
'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  a_     =  "platform"

  import jax
  import jax.numpy as jnp

  from transformers import AutoTokenizer
  from transformers.models.mbart.modeling_flax_mbart import (
      FlaxMBartForConditionalGeneration,
      FlaxMBartForQuestionAnswering,
      FlaxMBartForSequenceClassification,
      FlaxMBartModel,
      shift_tokens_right,
  )

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						):
    """simple docstring"""






    if attention_mask is None:
        snake_case_	:      int            =  np.where(input_ids != config.pad_token_id,						1,						0    )
    if decoder_attention_mask is None:
        snake_case_	:      Any            =  np.where(decoder_input_ids != config.pad_token_id,						1,						0    )
    if head_mask is None:
        snake_case_	:      Optional[Any]            =  np.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        snake_case_	:      Optional[Any]            =  np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        snake_case_	:      Union[str, Any]            =  np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }






class 				UpperCAmelCase_			(		unittest.TestCase   ):





 def __init__(      self	,    lowercase_	,    lowercase_=13	,    lowercase_=7	,    lowercase_=True	,    lowercase_=False	,    lowercase_=99	,    lowercase_=16	,    lowercase_=2	,    lowercase_=4	,    lowercase_=4	,    lowercase_="gelu"	,    lowercase_=0.1	,    lowercase_=0.1	,    lowercase_=32	,    lowercase_=2	,    lowercase_=1	,    lowercase_=0	,    lowercase_=2	,    lowercase_=0.02	,    ):
     snake_case_	:      List[str]            =  parent
     snake_case_	:      Optional[int]            =  batch_size
     snake_case_	:      Union[str, Any]            =  seq_length
     snake_case_	:      List[str]            =  is_training
     snake_case_	:      Tuple            =  use_labels
     snake_case_	:      Optional[Any]            =  vocab_size
     snake_case_	:      Any            =  hidden_size
     snake_case_	:      Dict            =  num_hidden_layers
     snake_case_	:      Optional[int]            =  num_attention_heads
     snake_case_	:      str            =  intermediate_size
     snake_case_	:      List[Any]            =  hidden_act
     snake_case_	:      Union[str, Any]            =  hidden_dropout_prob
     snake_case_	:      Optional[int]            =  attention_probs_dropout_prob
     snake_case_	:      List[Any]            =  max_position_embeddings
     snake_case_	:      List[str]            =  eos_token_id
     snake_case_	:      Any            =  pad_token_id
     snake_case_	:      int            =  bos_token_id
     snake_case_	:      List[str]            =  decoder_start_token_id
     snake_case_	:      List[Any]            =  initializer_range





 def     snake_case__						(      self):
     snake_case_	:      List[str]            =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size)	,    3	,    self.vocab_size)
     snake_case_	:      Tuple            =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa))	,    -1)

     snake_case_	:      Dict            =  shift_tokens_right(lowercase_	,    1)

     snake_case_	:      int            =  MBartConfig(
         vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_id=self.eos_token_id	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    decoder_start_token_id=self.decoder_start_token_id	,    initializer_range=self.initializer_range	,    use_cache=lowercase_	,    )
     snake_case_	:      Dict            =  prepare_mbart_inputs_dict(lowercase_	,    lowercase_	,    lowercase_)
     return config, inputs_dict





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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     snake_case_	:      Union[str, Any]            =  20
     snake_case_	:      List[Any]            =  model_class_name(lowercase_)

     snake_case_	:      List[str]            =  model.encode(inputs_dict["input_ids"])

     snake_case_     ,	snake_case_	:      Optional[Any]            =  (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     snake_case_	:      Any            =  model.init_cache(decoder_input_ids.shape[0]	,    lowercase_	,    lowercase_)
     snake_case_	:      Union[str, Any]            =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype="i4")

     snake_case_	:      Optional[Any]            =  jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
     snake_case_	:      Tuple            =  model.decode(
         decoder_input_ids[:, :-1]	,    lowercase_	,    decoder_attention_mask=lowercase_	,    past_key_values=lowercase_	,    decoder_position_ids=lowercase_	,    )

     snake_case_	:      int            =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype="i4")
     snake_case_	:      List[str]            =  model.decode(
         decoder_input_ids[:, -1:]	,    lowercase_	,    decoder_attention_mask=lowercase_	,    past_key_values=outputs_cache.past_key_values	,    decoder_position_ids=lowercase_	,    )

     snake_case_	:      Union[str, Any]            =  model.decode(lowercase_	,    lowercase_)

     snake_case_	:      Optional[int]            =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
     self.parent.assertTrue(diff < 1E-3	,    msg=F'Max diff is {diff}')





