






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFLEDForConditionalGeneration, TFLEDModel





@require_tf
class 					A							:
   __UpperCAmelCase			:			Tuple              =      LEDConfig
   __UpperCAmelCase			:			int              =      {}
   __UpperCAmelCase			:			Union[str, Any]              =      "gelu"


   def __init__(self					:						Any	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Union[str, Any]=1_3	,  __UpperCAmelCase					:						List[Any]=7	,  __UpperCAmelCase					:						List[str]=True	,  __UpperCAmelCase					:						List[Any]=False	,  __UpperCAmelCase					:						Optional[int]=9_9	,  __UpperCAmelCase					:						Union[str, Any]=3_2	,  __UpperCAmelCase					:						Optional[Any]=2	,  __UpperCAmelCase					:						Optional[int]=4	,  __UpperCAmelCase					:						Optional[int]=3_7	,  __UpperCAmelCase					:						List[Any]=0.1	,  __UpperCAmelCase					:						List[str]=0.1	,  __UpperCAmelCase					:						List[Any]=2_0	,  __UpperCAmelCase					:						List[Any]=2	,  __UpperCAmelCase					:						str=1	,  __UpperCAmelCase					:						Optional[Any]=0	,  __UpperCAmelCase					:						Dict=4	,  )						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  seq_length
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size

         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  eos_token_id
         UpperCAmelCase__								=  pad_token_id
         UpperCAmelCase__								=  bos_token_id
         UpperCAmelCase__								=  attention_window

         # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
         # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
         # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
         # because its local attention only attends to `self.attention_window` and one before and one after
         UpperCAmelCase__								=  self.attention_window + 2

         # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
         # the `test_attention_outputs` and `test_hidden_states_output` tests
         UpperCAmelCase__								=  (
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )


   def       lowercase_				(self					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length - 1]	,  self.vocab_size		)
         UpperCAmelCase__								=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)	,  1		)
         UpperCAmelCase__								=  tf.concat([input_ids, eos_tensor]	,  axis=1		)

         UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

         UpperCAmelCase__								=  self.config_cls(
             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_ids=[2]	,  bos_token_id=self.bos_token_id	,  pad_token_id=self.pad_token_id	,  decoder_start_token_id=self.pad_token_id	,  attention_window=self.attention_window	,  **self.config_updates	,  )
         UpperCAmelCase__								=  prepare_led_inputs_dict(__A	,  __A	,  __A		)
         UpperCAmelCase__								=  tf.concat(
             [tf.zeros_like(__A		)[:, :-1], tf.ones_like(__A		)[:, -1:]]	,  axis=-1	,  )
         UpperCAmelCase__								=  global_attention_mask
         return config, inputs_dict





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						List[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  TFLEDModel(config=__A		).get_decoder()
         UpperCAmelCase__								=  inputs_dict["""input_ids"""]

         UpperCAmelCase__								=  input_ids[:1, :]
         UpperCAmelCase__								=  inputs_dict["""attention_mask"""][:1, :]
         UpperCAmelCase__								=  1

         # first forward pass
         UpperCAmelCase__								=  model(__A	,  attention_mask=__A	,  use_cache=__A		)

         UpperCAmelCase__								=  outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         UpperCAmelCase__								=  ids_tensor((self.batch_size, 3)	,  config.vocab_size		)
         UpperCAmelCase__								=  tf.cast(ids_tensor((self.batch_size, 3)	,  2		)	,  tf.inta		)

         # append to next input_ids and
         UpperCAmelCase__								=  tf.concat([input_ids, next_tokens]	,  axis=-1		)
         UpperCAmelCase__								=  tf.concat([attention_mask, next_attn_mask]	,  axis=-1		)

         UpperCAmelCase__								=  model(__A	,  attention_mask=__A		)[0]
         UpperCAmelCase__								=  model(__A	,  attention_mask=__A	,  past_key_values=__A		)[0]

         self.parent.assertEqual(next_tokens.shape[1]	,  output_from_past.shape[1]		)

         # select random slice
         UpperCAmelCase__								=  int(ids_tensor((1,)	,  output_from_past.shape[-1]		)		)
         UpperCAmelCase__								=  output_from_no_past[:, -3:, random_slice_idx]
         UpperCAmelCase__								=  output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(__A	,  __A	,  rtol=1E-3		)




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None,  __A=None,  __A=None,  )					->    Optional[Any]:







      '''simple docstring'''


      if attention_mask is None:
            UpperCAmelCase__								=  tf.cast(tf.math.not_equal(lowercase__,  config.pad_token_id							),  tf.inta							)
      if decoder_attention_mask is None:
            UpperCAmelCase__								=  tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape,  dtype=tf.inta							),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],  config.pad_token_id							),  tf.inta							),
                ],  axis=-1,  )
      if head_mask is None:
            UpperCAmelCase__								=  tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            UpperCAmelCase__								=  tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
      }





