




'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  Dict						,						lowercase__   :  List[str]						,						lowercase__   :  Any=13						,						lowercase__   :  List[str]=7						,						lowercase__   :  int=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  List[str]=99						,						lowercase__   :  int=32						,						lowercase__   :  List[str]=2						,						lowercase__   :  Union[str, Any]=4						,						lowercase__   :  Union[str, Any]=37						,						lowercase__   :  Union[str, Any]="gelu"						,						lowercase__   :  List[Any]=0.1						,						lowercase__   :  Optional[Any]=0.1						,						lowercase__   :  Tuple=512						,						lowercase__   :  Optional[Any]=16						,						lowercase__   :  str=2						,						lowercase__   :  Union[str, Any]=0.02						,						lowercase__   :  Union[str, Any]=3						,						lowercase__   :  int=4						,						lowercase__   :  Tuple=None						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  parent
           a_     :							Optional[Any]     						=  13
           a_     :							int     						=  7
           a_     :							Optional[Any]     						=  True
           a_     :							str     						=  True
           a_     :							Union[str, Any]     						=  True
           a_     :							Tuple     						=  True
           a_     :							List[Any]     						=  99
           a_     :							Optional[int]     						=  32
           a_     :							Tuple     						=  2
           a_     :							List[str]     						=  4
           a_     :							List[Any]     						=  37
           a_     :							Any     						=  """gelu"""
           a_     :							Any     						=  0.1
           a_     :							Any     						=  0.1
           a_     :							Tuple     						=  512
           a_     :							List[str]     						=  16
           a_     :							int     						=  2
           a_     :							List[Any]     						=  0.02
           a_     :							str     						=  3
           a_     :							Dict     						=  4
           a_     :							Union[str, Any]     						=  None






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

           a_     :							Union[str, Any]     						=  None
           if self.use_input_mask:
                a_     :							int     						=  random_attention_mask([self.batch_size, self.seq_length]       )

           a_     :							Dict     						=  None
           if self.use_token_type_ids:
                a_     :							Union[str, Any]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size       )

           a_     :							int     						=  None
           a_     :							Any     						=  None
           a_     :							str     						=  None
           if self.use_labels:
                a_     :							Dict     						=  ids_tensor([self.batch_size]						,						self.type_sequence_label_size       )
                a_     :							Optional[Any]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels       )
                a_     :							Union[str, Any]     						=  ids_tensor([self.batch_size]						,						self.num_choices       )

           a_     :							Union[str, Any]     						=  RoFormerConfig(
               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						,						initializer_range=self.initializer_range						,						return_dict=lowercase__						,						)

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






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Dict						,						lowercase__   :  int						,						lowercase__   :  str						,						lowercase__   :  Optional[int]						,						lowercase__   :  Any						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  TFRoFormerModel(config=lowercase__       )
           a_     :							Optional[int]     						=  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

           a_     :							Optional[int]     						=  [input_ids, input_mask]
           a_     :							Union[str, Any]     						=  model(lowercase__       )

           a_     :							str     						=  model(lowercase__       )