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     snake_case_	:      List[Any]            =  20
     snake_case_	:      Optional[Any]            =  model_class_name(lowercase_)

     snake_case_	:      Tuple            =  model.encode(inputs_dict["input_ids"])

     snake_case_     ,	snake_case_	:      Dict            =  (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     snake_case_	:      Dict            =  jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
         ]	,    axis=-1	,    )

     snake_case_	:      Any            =  model.init_cache(decoder_input_ids.shape[0]	,    lowercase_	,    lowercase_)
     snake_case_	:      List[str]            =  jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )

     snake_case_	:      int            =  model.decode(
         decoder_input_ids[:, :-1]	,    lowercase_	,    decoder_attention_mask=lowercase_	,    past_key_values=lowercase_	,    decoder_position_ids=lowercase_	,    )
     snake_case_	:      Tuple            =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype="i4")
     snake_case_	:      str            =  model.decode(
         decoder_input_ids[:, -1:]	,    lowercase_	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=lowercase_	,    decoder_position_ids=lowercase_	,    )

     snake_case_	:      str            =  model.decode(lowercase_	,    lowercase_	,    decoder_attention_mask=lowercase_)

     snake_case_	:      int            =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
     self.parent.assertTrue(diff < 1E-3	,    msg=F'Max diff is {diff}')






@require_flax
class 				UpperCAmelCase_			(		unittest.TestCase   ):
 UpperCAmelCase_						      =  9_9





 def     snake_case__						(      self):
     snake_case_	:      Optional[Any]            =  np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]	,    dtype=np.intaa	,    )

     snake_case_	:      Optional[Any]            =  input_ids.shape[0]
     snake_case_	:      List[str]            =  MBartConfig(
         vocab_size=self.vocab_size	,    d_model=24	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=32	,    decoder_ffn_dim=32	,    max_position_embeddings=48	,    eos_token_id=2	,    pad_token_id=1	,    bos_token_id=0	,    )
     return config, input_ids, batch_size





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_     ,	snake_case_	:      Union[str, Any]            =  self._get_config_and_data()
     snake_case_	:      List[str]            =  FlaxMBartForSequenceClassification(lowercase_)
     snake_case_	:      List[Any]            =  model(input_ids=lowercase_	,    decoder_input_ids=lowercase_)
     snake_case_	:      List[Any]            =  (batch_size, config.num_labels)
     self.assertEqual(outputs["logits"].shape	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_     ,	snake_case_	:      List[str]            =  self._get_config_and_data()
     snake_case_	:      Tuple            =  FlaxMBartForQuestionAnswering(lowercase_)
     snake_case_	:      List[Any]            =  model(input_ids=lowercase_)

     self.assertEqual(outputs["start_logits"].shape	,    input_ids.shape)
     self.assertEqual(outputs["end_logits"].shape	,    input_ids.shape)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_     ,	snake_case_	:      Dict            =  self._get_config_and_data()
     snake_case_	:      Optional[int]            =  FlaxMBartForConditionalGeneration(lowercase_)
     snake_case_	:      str            =  lm_model(input_ids=lowercase_)
     snake_case_	:      Optional[int]            =  (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_	:      int            =  MBartConfig(
         vocab_size=self.vocab_size	,    d_model=14	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=8	,    decoder_ffn_dim=8	,    max_position_embeddings=48	,    )
     snake_case_	:      Optional[Any]            =  FlaxMBartForConditionalGeneration(lowercase_)
     snake_case_	:      List[str]            =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,    dtype=np.intaa)
     snake_case_	:      Optional[Any]            =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,    dtype=np.intaa)
     snake_case_	:      Any            =  lm_model(input_ids=lowercase_	,    decoder_input_ids=lowercase_)
     snake_case_	:      Union[str, Any]            =  (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_	:      str            =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,    dtype=np.intaa)
     snake_case_	:      Any            =  shift_tokens_right(lowercase_	,    1)
     snake_case_	:      Dict            =  np.equal(lowercase_	,    1).astype(np.floataa).sum()
     snake_case_	:      Any            =  np.equal(lowercase_	,    1).astype(np.floataa).sum()
     self.assertEqual(shifted.shape	,    input_ids.shape)
     self.assertEqual(lowercase_	,    n_pad_before - 1)
     self.assertTrue(np.equal(shifted[:, 0]	,    2).all())






