




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )


@require_flax
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int)						->int:





           '''simple docstring'''

           A__							=   jnp.ones((batch_size, length)) / length
           return scores


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[Any]:





           '''simple docstring'''

           A__							=   None
           A__							=   20

           A__							=   self._get_uniform_logits(batch_size=2 ,			length=UpperCAmelCase__)

           # tweak scores to not be uniform anymore
           A__							=   scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
           A__							=   scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

           # compute softmax
           A__							=   jax.nn.softmax(UpperCAmelCase__ ,			axis=-1)

           A__							=   FlaxTemperatureLogitsWarper(temperature=0.5)
           A__							=   FlaxTemperatureLogitsWarper(temperature=1.3)

           A__							=   jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase__ ,			scores.copy() ,			cur_len=UpperCAmelCase__) ,			axis=-1)
           A__							=   jax.nn.softmax(temp_dist_warper_smoother(UpperCAmelCase__ ,			scores.copy() ,			cur_len=UpperCAmelCase__) ,			axis=-1)

           # uniform distribution stays uniform
           self.assertTrue(jnp.allclose(probs[0, :] ,			warped_prob_sharp[0, :] ,			atol=1e-3))
           self.assertTrue(jnp.allclose(probs[0, :] ,			warped_prob_smooth[0, :] ,			atol=1e-3))

           # sharp peaks get higher, valleys get lower
           self.assertLess(probs[1, :].max() ,			warped_prob_sharp[1, :].max())
           self.assertGreater(probs[1, :].min() ,			warped_prob_sharp[1, :].min())

           # smooth peaks get lower, valleys get higher
           self.assertGreater(probs[1, :].max() ,			warped_prob_smooth[1, :].max())
           self.assertLess(probs[1, :].min() ,			warped_prob_smooth[1, :].min())


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

           A__							=   None
           A__							=   10
           A__							=   2

           # create ramp distribution
           A__							=   np.broadcast_to(np.arange(UpperCAmelCase__)[None, :] ,			(batch_size, vocab_size)).copy()
           A__							=   ramp_logits[1:, : vocab_size // 2] + vocab_size

           A__							=   FlaxTopKLogitsWarper(3)

           A__							=   top_k_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)

           # check that correct tokens are filtered
           self.assertListEqual(jnp.isinf(scores[0]).tolist() ,			7 * [True] + 3 * [False])
           self.assertListEqual(jnp.isinf(scores[1]).tolist() ,			2 * [True] + 3 * [False] + 5 * [True])

           # check special case
           A__							=   5
           A__							=   FlaxTopKLogitsWarper(top_k=1 ,			filter_value=0.0 ,			min_tokens_to_keep=3)

           A__							=   np.broadcast_to(np.arange(UpperCAmelCase__)[None, :] ,			(batch_size, length)).copy()
           A__							=   top_k_warp_safety_check(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)

           # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
           self.assertListEqual((scores == 0.0).sum(axis=-1).tolist() ,			[2, 2])


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->int:





           '''simple docstring'''

           A__							=   None
           A__							=   10
           A__							=   2

           # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
           A__							=   np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

           A__							=   FlaxTopPLogitsWarper(0.8)
           A__							=   np.exp(top_p_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__))

           # dist should be filtered to keep min num values so that sum is >= top_p
           # exp (-inf) => 0
           A__							=   np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
           self.assertTrue(np.allclose(UpperCAmelCase__ ,			UpperCAmelCase__ ,			atol=1e-3))

           # check edge cases with negative and extreme logits
           A__							=   np.broadcast_to(np.arange(UpperCAmelCase__)[None, :] ,			(batch_size, vocab_size)).copy() - (
               vocab_size // 2
           )

           # make ramp_logits more extreme
           A__							=   ramp_logits[1] * 100.0

           # make sure at least 2 tokens are kept
           A__							=   FlaxTopPLogitsWarper(0.9 ,			min_tokens_to_keep=2 ,			filter_value=0.0)
           A__							=   top_p_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)

           # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
           self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist() ,			[3, 2])


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->int:





           '''simple docstring'''

           A__							=   20
           A__							=   4
           A__							=   0

           A__							=   FlaxMinLengthLogitsProcessor(min_length=10 ,			eos_token_id=UpperCAmelCase__)

           # check that min length is applied at length 5
           A__							=   ids_tensor((batch_size, 20) ,			vocab_size=20)
           A__							=   5
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   min_dist_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,			4 * [-float('''inf''')])

           # check that min length is not applied anymore at length 15
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   15
           A__							=   min_dist_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[Any]:





           '''simple docstring'''

           A__							=   20
           A__							=   4
           A__							=   0

           A__							=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)

           # check that all scores are -inf except the bos_token_id score
           A__							=   ids_tensor((batch_size, 1) ,			vocab_size=20)
           A__							=   1
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   logits_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
           self.assertListEqual(scores[:, bos_token_id].tolist() ,			4 * [0])  # score for bos_token_id shold be zero

           # check that bos_token_id is not forced if current length is greater than 1
           A__							=   3
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   logits_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->List[str]:





           '''simple docstring'''

