


'''simple docstring'''







import os
import tempfile
import unittest

from transformers import FlaubertConfig, 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 (
           FlaubertForMultipleChoice,
           FlaubertForQuestionAnswering,
           FlaubertForQuestionAnsweringSimple,
           FlaubertForSequenceClassification,
           FlaubertForTokenClassification,
           FlaubertModel,
           FlaubertWithLMHeadModel,
       )
       from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=2   ,						snake_case__=99   ,						snake_case__=0   ,						snake_case__=32   ,						snake_case__=5   ,						snake_case__=4   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=12   ,						snake_case__=2   ,						snake_case__=0.02   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__="last"   ,						snake_case__=None   ,						snake_case__=None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							parent
            _lowerCAmelCase     :				int       =							batch_size
            _lowerCAmelCase     :				str       =							seq_length
            _lowerCAmelCase     :				Tuple       =							is_training
            _lowerCAmelCase     :				Any       =							use_input_lengths
            _lowerCAmelCase     :				Dict       =							use_token_type_ids
            _lowerCAmelCase     :				List[str]       =							use_labels
            _lowerCAmelCase     :				Any       =							gelu_activation
            _lowerCAmelCase     :				Union[str, Any]       =							sinusoidal_embeddings
            _lowerCAmelCase     :				str       =							causal
            _lowerCAmelCase     :				Any       =							asm
            _lowerCAmelCase     :				Optional[int]       =							n_langs
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				List[str]       =							n_special
            _lowerCAmelCase     :				Any       =							hidden_size
            _lowerCAmelCase     :				Optional[int]       =							num_hidden_layers
            _lowerCAmelCase     :				Dict       =							num_attention_heads
            _lowerCAmelCase     :				Dict       =							hidden_dropout_prob
            _lowerCAmelCase     :				Optional[Any]       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				Union[str, Any]       =							max_position_embeddings
            _lowerCAmelCase     :				Tuple       =							type_vocab_size
            _lowerCAmelCase     :				Any       =							type_sequence_label_size
            _lowerCAmelCase     :				Optional[int]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							num_labels
            _lowerCAmelCase     :				Optional[int]       =							num_choices
            _lowerCAmelCase     :				Dict       =							summary_type
            _lowerCAmelCase     :				str       =							use_proj
            _lowerCAmelCase     :				int       =							scope







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )
            _lowerCAmelCase     :				str       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				Any       =							None
            if self.use_input_lengths:
                   _lowerCAmelCase     :				int       =							(
                       ids_tensor([self.batch_size]   ,						vocab_size=2 ) + self.seq_length - 2
                   )  # small variation of seq_length

            _lowerCAmelCase     :				Tuple       =							None
            if self.use_token_type_ids:
                   _lowerCAmelCase     :				Dict       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.n_langs )

            _lowerCAmelCase     :				Optional[Any]       =							None
            _lowerCAmelCase     :				Optional[int]       =							None
            _lowerCAmelCase     :				Optional[int]       =							None
            if self.use_labels:
                   _lowerCAmelCase     :				int       =							ids_tensor([self.batch_size]   ,						self.type_sequence_label_size )
                   _lowerCAmelCase     :				Optional[int]       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels )
                   _lowerCAmelCase     :				List[str]       =							ids_tensor([self.batch_size]   ,						2 ).float()
                   _lowerCAmelCase     :				int       =							ids_tensor([self.batch_size]   ,						self.num_choices )

            _lowerCAmelCase     :				List[Any]       =							self.get_config()

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )







     def a						(			self ):

            '''simple docstring'''

            return FlaubertConfig(
                vocab_size=self.vocab_size   ,						n_special=self.n_special   ,						emb_dim=self.hidden_size   ,						n_layers=self.num_hidden_layers   ,						n_heads=self.num_attention_heads   ,						dropout=self.hidden_dropout_prob   ,						attention_dropout=self.attention_probs_dropout_prob   ,						gelu_activation=self.gelu_activation   ,						sinusoidal_embeddings=self.sinusoidal_embeddings   ,						asm=self.asm   ,						causal=self.causal   ,						n_langs=self.n_langs   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						summary_type=self.summary_type   ,						use_proj=self.use_proj   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							FlaubertModel(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				str       =							model(snake_case__   ,						lengths=snake_case__   ,						langs=snake_case__ )
            _lowerCAmelCase     :				str       =							model(snake_case__   ,						langs=snake_case__ )
            _lowerCAmelCase     :				Dict       =							model(snake_case__ )
            self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							FlaubertWithLMHeadModel(snake_case__ )
            model.to(snake_case__ )
            model.eval()

            _lowerCAmelCase     :				Tuple       =							model(snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__ )
            self.parent.assertEqual(result.loss.shape   ,						() )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							FlaubertForQuestionAnsweringSimple(snake_case__ )
            model.to(snake_case__ )
            model.eval()

            _lowerCAmelCase     :				Optional[int]       =							model(snake_case__ )

            _lowerCAmelCase     :				Any       =							model(snake_case__   ,						start_positions=snake_case__   ,						end_positions=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 a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							FlaubertForQuestionAnswering(snake_case__ )
            model.to(snake_case__ )
            model.eval()

            _lowerCAmelCase     :				int       =							model(snake_case__ )

            _lowerCAmelCase     :				Tuple       =							model(
                snake_case__   ,						start_positions=snake_case__   ,						end_positions=snake_case__   ,						cls_index=snake_case__   ,						is_impossible=snake_case__   ,						p_mask=snake_case__   ,						)

            _lowerCAmelCase     :				List[Any]       =							model(
                snake_case__   ,						start_positions=snake_case__   ,						end_positions=snake_case__   ,						cls_index=snake_case__   ,						is_impossible=snake_case__   ,						)