@require_flax
class 				UpperCAmelCase_			(		snake_case__      ,  unittest.TestCase      ,  snake_case__   ):
 UpperCAmelCase_						      =  True
 UpperCAmelCase_						      =  (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 UpperCAmelCase_						      =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





 def     snake_case__						(      self):
     snake_case_	:      str            =  FlaxMBartModelTester(self)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      int            =  self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(lowercase_	,    lowercase_	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      Any            =  self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_	,    lowercase_	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      Optional[Any]            =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__):
             snake_case_	:      Dict            =  self._prepare_for_class(lowercase_	,    lowercase_)
             snake_case_	:      Tuple            =  model_class(lowercase_)

             @jax.jit
             def encode_jitted(lowercase_	,    lowercase_=None	,    **lowercase_):
                 return model.encode(input_ids=lowercase_	,    attention_mask=lowercase_)

             with self.subTest("JIT Enabled"):
                 snake_case_	:      int            =  encode_jitted(**lowercase_).to_tuple()

             with self.subTest("JIT Disabled"):
                 with jax.disable_jit():
                     snake_case_	:      str            =  encode_jitted(**lowercase_).to_tuple()

             self.assertEqual(len(lowercase_)	,    len(lowercase_))
             for jitted_output, output in zip(lowercase_	,    lowercase_):
                 self.assertEqual(jitted_output.shape	,    output.shape)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      Optional[Any]            =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__):
             snake_case_	:      Optional[int]            =  model_class(lowercase_)
             snake_case_	:      int            =  model.encode(inputs_dict["input_ids"]	,    inputs_dict["attention_mask"])

             snake_case_	:      Any            =  {
                 "decoder_input_ids": inputs_dict["decoder_input_ids"],
                 "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                 "encoder_outputs": encoder_outputs,
             }

             @jax.jit
             def decode_jitted(lowercase_	,    lowercase_	,    lowercase_):
                 return model.decode(
                     decoder_input_ids=lowercase_	,    decoder_attention_mask=lowercase_	,    encoder_outputs=lowercase_	,    )

             with self.subTest("JIT Enabled"):
                 snake_case_	:      str            =  decode_jitted(**lowercase_).to_tuple()

             with self.subTest("JIT Disabled"):
                 with jax.disable_jit():
                     snake_case_	:      str            =  decode_jitted(**lowercase_).to_tuple()

             self.assertEqual(len(lowercase_)	,    len(lowercase_))
             for jitted_output, output in zip(lowercase_	,    lowercase_):
                 self.assertEqual(jitted_output.shape	,    output.shape)





 @slow
 def     snake_case__						(      self):
     for model_class_name in self.all_model_classes:
         snake_case_	:      Tuple            =  model_class_name.from_pretrained("facebook/mbart-large-cc25"	,    from_pt=lowercase_)
         # FlaxMBartForSequenceClassification expects eos token in input_ids
         snake_case_	:      Union[str, Any]            =  np.ones((1, 1)) * model.config.eos_token_id
         snake_case_	:      List[Any]            =  model(lowercase_)
         self.assertIsNotNone(lowercase_)






@require_flax
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase_			(		unittest.TestCase   ):
 UpperCAmelCase_						      =  [
     """ UN Chief Says There Is No Military Solution in Syria""",
 ]
 UpperCAmelCase_						      =  [
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
 ]
 UpperCAmelCase_						      =  """facebook/mbart-large-en-ro"""





 @cached_property
 def     snake_case__						(      self):
     return AutoTokenizer.from_pretrained(self.model_name)