           A__							=   20
           A__							=   4
           A__							=   0
           A__							=   5

           A__							=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__)

           # check that all scores are -inf except the eos_token_id when max_length is reached
           A__							=   ids_tensor((batch_size, 4) ,			vocab_size=20)
           A__							=   4
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   logits_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
           self.assertListEqual(scores[:, eos_token_id].tolist() ,			4 * [0])  # score for eos_token_id should be zero

           # check that eos_token_id is not forced if max_length is not reached
           A__							=   3
           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   logits_processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           self.assertFalse(jnp.isinf(UpperCAmelCase__).any())


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   4
           A__							=   10
           A__							=   15
           A__							=   2
           A__							=   1
           A__							=   15

           # dummy input_ids and scores
           A__							=   ids_tensor((batch_size, sequence_length) ,			UpperCAmelCase__)
           A__							=   input_ids.copy()

           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   scores.copy()

           # instantiate all dist processors
           A__							=   FlaxTemperatureLogitsWarper(temperature=0.5)
           A__							=   FlaxTopKLogitsWarper(3)
           A__							=   FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           A__							=   FlaxMinLengthLogitsProcessor(min_length=10 ,			eos_token_id=UpperCAmelCase__)
           A__							=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)
           A__							=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__)

           A__							=   10

           # no processor list
           A__							=   temp_dist_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           A__							=   top_k_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           A__							=   top_p_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           A__							=   min_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           A__							=   bos_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
           A__							=   eos_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)

           # with processor list
           A__							=   FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
           A__							=   processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)

           # scores should be equal
           self.assertTrue(jnp.allclose(UpperCAmelCase__ ,			UpperCAmelCase__ ,			atol=1e-3))

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist() ,			input_ids_comp.tolist())





    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

           A__							=   4
           A__							=   10
           A__							=   15
           A__							=   2
           A__							=   1
           A__							=   15

           # dummy input_ids and scores
           A__							=   ids_tensor((batch_size, sequence_length) ,			UpperCAmelCase__)
           A__							=   input_ids.copy()

           A__							=   self._get_uniform_logits(UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   scores.copy()

           # instantiate all dist processors
           A__							=   FlaxTemperatureLogitsWarper(temperature=0.5)
           A__							=   FlaxTopKLogitsWarper(3)
           A__							=   FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           A__							=   FlaxMinLengthLogitsProcessor(min_length=10 ,			eos_token_id=UpperCAmelCase__)
           A__							=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__)
           A__							=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__)

           A__							=   10

           # no processor list
           def run_no_processor_list(UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[str]):
                  A__							=   temp_dist_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  A__							=   top_k_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  A__							=   top_p_warp(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  A__							=   min_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  A__							=   bos_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  A__							=   eos_dist_proc(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  return scores

           # with processor list
           def run_processor_list(UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Tuple):
                  A__							=   FlaxLogitsProcessorList(
                      [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
                  A__							=   processor(UpperCAmelCase__ ,			UpperCAmelCase__ ,			cur_len=UpperCAmelCase__)
                  return scores

           A__							=   jax.jit(UpperCAmelCase__)
           A__							=   jax.jit(UpperCAmelCase__)

           A__							=   jitted_run_no_processor_list(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   jitted_run_processor_list(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

           # scores should be equal
           self.assertTrue(jnp.allclose(UpperCAmelCase__ ,			UpperCAmelCase__ ,			atol=1e-3))

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist() ,			input_ids_comp.tolist())








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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Optional[Any]								=       {"""vocab_file""": """spiece.model"""}

_lowerCamelCase     :		str								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase     :		List[str]								=       {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES

    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str="<pad>" ,			UpperCAmelCase__				:     Optional[Any]="</s>" ,			UpperCAmelCase__				:     Any="<unk>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask_2>" ,			UpperCAmelCase__				:     List[str]="<mask_1>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[Any]=103 ,			UpperCAmelCase__				:     Optional[Dict[str, Any]] = None ,			**UpperCAmelCase__				:     Dict ,			)						->None:





           '''simple docstring'''

           A__							=   offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__							=   (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__) ,			self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__							=   additional_special_tokens_extended
           else:
                  A__							=   [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 ,			self.offset)]

           A__							=   {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token_sent=UpperCAmelCase__ ,			offset=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**UpperCAmelCase__ ,			)
           A__							=   mask_token_sent
           A__							=   vocab_file
           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__							=   {
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,			self.offset - 1)})

           A__							=   {v: k for k, v in self.encoder.items()}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__							=   {self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def __getstate__(      self				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.__dict__.copy()
           A__							=   None
           return state


    def __setstate__(      self				:     int ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__							=   d

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

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__ ,			out_type=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__							=   self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__							=   self.sp_model.IdToPiece(index - self.offset)
           return token


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           A__							=   []
           A__							=   ''''''
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__							=   []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int]=False)						->Union[str, Any]:





           '''simple docstring'''

           return 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List ,			UpperCAmelCase__				:     Optional[List] = None ,			UpperCAmelCase__				:     bool = False)						->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str]=None)						->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   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:
                         A__							=   self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)