           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[int]						,						lowercase__   :  int						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  True
           a_     :							Optional[Any]     						=  TFRoFormerForCausalLM(config=lowercase__       )
           a_     :							List[Any]     						=  {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           a_     :							List[Any]     						=  model(lowercase__       )["""logits"""]
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape       )						,						[self.batch_size, self.seq_length, self.vocab_size]       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Tuple						,						lowercase__   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  Optional[int]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  TFRoFormerForMaskedLM(config=lowercase__       )
           a_     :							Optional[Any]     						=  {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           a_     :							Optional[Any]     						=  model(lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Any						,						lowercase__   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.num_labels
           a_     :							List[str]     						=  TFRoFormerForSequenceClassification(config=lowercase__       )
           a_     :							Optional[Any]     						=  {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           a_     :							Optional[int]     						=  model(lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.num_choices
           a_     :							Any     						=  TFRoFormerForMultipleChoice(config=lowercase__       )
           a_     :							Tuple     						=  tf.tile(tf.expand_dims(lowercase__						,						1       )						,						(1, self.num_choices, 1)       )
           a_     :							Dict     						=  tf.tile(tf.expand_dims(lowercase__						,						1       )						,						(1, self.num_choices, 1)       )
           a_     :							List[Any]     						=  tf.tile(tf.expand_dims(lowercase__						,						1       )						,						(1, self.num_choices, 1)       )
           a_     :							Tuple     						=  {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           a_     :							Optional[int]     						=  model(lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices)       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  Tuple						,						lowercase__   :  List[str]						,						lowercase__   :  Dict						,						lowercase__   :  Optional[int]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							int     						=  self.num_labels
           a_     :							str     						=  TFRoFormerForTokenClassification(config=lowercase__       )
           a_     :							Tuple     						=  {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           a_     :							Any     						=  model(lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  int						,						lowercase__   :  List[str]						,						lowercase__   :  List[Any]						,						lowercase__   :  int						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							List[str]     						=  TFRoFormerForQuestionAnswering(config=lowercase__       )
           a_     :							Dict     						=  {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           a_     :							Dict     						=  model(lowercase__       )
           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 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							int     						=  self.prepare_config_and_inputs()
           (
               (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     
           )     :							List[Any]     						=  config_and_inputs
           a_     :							int     						=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict







@require_tf
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       str   							=					(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __magic_name__  :       Any   							=					(
          {
              '''feature-extraction''': TFRoFormerModel,
              '''fill-mask''': TFRoFormerForMaskedLM,
              '''question-answering''': TFRoFormerForQuestionAnswering,
              '''text-classification''': TFRoFormerForSequenceClassification,
              '''text-generation''': TFRoFormerForCausalLM,
              '''token-classification''': TFRoFormerForTokenClassification,
              '''zero-shot''': TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       List[Any]   							=					False






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  str						,						lowercase__   :  List[str]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

           return False






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  TFRoFormerModelTester(self       )
           a_     :							Optional[Any]     						=  ConfigTester(self						,						config_class=lowercase__						,						hidden_size=37       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							int     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowercase__       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowercase__       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowercase__       )






      @slow
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""       )
           self.assertIsNotNone(lowercase__       )







@require_tf
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      @slow
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""       )
           a_     :							Optional[Any]     						=  tf.constant([[0, 1, 2, 3, 4, 5]]       )
           a_     :							Optional[int]     						=  model(lowercase__       )[0]

           # TODO Replace vocab size
           a_     :							Optional[int]     						=  5_0000

           a_     :							Any     						=  [1, 6, vocab_size]
           self.assertEqual(output.shape						,						lowercase__       )

           print(output[:, :3, :3]       )

           # TODO Replace values below with what was printed above.
           a_     :							Any     						=  tf.constant(
               [
                   [
                       [-0.12053341, -1.0264901, 0.29221946],
                       [-1.5133783, 0.197433, 0.15190607],
                       [-5.0135403, -3.900256, -0.84038764],
                   ]
               ]       )
           tf.debugging.assert_near(output[:, :3, :3]						,						lowercase__						,						atol=1e-4       )







@require_tf
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):
      __magic_name__  :       str   							=					1E-4






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  tf.constant([[4, 10]]       )
           a_     :							Dict     						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,						embedding_dim=6       )

           a_     :							Optional[int]     						=  emba(input_ids.shape       )
           a_     :							Dict     						=  tf.constant(
               [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]       )

           tf.debugging.assert_near(lowercase__						,						lowercase__						,						atol=self.tolerance       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  tf.constant(
               [
                   [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                   [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                   [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
               ]       )
           a_     :							str     						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,						embedding_dim=512       )
           emba([2, 16, 512]       )
           a_     :							Dict     						=  emba.weight[:3, :5]

           tf.debugging.assert_near(lowercase__						,						lowercase__						,						atol=self.tolerance       )