@require_tf
class 					A							(    A__		,    A__		,    unittest.TestCase					):
   __UpperCAmelCase			:			Union[str, Any]              =      (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   __UpperCAmelCase			:			Tuple              =      (TFLEDForConditionalGeneration,) if is_tf_available() else ()
   __UpperCAmelCase			:			Optional[int]              =      (
       {
           "conversational": TFLEDForConditionalGeneration,
           "feature-extraction": TFLEDModel,
           "summarization": TFLEDForConditionalGeneration,
           "text2text-generation": TFLEDForConditionalGeneration,
           "translation": TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase			:			int              =      True
   __UpperCAmelCase			:			Optional[Any]              =      False
   __UpperCAmelCase			:			Union[str, Any]              =      False
   __UpperCAmelCase			:			Optional[Any]              =      False


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFLEDModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__A		)


   def       lowercase_				(self					:						int		)						->    str:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def       lowercase_				(self					:						Union[str, Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__A		)


   def       lowercase_				(self					:						Any		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__								=  tf.zeros_like(inputs_dict["attention_mask"]		)
         UpperCAmelCase__								=  2
         UpperCAmelCase__								=  tf.where(
             tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices	,  1	,  inputs_dict["global_attention_mask"]	,  )

         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  self.model_tester.seq_length
         UpperCAmelCase__								=  self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(__UpperCAmelCase					:						int		):
               UpperCAmelCase__								=  outputs.decoder_attentions
               self.assertEqual(len(__A		)	,  self.model_tester.num_hidden_layers		)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:]		)	,  [self.model_tester.num_attention_heads, seq_length, seq_length]	,  )

         def check_encoder_attentions_output(__UpperCAmelCase					:						int		):
               UpperCAmelCase__								=  [t.numpy() for t in outputs.encoder_attentions]
               UpperCAmelCase__								=  [t.numpy() for t in outputs.encoder_global_attentions]
               self.assertEqual(len(__A		)	,  self.model_tester.num_hidden_layers		)
               self.assertEqual(len(__A		)	,  self.model_tester.num_hidden_layers		)
               self.assertListEqual(
                   list(attentions[0].shape[-3:]		)	,  [self.model_tester.num_attention_heads, seq_length, seq_length]	,  )
               self.assertListEqual(
                   list(global_attentions[0].shape[-3:]		)	,  [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]	,  )

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  True
               UpperCAmelCase__								=  False
               UpperCAmelCase__								=  False
               UpperCAmelCase__								=  model_class(__A		)
               UpperCAmelCase__								=  model(self._prepare_for_class(__A	,  __A		)		)
               UpperCAmelCase__								=  len(__A		)
               self.assertEqual(config.output_hidden_states	,  __A		)
               check_encoder_attentions_output(__A		)

               if self.is_encoder_decoder:
                     UpperCAmelCase__								=  model_class(__A		)
                     UpperCAmelCase__								=  model(self._prepare_for_class(__A	,  __A		)		)
                     self.assertEqual(config.output_hidden_states	,  __A		)
                     check_decoder_attentions_output(__A		)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               UpperCAmelCase__								=  True
               UpperCAmelCase__								=  model_class(__A		)
               UpperCAmelCase__								=  model(self._prepare_for_class(__A	,  __A		)		)
               self.assertEqual(config.output_hidden_states	,  __A		)
               check_encoder_attentions_output(__A		)

               # Check attention is always last and order is fine
               UpperCAmelCase__								=  True
               UpperCAmelCase__								=  True
               UpperCAmelCase__								=  model_class(__A		)
               UpperCAmelCase__								=  model(self._prepare_for_class(__A	,  __A		)		)

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,  len(__A		)		)
               self.assertEqual(model.config.output_hidden_states	,  __A		)
               check_encoder_attentions_output(__A		)


   @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."		)
   def       lowercase_				(self					:						Optional[Any]		)						->    str:
         """simple docstring"""



         pass





   def       lowercase_				(self					:						Dict		)						->    List[str]:
         """simple docstring"""



         pass




def        lowerCAmelCase_			(					__A							)					->    int:







      '''simple docstring'''


      return tf.constant(lowercase__,  dtype=tf.intaa							)


UpperCamelCase__    							= 1e-4







@slow
@require_tf
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						int		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		).led

         # change to intended input here
         UpperCAmelCase__								=  _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
         UpperCAmelCase__								=  _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
         UpperCAmelCase__								=  prepare_led_inputs_dict(model.config	,  __A	,  __A		)
         UpperCAmelCase__								=  model(**__A		)[0]
         UpperCAmelCase__								=  (1, 1_0_2_4, 7_6_8)
         self.assertEqual(output.shape	,  __A		)
         # change to expected output here
         UpperCAmelCase__								=  tf.convert_to_tensor(
             [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]	,  )
         tf.debugging.assert_near(output[:, :3, :3]	,  __A	,  atol=1E-3		)





   def       lowercase_				(self					:						Optional[Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		)

