





import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 (
       NystromformerForMaskedLM,
       NystromformerForMultipleChoice,
       NystromformerForQuestionAnswering,
       NystromformerForSequenceClassification,
       NystromformerForTokenClassification,
       NystromformerModel,
   )
   from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class 			__UpperCamelCase    :





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase=13				,							_UpperCamelCase=7				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=99				,							_UpperCamelCase=32				,							_UpperCamelCase=5				,							_UpperCamelCase=4				,							_UpperCamelCase=37				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=512				,							_UpperCamelCase=16				,							_UpperCamelCase=2				,							_UpperCamelCase=0.02				,							_UpperCamelCase=3				,							_UpperCamelCase=4				,							_UpperCamelCase=None				,							):
        _UpperCAmelCase           =   parent
        _UpperCAmelCase           =   batch_size
        _UpperCAmelCase           =   seq_length
        _UpperCAmelCase           =   is_training
        _UpperCAmelCase           =   use_input_mask
        _UpperCAmelCase           =   use_token_type_ids
        _UpperCAmelCase           =   use_labels
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   type_vocab_size
        _UpperCAmelCase           =   type_sequence_label_size
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   num_labels
        _UpperCAmelCase           =   num_choices
        _UpperCAmelCase           =   scope





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size					)

        _UpperCAmelCase           =   None
        if self.use_input_mask:
           _UpperCAmelCase           =   random_attention_mask([self.batch_size, self.seq_length]					)

        _UpperCAmelCase           =   None
        if self.use_token_type_ids:
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size					)

        _UpperCAmelCase           =   None
        _UpperCAmelCase           =   None
        _UpperCAmelCase           =   None
        if self.use_labels:
           _UpperCAmelCase           =   ids_tensor([self.batch_size]				,							self.type_sequence_label_size					)
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels					)
           _UpperCAmelCase           =   ids_tensor([self.batch_size]				,							self.num_choices					)

        _UpperCAmelCase           =   self.get_config()

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





     def 		UpperCamelCase(     self					):
        return NystromformerConfig(
            vocab_size=self.vocab_size				,							hidden_size=self.hidden_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				,							is_decoder=_UpperCamelCase				,							initializer_range=self.initializer_range				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   NystromformerModel(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase					)
        _UpperCAmelCase           =   model(_UpperCamelCase				,							token_type_ids=_UpperCamelCase					)
        _UpperCAmelCase           =   model(_UpperCamelCase					)
        self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   NystromformerForMaskedLM(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   NystromformerForQuestionAnswering(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							start_positions=_UpperCamelCase				,							end_positions=_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				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.num_labels
        _UpperCAmelCase           =   NystromformerForSequenceClassification(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.num_labels
        _UpperCAmelCase           =   NystromformerForTokenClassification(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.num_choices
        _UpperCAmelCase           =   NystromformerForMultipleChoice(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   input_ids.unsqueeze(1					).expand(-1				,							self.num_choices				,							-1					).contiguous()
        _UpperCAmelCase           =   token_type_ids.unsqueeze(1					).expand(-1				,							self.num_choices				,							-1					).contiguous()
        _UpperCAmelCase           =   input_mask.unsqueeze(1					).expand(-1				,							self.num_choices				,							-1					).contiguous()
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							token_type_ids=_UpperCamelCase				,							labels=_UpperCamelCase				,							)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      
        )           =   config_and_inputs
        _UpperCAmelCase           =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict




@require_torch
class 			__UpperCamelCase    (							A__	, A__	, unittest.TestCase    ):
     __A	:  Tuple							  =   (
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __A	:  Optional[Any]							  =   (
         {
             """feature-extraction""": NystromformerModel,
             """fill-mask""": NystromformerForMaskedLM,
             """question-answering""": NystromformerForQuestionAnswering,
             """text-classification""": NystromformerForSequenceClassification,
             """token-classification""": NystromformerForTokenClassification,
             """zero-shot""": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __A	:  Any							  =   False
     __A	:  Optional[Any]							  =   False