            ((_lowerCAmelCase)      ,  )     :				Optional[Any]       =							result_with_labels.to_tuple()

            _lowerCAmelCase     :				Union[str, Any]       =							model(snake_case__   ,						start_positions=snake_case__   ,						end_positions=snake_case__ )

            ((_lowerCAmelCase)      ,  )     :				Optional[int]       =							result_with_labels.to_tuple()

            self.parent.assertEqual(result_with_labels.loss.shape   ,						() )
            self.parent.assertEqual(result.start_top_log_probs.shape   ,						(self.batch_size, model.config.start_n_top) )
            self.parent.assertEqual(result.start_top_index.shape   ,						(self.batch_size, model.config.start_n_top) )
            self.parent.assertEqual(
                result.end_top_log_probs.shape   ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
            self.parent.assertEqual(
                result.end_top_index.shape   ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
            self.parent.assertEqual(result.cls_logits.shape   ,						(self.batch_size,) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							FlaubertForSequenceClassification(snake_case__ )
            model.to(snake_case__ )
            model.eval()

            _lowerCAmelCase     :				str       =							model(snake_case__ )
            _lowerCAmelCase     :				Tuple       =							model(snake_case__   ,						labels=snake_case__ )

            self.parent.assertEqual(result.loss.shape   ,						() )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.num_labels
            _lowerCAmelCase     :				Optional[Any]       =							FlaubertForTokenClassification(snake_case__ )
            model.to(snake_case__ )
            model.eval()

            _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						attention_mask=snake_case__   ,						labels=snake_case__ )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							self.num_choices
            _lowerCAmelCase     :				Optional[Any]       =							FlaubertForMultipleChoice(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				List[Any]       =							input_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Optional[Any]       =							token_type_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Union[str, Any]       =							input_mask.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				List[Any]       =							model(
                snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__   ,						)
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				List[str]       =							config_and_inputs
            _lowerCAmelCase     :				int       =							{
                'input_ids': input_ids,
                'token_type_ids': token_type_ids,
                'lengths': input_lengths,
                'attention_mask': input_mask,
            }
            return config, inputs_dict
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (
         (
             FlaubertModel,
             FlaubertWithLMHeadModel,
             FlaubertForQuestionAnswering,
             FlaubertForQuestionAnsweringSimple,
             FlaubertForSequenceClassification,
             FlaubertForTokenClassification,
             FlaubertForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__           =     (
         {
             "feature-extraction": FlaubertModel,
             "fill-mask": FlaubertWithLMHeadModel,
             "question-answering": FlaubertForQuestionAnsweringSimple,
             "text-classification": FlaubertForSequenceClassification,
             "token-classification": FlaubertForTokenClassification,
             "zero-shot": FlaubertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith('Fast' )
            ):
                   # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                   # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                   # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                   return True

            return False







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__=False ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super()._prepare_for_class(snake_case__   ,						snake_case__   ,						return_labels=snake_case__ )

            if return_labels:
                   if model_class.__name__ == "FlaubertForQuestionAnswering":
                          _lowerCAmelCase     :				int       =							torch.zeros(
                              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )
                          _lowerCAmelCase     :				Dict       =							torch.zeros(
                              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )

            return inputs_dict







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							FlaubertModelTester(self )
            _lowerCAmelCase     :				Any       =							ConfigTester(self   ,						config_class=snake_case__   ,						emb_dim=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_model(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_lm_head(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_simple_qa(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_qa(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_token_classif(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_multiple_choice(*snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Any       =							FlaubertModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     @slow
     @require_torch_gpu
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                   # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
                   if model_class == FlaubertForMultipleChoice:
                          return

                   _lowerCAmelCase     :				Tuple       =							True
                   _lowerCAmelCase     :				List[Any]       =							model_class(config=snake_case__ )

                   _lowerCAmelCase     :				Union[str, Any]       =							self._prepare_for_class(snake_case__   ,						snake_case__ )
                   _lowerCAmelCase     :				Dict       =							torch.jit.trace(
                       snake_case__   ,						(inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )

                   with tempfile.TemporaryDirectory() as tmp:
                          torch.jit.save(snake_case__   ,						os.path.join(snake_case__   ,						'traced_model.pt' ) )
                          _lowerCAmelCase     :				Union[str, Any]       =							torch.jit.load(os.path.join(snake_case__   ,						'traced_model.pt' )   ,						map_location=snake_case__ )
                          loaded(inputs_dict['input_ids'].to(snake_case__ )   ,						inputs_dict['attention_mask'].to(snake_case__ ) )
@require_torch
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							FlaubertModel.from_pretrained('flaubert/flaubert_base_cased' )
            _lowerCAmelCase     :				Tuple       =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
            with torch.no_grad():
                   _lowerCAmelCase     :				Union[str, Any]       =							model(snake_case__ )[0]
            _lowerCAmelCase     :				Union[str, Any]       =							torch.Size((1, 11, 768) )
            self.assertEqual(output.shape   ,						snake_case__ )
            _lowerCAmelCase     :				str       =							torch.tensor(
                [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )

            self.assertTrue(torch.allclose(output[:, :3, :3]   ,						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
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase     :  Tuple											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  str											=    {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase     :  str											=    {
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     GPTaTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            _lowerCAmelCase     :				str       =							kwargs.pop('add_bos_token'   ,						snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				str       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				List[str]       =							add_prefix_space
                   _lowerCAmelCase     :				Dict       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Any       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

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







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

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

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







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							[]
            for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(snake_case__   ,						add_special_tokens=snake_case__ ) + [self.eos_token_id] )

            if len(snake_case__ ) > self.model_max_length:
                   _lowerCAmelCase     :				str       =							input_ids[-self.model_max_length :]
            return input_ids

