


'''simple docstring'''







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=99   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=9   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=32   ,						snake_case__=5   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__=8   ,						snake_case__=0.1   ,						snake_case__=0.002   ,						snake_case__=1   ,						snake_case__=0   ,						snake_case__=0   ,						snake_case__=None   ,						snake_case__=None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							parent
            _lowerCAmelCase     :				Union[str, Any]       =							batch_size
            _lowerCAmelCase     :				Dict       =							encoder_seq_length
            _lowerCAmelCase     :				Any       =							decoder_seq_length
            # For common tests
            _lowerCAmelCase     :				int       =							self.decoder_seq_length
            _lowerCAmelCase     :				Optional[Any]       =							is_training
            _lowerCAmelCase     :				Optional[Any]       =							use_attention_mask
            _lowerCAmelCase     :				List[Any]       =							use_labels
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				Optional[int]       =							hidden_size
            _lowerCAmelCase     :				List[str]       =							num_hidden_layers
            _lowerCAmelCase     :				Any       =							num_attention_heads
            _lowerCAmelCase     :				Tuple       =							d_ff
            _lowerCAmelCase     :				Dict       =							relative_attention_num_buckets
            _lowerCAmelCase     :				Any       =							dropout_rate
            _lowerCAmelCase     :				Tuple       =							initializer_factor
            _lowerCAmelCase     :				Optional[int]       =							eos_token_id
            _lowerCAmelCase     :				str       =							pad_token_id
            _lowerCAmelCase     :				str       =							decoder_start_token_id
            _lowerCAmelCase     :				Optional[int]       =							None
            _lowerCAmelCase     :				Optional[Any]       =							decoder_layers







     def a						(			self ):

            '''simple docstring'''

            return TaConfig.from_pretrained('google/umt5-base' )







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

            '''simple docstring'''

            if attention_mask is None:
                   _lowerCAmelCase     :				str       =							input_ids.ne(config.pad_token_id )
            if decoder_attention_mask is None:
                   _lowerCAmelCase     :				Tuple       =							decoder_input_ids.ne(config.pad_token_id )
            if head_mask is None:
                   _lowerCAmelCase     :				str       =							torch.ones(config.num_hidden_layers   ,						config.num_attention_heads   ,						device=snake_case__ )
            if decoder_head_mask is None:
                   _lowerCAmelCase     :				List[str]       =							torch.ones(config.num_decoder_layers   ,						config.num_attention_heads   ,						device=snake_case__ )
            if cross_attn_head_mask is None:
                   _lowerCAmelCase     :				Dict       =							torch.ones(
                       config.num_decoder_layers   ,						config.num_attention_heads   ,						device=snake_case__ )
            return {
                "input_ids": input_ids,
                "decoder_input_ids": decoder_input_ids,
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "head_mask": head_mask,
                "decoder_head_mask": decoder_head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
            }







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							ids_tensor([self.batch_size, self.encoder_seq_length]   ,						self.vocab_size )
            _lowerCAmelCase     :				List[Any]       =							ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for NllbMoe the position_ids are prepared such that
            # all pad tokens have pos id = 2 and rest are between 2..seq_length
            # and the seq_length here is seq_length - num_pad_tokens
            # but when using past, there is no way of knowing if the past input ids had
            # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
            # position_ids being off by num_pad_tokens in past input
            _lowerCAmelCase     :				str       =							input_ids.clamp(self.pad_token_id + 1 )
            _lowerCAmelCase     :				Union[str, Any]       =							decoder_input_ids.clamp(self.pad_token_id + 1 )

            _lowerCAmelCase     :				Any       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							config.num_attention_heads
            _lowerCAmelCase     :				str       =							self.prepare_inputs_dict(snake_case__   ,						snake_case__   ,						snake_case__ )
            return config, input_dict







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							self.prepare_config_and_inputs()
            return config, inputs_dict







     def a						(			self ):

            '''simple docstring'''

            return TaConfig(
                vocab_size=166   ,						d_model=self.hidden_size   ,						d_ff=self.d_ff   ,						d_kv=self.hidden_size // self.num_attention_heads   ,						num_layers=self.num_hidden_layers   ,						num_decoder_layers=self.decoder_layers   ,						num_heads=self.num_attention_heads   ,						relative_attention_num_buckets=self.relative_attention_num_buckets   ,						dropout_rate=self.dropout_rate   ,						initializer_factor=self.initializer_factor   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.pad_token_id   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						)







     def a						(			self ):

            '''simple docstring'''

            return TaConfig(
                vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						d_ff=self.d_ff   ,						d_kv=self.hidden_size // self.num_attention_heads   ,						num_layers=self.num_hidden_layers   ,						num_decoder_layers=self.decoder_layers   ,						num_heads=self.num_attention_heads   ,						relative_attention_num_buckets=self.relative_attention_num_buckets   ,						dropout_rate=self.dropout_rate   ,						initializer_factor=self.initializer_factor   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.pad_token_id   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						)







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

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							UMTaModel(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Any       =							model(
                input_ids=snake_case__   ,						decoder_input_ids=snake_case__   ,						attention_mask=snake_case__   ,						decoder_attention_mask=snake_case__   ,						)
            _lowerCAmelCase     :				Any       =							model(input_ids=snake_case__   ,						decoder_input_ids=snake_case__ )
            _lowerCAmelCase     :				str       =							result.last_hidden_state
            _lowerCAmelCase     :				Any       =							result.past_key_values
            _lowerCAmelCase     :				List[Any]       =							result.encoder_last_hidden_state

            self.parent.assertEqual(encoder_output.size()   ,						(self.batch_size, self.encoder_seq_length, self.hidden_size) )
            self.parent.assertEqual(decoder_output.size()   ,						(self.batch_size, self.decoder_seq_length, self.hidden_size) )
            # There should be `num_layers` key value embeddings stored in decoder_past
            self.parent.assertEqual(len(snake_case__ )   ,						config.num_layers )
            # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
            self.parent.assertEqual(len(decoder_past[0] )   ,						4 )







