[int]								= DistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				int								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Any								= DistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Any								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			start_positions=SCREAMING_SNAKE_CASE_    ,			end_positions=SCREAMING_SNAKE_CASE_       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Optional[int]								= self.num_labels
        lowerCAmelCase_   :				str								= DistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				List[str]								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= self.num_labels
        lowerCAmelCase_   :				Union[str, Any]								= DistilBertForTokenClassification(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()

        lowerCAmelCase_   :				int								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Tuple								= self.num_choices
        lowerCAmelCase_   :				Union[str, Any]								= DistilBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				List[Any]								= input_ids.unsqueeze(1       ).expand(-1    ,			self.num_choices    ,			-1       ).contiguous()
        lowerCAmelCase_   :				int								= input_mask.unsqueeze(1       ).expand(-1    ,			self.num_choices    ,			-1       ).contiguous()
        lowerCAmelCase_   :				List[Any]								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_choices)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Any								= self.prepare_config_and_inputs()
        ((lowerCAmelCase_)						,(lowerCAmelCase_)						,(lowerCAmelCase_)						,(lowerCAmelCase_)						,(lowerCAmelCase_)						,(lowerCAmelCase_))   :				str								= config_and_inputs
        lowerCAmelCase_   :				List[str]								= {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      _SCREAMING_SNAKE_CASE  				=     (
          {
              """feature-extraction""": DistilBertModel,
              """fill-mask""": DistilBertForMaskedLM,
              """question-answering""": DistilBertForQuestionAnswering,
              """text-classification""": DistilBertForSequenceClassification,
              """token-classification""": DistilBertForTokenClassification,
              """zero-shot""": DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     True



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				Optional[int]								= DistilBertModelTester(self       )
        lowerCAmelCase_   :				int								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			dim=3_7       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        self.config_tester.run_common_tests()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				str								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Optional[int]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Optional[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				str								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				Tuple								= DistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )





      @slow
      @require_torch_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
          # BertForMultipleChoice behaves incorrectly in JIT environments.
          if model_class == DistilBertForMultipleChoice:
            return

          lowerCAmelCase_   :				List[str]								= True
          lowerCAmelCase_   :				str								= model_class(config=SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Optional[Any]								= torch.jit.trace(
              SCREAMING_SNAKE_CASE_    ,			(inputs_dict['input_ids'].to('cpu'       ), inputs_dict['attention_mask'].to('cpu'       ))       )

          with tempfile.TemporaryDirectory() as tmp:
            torch.jit.save(SCREAMING_SNAKE_CASE_    ,			os.path.join(SCREAMING_SNAKE_CASE_    ,			'traced_model.pt'       )       )
            lowerCAmelCase_   :				Union[str, Any]								= torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE_    ,			'traced_model.pt'       )    ,			map_location=SCREAMING_SNAKE_CASE_       )
            loaded(inputs_dict['input_ids'].to(SCREAMING_SNAKE_CASE_       )    ,			inputs_dict['attention_mask'].to(SCREAMING_SNAKE_CASE_       )       )







@require_torch
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Tuple								= DistilBertModel.from_pretrained('distilbert-base-uncased'       )
        lowerCAmelCase_   :				Dict								= torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]       )
        lowerCAmelCase_   :				Optional[int]								= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_       )[0]
        lowerCAmelCase_   :				Optional[int]								= torch.Size((1, 1_1, 7_6_8)       )
        self.assertEqual(output.shape    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				int								= torch.tensor(
            [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]       )

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )       )










"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{"""tokenizer_file""": """tokenizer.json"""}

lowercase__		:				List[str]          =	{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     ["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE  				=     None



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="<unk>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="</s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<pad>"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=False    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			bos_token=SCREAMING_SNAKE_CASE_    ,			eos_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			add_prefix_space=SCREAMING_SNAKE_CASE_    ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)
        lowerCAmelCase_   :				Optional[Any]								= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get('add_prefix_space'    ,			SCREAMING_SNAKE_CASE_       ) != add_prefix_space:
          lowerCAmelCase_   :				str								= getattr(SCREAMING_SNAKE_CASE_    ,			pre_tok_state.pop('type'       )       )
          lowerCAmelCase_   :				str								= add_prefix_space
          lowerCAmelCase_   :				str								= pre_tok_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= add_prefix_space



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )
        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )

        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = None       ):
        lowerCAmelCase_   :				Optional[int]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      "Conversation"       ):
        lowerCAmelCase_   :				int								= []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       ) + [self.eos_token_id]       )

        if len(SCREAMING_SNAKE_CASE_       ) > self.model_max_length:
          lowerCAmelCase_   :				Any								= input_ids[-self.model_max_length :]
        return input_ids