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   NystromformerModelTester(self					)
        _UpperCAmelCase           =   ConfigTester(self				,							config_class=_UpperCamelCase				,							hidden_size=37					)





     def 		UpperCamelCase(     self					):
        self.config_tester.run_common_tests()





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           _UpperCAmelCase           =   type
           self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase					)





     @slow
     def 		UpperCamelCase(     self					):
        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase           =   NystromformerModel.from_pretrained(_UpperCamelCase					)
           self.assertIsNotNone(_UpperCamelCase					)




@require_torch
class 			__UpperCamelCase    (							unittest.TestCase    ):





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''					)
        _UpperCAmelCase           =   torch.tensor([[0, 1, 2, 3, 4, 5]]					)

        with torch.no_grad():
           _UpperCAmelCase           =   model(_UpperCamelCase					)[0]

        _UpperCAmelCase           =   torch.Size((1, 6, 768)					)
        self.assertEqual(output.shape				,							_UpperCamelCase					)

        _UpperCAmelCase           =   torch.tensor(
            [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]					)

        self.assertTrue(torch.allclose(output[:, :3, :3]				,							_UpperCamelCase				,							atol=1e-4					)					)





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   '''the [MASK] of Belgium is Brussels'''

        _UpperCAmelCase           =   AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''					)
        _UpperCAmelCase           =   NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''					)

        _UpperCAmelCase           =   tokenizer(_UpperCamelCase				,							return_tensors='''pt'''					)

        with torch.no_grad():
           _UpperCAmelCase           =   model(encoding.input_ids					).logits

        _UpperCAmelCase           =   token_logits[:, 2, :].argmax(-1					)[0]

        self.assertEqual(tokenizer.decode(_UpperCamelCase					)				,							'''capital'''					)






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{"vocab_file": "spiece.model"}

UpperCAmelCase_           =	{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_           =	{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

UpperCAmelCase_           =	"▁"




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   VOCAB_FILES_NAMES
     __A	:  Union[str, Any]							  =   PRETRAINED_VOCAB_FILES_MAP
     __A	:  Any							  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Tuple							  =   ["""input_ids""", """attention_mask"""]





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase="</s>"				,							_UpperCamelCase="<unk>"				,							_UpperCamelCase="<pad>"				,							_UpperCamelCase=100				,							_UpperCamelCase=None				,							_UpperCamelCase = None				,							_UpperCamelCase=True				,							**_UpperCamelCase				,							):
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
           _UpperCAmelCase           =   [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase					)]
        elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _UpperCAmelCase           =   len(set(filter(lambda _UpperCamelCase					: bool('''extra_id''' in str(_UpperCamelCase					)					)				,							_UpperCamelCase					)					)					)
           if extra_tokens != extra_ids:
              raise ValueError(
                  f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                  ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                  ''' tokens'''					)
        if legacy:
           logger.warning_once(
               f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
               ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''					)

        _UpperCAmelCase           =   legacy
        _UpperCAmelCase           =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=_UpperCamelCase				,							unk_token=_UpperCamelCase				,							pad_token=_UpperCamelCase				,							extra_ids=_UpperCamelCase				,							additional_special_tokens=_UpperCamelCase				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=_UpperCamelCase				,							**_UpperCamelCase				,							)

        _UpperCAmelCase           =   vocab_file
        _UpperCAmelCase           =   extra_ids

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(_UpperCamelCase					)





     @staticmethod
     def 		UpperCamelCase(     _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _UpperCAmelCase           =   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
              return init_max_model_length
           elif init_max_model_length is None:
              warnings.warn(
                  '''This tokenizer was incorrectly instantiated with a model max length of'''
                  f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                  ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                  ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                  f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                  f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                  f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                  ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                  ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''				,							_UpperCamelCase				,							)

        return max_model_length





     @property
     def 		UpperCamelCase(     self					):
        return self.sp_model.get_piece_size() + self._extra_ids