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

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							UMTaModel(config=snake_case__ ).get_decoder().to(snake_case__ ).eval()
            # first forward pass
            _lowerCAmelCase     :				List[Any]       =							model(snake_case__   ,						use_cache=snake_case__ )
            _lowerCAmelCase     :				Dict       =							model(snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							model(snake_case__   ,						use_cache=snake_case__ )

            self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) )
            self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) + 1 )

            _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            _lowerCAmelCase     :				List[str]       =							ids_tensor((self.batch_size, 1)   ,						config.vocab_size )

            # append to next input_ids and
            _lowerCAmelCase     :				Any       =							torch.cat([input_ids, next_tokens]   ,						dim=-1 )

            _lowerCAmelCase     :				Any       =							model(snake_case__ )['last_hidden_state']
            _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						past_key_values=snake_case__ )['last_hidden_state']

            # select random slice
            _lowerCAmelCase     :				str       =							ids_tensor((1,)   ,						output_from_past.shape[-1] ).item()
            _lowerCAmelCase     :				int       =							output_from_no_past[:, -1, random_slice_idx].detach()
            _lowerCAmelCase     :				Optional[Any]       =							output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(snake_case__   ,						snake_case__   ,						atol=1E-3 ) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							UMTaModel(config=snake_case__ ).to(snake_case__ ).half().eval()
            _lowerCAmelCase     :				Dict       =							model(**snake_case__ )['last_hidden_state']
            self.parent.assertFalse(torch.isnan(snake_case__ ).any().item() )
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     __magic_name__           =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
     __magic_name__           =     (
         {
             "conversational": UMTaForConditionalGeneration,
             "feature-extraction": UMTaModel,
             "summarization": UMTaForConditionalGeneration,
             "text2text-generation": UMTaForConditionalGeneration,
             "translation": UMTaForConditionalGeneration,
             "question-answering": UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__           =     True
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     True
     __magic_name__           =     True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     __magic_name__           =     [0.8, 0.9]







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							UMTaModelTester(self )







     @unittest.skip('Test has a segmentation fault on torch 1.8.0' )
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.model_tester.prepare_config_and_inputs()
            _lowerCAmelCase     :				Union[str, Any]       =							UMTaModel(config_and_inputs[0] ).to(snake_case__ )
            with tempfile.TemporaryDirectory() as tmpdirname:
                   torch.onnx.export(
                       snake_case__   ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,						F'{tmpdirname}/t5_test.onnx'   ,						export_params=snake_case__   ,						opset_version=9   ,						input_names=['input_ids', 'decoder_input_ids']   ,						)







     @unittest.skipIf(torch_device == 'cpu'   ,						'Cant do half precision' )
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
            _lowerCAmelCase     :				List[Any]       =							self.model_tester.prepare_config_and_inputs()
            _lowerCAmelCase     :				Dict       =							config_and_inputs[0]
            _lowerCAmelCase     :				int       =							UMTaForConditionalGeneration(snake_case__ ).eval()
            model.to(snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							{
                'head_mask': torch.zeros(config.num_layers   ,						config.num_heads   ,						device=snake_case__ ),
                'decoder_head_mask': torch.zeros(config.num_decoder_layers   ,						config.num_heads   ,						device=snake_case__ ),
                'cross_attn_head_mask': torch.zeros(config.num_decoder_layers   ,						config.num_heads   ,						device=snake_case__ ),
            }

            for attn_name, (name, mask) in zip(snake_case__   ,						head_masking.items() ):
                   _lowerCAmelCase     :				Tuple       =							{name: mask}
                   # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                   if name == "head_mask":
                          _lowerCAmelCase     :				Optional[int]       =							torch.ones(
                              config.num_decoder_layers   ,						config.num_heads   ,						device=snake_case__ )

                   _lowerCAmelCase     :				Optional[Any]       =							model.generate(
                       config_and_inputs[1]['input_ids']   ,						num_beams=1   ,						max_length=3   ,						output_attentions=snake_case__   ,						return_dict_in_generate=snake_case__   ,						**snake_case__   ,						)
                   # We check the state of decoder_attentions and cross_attentions just from the last step
                   _lowerCAmelCase     :				Tuple       =							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                   self.assertEqual(sum([w.sum().item() for w in attn_weights] )   ,						0.0 )







     @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
     def a						(			self ):

            '''simple docstring'''

            pass
@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     @unittest.skip(
         'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							UMTaForConditionalGeneration.from_pretrained('google/umt5-small'   ,						return_dict=snake_case__ ).to(snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							AutoTokenizer.from_pretrained('google/umt5-small'   ,						use_fast=snake_case__   ,						legacy=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							[
                'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
                'No se como puedo <extra_id_0>.',
                'This is the reason why we <extra_id_0> them.',
                'The <extra_id_0> walks in <extra_id_1>, seats',
                'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
            ]
            _lowerCAmelCase     :				str       =							tokenizer(snake_case__   ,						return_tensors='pt'   ,						padding=snake_case__ ).input_ids
            # fmt: off
            _lowerCAmelCase     :				Optional[Any]       =							torch.tensor(
                [
                    [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
                ] )
            # fmt: on
            torch.testing.assert_allclose(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				str       =							model.generate(input_ids.to(snake_case__ ) )
            _lowerCAmelCase     :				Optional[int]       =							[
                '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
                '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
                '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
                '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
                '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.batch_decode(snake_case__ )
            self.assertEqual(snake_case__   ,						snake_case__ )





'''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