 @cached_property
 def     snake_case__						(      self):
     snake_case_	:      Optional[Any]            =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name	,    from_pt=lowercase_)
     return model





 def     snake_case__						(      self	,    **lowercase_):
     snake_case_	:      Optional[Any]            =  self.translate_src_text(**lowercase_)
     self.assertListEqual(self.expected_text	,    lowercase_)





 def     snake_case__						(      self	,    **lowercase_):
     snake_case_	:      Dict            =  self.tokenizer(self.src_text	,    **lowercase_	,    return_tensors="np")
     snake_case_	:      Tuple            =  self.model.generate(
         model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]	,    early_stopping=lowercase_	,    num_beams=2	,    ).sequences
     snake_case_	:      int            =  self.tokenizer.batch_decode(lowercase_	,    skip_special_tokens=lowercase_)
     return generated_words





 @slow
 def     snake_case__						(      self):
     self._assert_generated_batch_equal_expected()






'''simple docstring'''
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_     =  logging.get_logger(__name__)






class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  ["""input_ids""", """attention_mask"""]





 def __init__(      self	,    lowercase_="[PAD]"	,    lowercase_="[BOS]"	,    lowercase_="[EOS]"	,    lowercase_="[MASK]"	,    lowercase_="[CLS]"	,    lowercase_="[SEP]"	,    lowercase_=20_48	,    **lowercase_	,    ):
     snake_case_	:      int            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else pad_token
     snake_case_	:      List[str]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else bos_token
     snake_case_	:      Tuple            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else eos_token
     snake_case_	:      Optional[Any]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else mask_token
     snake_case_	:      List[str]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else cls_token
     snake_case_	:      int            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else sep_token

     super().__init__(
         pad_token=lowercase_	,    bos_token=lowercase_	,    eos_token=lowercase_	,    mask_token=lowercase_	,    cls_token=lowercase_	,    sep_token=lowercase_	,    model_max_length=lowercase_	,    **lowercase_	,    )

     snake_case_	:      Dict            =  2**8  # utf is 8 bits

     # define special tokens dict
     snake_case_	:      Dict[str, int]            =  {
         self.pad_token: 0,
         self.bos_token: 1,
         self.eos_token: 2,
         self.mask_token: 3,
         self.cls_token: 4,
         self.sep_token: 5,
     }
     snake_case_	:      Dict            =  len(self.special_tokens_encoder)
     snake_case_	:      Dict[int, str]            =  {v: k for k, v in self.special_tokens_encoder.items()}





 def     snake_case__						(      self):
     snake_case_	:      str            =  self.special_tokens_encoder.copy()
     vocab.update(self.added_tokens_encoder)
     for i in range(self._utf_vocab_size):
         snake_case_	:      Tuple            =  chr(lowercase_)
         snake_case_	:      Optional[Any]            =  i + len(self.special_tokens_encoder)
     return vocab





 @property
 def     snake_case__						(      self):
     return self._utf_vocab_size + self._num_special_tokens





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

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     else:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Tuple            =  [chr(lowercase_) for i in text.encode("utf-8")]
     return tokens





 def     snake_case__						(      self	,    lowercase_):
     if token in self.special_tokens_encoder:
         snake_case_	:      Optional[int]            =  self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
         snake_case_	:      str            =  self.added_tokens_encoder[token]
     elif len(lowercase_) != 1:
         snake_case_	:      Union[str, Any]            =  self.unk_token_id
     else:
         snake_case_	:      Optional[Any]            =  ord(lowercase_) + self._num_special_tokens
     return token_id





 def     snake_case__						(      self	,    lowercase_):
     if index in self.special_tokens_decoder:
         snake_case_	:      Optional[Any]            =  self.special_tokens_decoder[index]
     elif index in self.added_tokens_decoder:
         snake_case_	:      List[str]            =  self.added_tokens_decoder[index]
     else:
         snake_case_	:      Union[str, Any]            =  chr(index - self._num_special_tokens)
     return token





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      str            =  b""
     for token in tokens:
         if token in self.special_tokens_decoder:
             snake_case_	:      List[Any]            =  self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.added_tokens_decoder:
             snake_case_	:      Any            =  self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.special_tokens_encoder:
             snake_case_	:      Union[str, Any]            =  token.encode("utf-8")
         elif token in self.added_tokens_encoder:
             snake_case_	:      Any            =  token.encode("utf-8")
         else:
             snake_case_	:      Union[str, Any]            =  bytes([ord(lowercase_)])
         bstring += tok_string
     snake_case_	:      Union[str, Any]            =  bstring.decode("utf-8"	,    errors="replace")
     return string





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     return ()