         # change to intended input here
         UpperCAmelCase__								=  _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
         UpperCAmelCase__								=  _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
         UpperCAmelCase__								=  prepare_led_inputs_dict(model.config	,  __A	,  __A		)
         UpperCAmelCase__								=  model(**__A		)[0]
         UpperCAmelCase__								=  (1, 1_0_2_4, model.config.vocab_size)
         self.assertEqual(output.shape	,  __A		)
         # change to expected output here
         UpperCAmelCase__								=  tf.convert_to_tensor(
             [[3_3.6_5_0_7, 6.4572, 1_6.8_0_8_9], [5.8739, -2.4238, 1_1.2_9_0_2], [-3.2139, -4.3149, 4.2783]]	,  )
         tf.debugging.assert_near(output[:, :3, :3]	,  __A	,  atol=1E-3	,  rtol=1E-3		)




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__    							= {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__    							= {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[str]              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			int              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			int              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			str              =      ['input_ids', 'attention_mask']
   __UpperCAmelCase			:			str              =      BartTokenizer


   def __init__(self					:						Tuple	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						Optional[int]=None	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						str="replace"	,  __UpperCAmelCase					:						List[str]="<s>"	,  __UpperCAmelCase					:						Tuple="</s>"	,  __UpperCAmelCase					:						List[Any]="</s>"	,  __UpperCAmelCase					:						Tuple="<s>"	,  __UpperCAmelCase					:						List[str]="<unk>"	,  __UpperCAmelCase					:						Optional[int]="<pad>"	,  __UpperCAmelCase					:						List[Any]="<mask>"	,  __UpperCAmelCase					:						Any=False	,  __UpperCAmelCase					:						str=True	,  **__UpperCAmelCase					:						Tuple	,  )						->    Any:
         """simple docstring"""



         super().__init__(
             __UpperCAmelCase	,  __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  errors=__UpperCAmelCase	,  bos_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  sep_token=__UpperCAmelCase	,  cls_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  add_prefix_space=__UpperCAmelCase	,  trim_offsets=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         UpperCAmelCase__								=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
         if pre_tok_state.get("add_prefix_space"	,  __UpperCAmelCase		) != add_prefix_space:
               UpperCAmelCase__								=  getattr(__UpperCAmelCase	,  pre_tok_state.pop("type"		)		)
               UpperCAmelCase__								=  add_prefix_space
               UpperCAmelCase__								=  pre_tok_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase__								=  "post_processor"
         UpperCAmelCase__								=  getattr(self.backend_tokenizer	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         if tokenizer_component_instance:
               UpperCAmelCase__								=  json.loads(tokenizer_component_instance.__getstate__()		)

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     UpperCAmelCase__								=  tuple(state["sep"]		)
               if "cls" in state:
                     UpperCAmelCase__								=  tuple(state["cls"]		)

               UpperCAmelCase__								=  False

               if state.get("add_prefix_space"	,  __UpperCAmelCase		) != add_prefix_space:
                     UpperCAmelCase__								=  add_prefix_space
                     UpperCAmelCase__								=  True

               if state.get("trim_offsets"	,  __UpperCAmelCase		) != trim_offsets:
                     UpperCAmelCase__								=  trim_offsets
                     UpperCAmelCase__								=  True

               if changes_to_apply:
                     UpperCAmelCase__								=  getattr(__UpperCAmelCase	,  state.pop("type"		)		)
                     UpperCAmelCase__								=  component_class(**__UpperCAmelCase		)
                     setattr(self.backend_tokenizer	,  __UpperCAmelCase	,  __UpperCAmelCase		)


   @property
   def       lowercase_				(self					:						Tuple		)						->    str:
         """simple docstring"""



         if self._mask_token is None:
               if self.verbose:
                     logger.error("Using mask_token, but it is not set yet."		)
               return None
         return str(self._mask_token		)


   @mask_token.setter
   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Union[str, Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else value
         UpperCAmelCase__								=  value


   def       lowercase_				(self					:						List[str]	,  *__UpperCAmelCase					:						str	,  **__UpperCAmelCase					:						List[str]		)						->    BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  __UpperCAmelCase		)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."		)

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


   def       lowercase_				(self					:						List[Any]	,  *__UpperCAmelCase					:						Optional[Any]	,  **__UpperCAmelCase					:						Optional[Any]		)						->    BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  __UpperCAmelCase		)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."		)

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


   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self._tokenizer.model.save(__UpperCAmelCase	,  name=__UpperCAmelCase		)
         return tuple(__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Dict=None		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List[int]	,  __UpperCAmelCase					:						Optional[List[int]] = None		)						->    List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [self.cls_token_id]

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