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   {self.convert_ids_to_tokens(_UpperCamelCase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab





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

        # normal case: some special tokens
        if token_ids_a is None:
           return ([0] * len(_UpperCamelCase					)) + [1]
        return ([0] * len(_UpperCamelCase					)) + [1] + ([0] * len(_UpperCamelCase					)) + [1]





     def 		UpperCamelCase(     self					):
        return list(
            set(filter(lambda _UpperCamelCase					: bool(re.search(R'''<extra_id_\d+>'''				,							_UpperCamelCase					)					) is not None				,							self.additional_special_tokens					)					)					)





     def 		UpperCamelCase(     self					):
        return [self._convert_token_to_id(_UpperCamelCase					) for token in self.get_sentinel_tokens()]





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if len(_UpperCamelCase					) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
               ''' eos tokens being added.'''					)
           return token_ids
        else:
           return token_ids + [self.eos_token_id]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   [self.eos_token_id]

        if token_ids_a is None:
           return len(token_ids_a + eos					) * [0]
        return len(token_ids_a + eos + token_ids_a + eos					) * [0]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
        if token_ids_a is None:
           return token_ids_a
        else:
           _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
           return token_ids_a + token_ids_a





     def __getstate__(     self					):
        _UpperCAmelCase           =   self.__dict__.copy()
        _UpperCAmelCase           =   None
        return state





     def __setstate__(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   d

        # for backward compatibility
        if not hasattr(self				,							'''sp_model_kwargs'''					):
           _UpperCAmelCase           =   {}

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(self.vocab_file					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
           _UpperCAmelCase           =   SPIECE_UNDERLINE + text.replace(_UpperCamelCase				,							''' '''					)
        return super().tokenize(_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        if not self.legacy:
           _UpperCAmelCase           =   text.startswith(_UpperCamelCase					)
           if is_first:
              _UpperCAmelCase           =   text[1:]

        _UpperCAmelCase           =   self.sp_model.encode(_UpperCamelCase				,							out_type=_UpperCamelCase					)

        if not self.legacy and not is_first and not text.startswith(''' '''					) and tokens[0].startswith(_UpperCamelCase					):
           _UpperCAmelCase           =   ([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
        return tokens





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if token.startswith('''<extra_id_'''					):
           _UpperCAmelCase           =   re.match(R'''<extra_id_(\d+)>'''				,							_UpperCamelCase					)
           _UpperCAmelCase           =   int(match.group(1					)					)
           return self.vocab_size - num - 1
        return self.sp_model.piece_to_id(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if index < self.sp_model.get_piece_size():
           _UpperCAmelCase           =   self.sp_model.IdToPiece(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   f'''<extra_id_{self.vocab_size - 1 - index}>'''
        return token





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   []
        _UpperCAmelCase           =   ''''''
        _UpperCAmelCase           =   False
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              if not prev_is_special:
                 out_string += " "
              out_string += self.sp_model.decode(_UpperCamelCase					) + token
              _UpperCAmelCase           =   True
              _UpperCAmelCase           =   []
           else:
              current_sub_tokens.append(_UpperCamelCase					)
              _UpperCAmelCase           =   False
        out_string += self.sp_model.decode(_UpperCamelCase					)
        return out_string.strip()





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        if not os.path.isdir(_UpperCamelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
        _UpperCAmelCase           =   os.path.join(
            _UpperCamelCase				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

        if os.path.abspath(self.vocab_file					) != os.path.abspath(_UpperCamelCase					) and os.path.isfile(self.vocab_file					):
           copyfile(self.vocab_file				,							_UpperCamelCase					)
        elif not os.path.isfile(self.vocab_file					):
           with open(_UpperCamelCase				,							'''wb'''					) as fi:
              _UpperCAmelCase           =   self.sp_model.serialized_model_proto()
              fi.write(_UpperCamelCase					)

        return (out_vocab_file,)