@require_tf
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):
      __magic_name__  :       Optional[Any]   							=					1E-4






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Any     						=  tf.reshape(tf.range(2 * 12 * 16 * 64						,						dtype=tf.floataa       )						,						shape=(2, 12, 16, 64)       ) / 100

           a_     :							str     						=  -tf.reshape(tf.range(2 * 12 * 16 * 64						,						dtype=tf.floataa       )						,						shape=(2, 12, 16, 64)       ) / 100

           a_     :							Any     						=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=32						,						embedding_dim=64       )
           a_     :							List[str]     						=  embed_positions([2, 16, 768]       )[None, None, :, :]

           a_						,     a_     :							str     						=  TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               lowercase__						,						lowercase__						,						lowercase__       )

           a_     :							Optional[int]     						=  tf.constant(
               [
                   [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                   [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                   [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                   [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                   [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                   [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
               ]       )
           a_     :							Any     						=  tf.constant(
               [
                   [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                   [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                   [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                   [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                   [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                   [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
               ]       )

           tf.debugging.assert_near(query_layer[0, 0, :6, :8]						,						lowercase__						,						atol=self.tolerance       )
           tf.debugging.assert_near(key_layer[0, 0, :6, :8]						,						lowercase__						,						atol=self.tolerance       )






'''simple docstring'''


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCAmelCase_      :  int				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Optional[Any]				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  List[str]				     =  {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_      :  Tuple				     =  {
    'allenai/led-base-16384': 1_6384,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					VOCAB_FILES_NAMES
      __magic_name__  :       str   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[int]   							=					LEDTokenizer
      __magic_name__  :       Optional[int]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  str						,						lowercase__   :  int=None						,						lowercase__   :  Any=None						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  List[Any]="replace"						,						lowercase__   :  Optional[Any]="<s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  List[str]="<s>"						,						lowercase__   :  str="<unk>"						,						lowercase__   :  str="<pad>"						,						lowercase__   :  str="<mask>"						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Optional[int]=True						,						**lowercase__   :  str						,						):


           '''simple docstring'''

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

           a_     :							str     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							Optional[int]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							List[str]     						=  add_prefix_space
                a_     :							Union[str, Any]     						=  pre_tok_class(**lowercase__       )

           a_     :							Union[str, Any]     						=  add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           a_     :							Any     						=  """post_processor"""
           a_     :							int     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[int]     						=  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:
                     a_     :							int     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							Optional[int]     						=  tuple(state["""cls"""]       )

                a_     :							List[Any]     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Optional[Any]     						=  add_prefix_space
                     a_     :							Dict     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							List[Any]     						=  trim_offsets
                     a_     :							Optional[Any]     						=  True

                if changes_to_apply:
                     a_     :							Optional[int]     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Any     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 							lowercase_						( self   :  Dict       ):


           '''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   :  List[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Optional[int]     						=  value






      def 							lowercase_						( self   :  Optional[int]						,						*lowercase__   :  Any						,						**lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           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(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]						,						*lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           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(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  [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   :  List[Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  [self.sep_token_id]
           a_     :							int     						=  [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]






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[Dict[str, EncodedInput], BatchEncoding]						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[bool] = None						,						):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  super()._pad(
               encoded_inputs=lowercase__						,						max_length=lowercase__						,						padding_strategy=lowercase__						,						pad_to_multiple_of=lowercase__						,						return_attention_mask=lowercase__						,						)

           # Load from model defaults
           if return_attention_mask is None:
                a_     :							Tuple     						=  """attention_mask""" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                a_     :							Tuple     						=  encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                a_     :							Optional[int]     						=  len(encoded_inputs["""global_attention_mask"""]       ) != len(lowercase__       )

                if needs_to_be_padded:
                     a_     :							int     						=  len(lowercase__       ) - len(encoded_inputs["""global_attention_mask"""]       )

                     if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          a_     :							Optional[int]     						=  (
                              encoded_inputs["""global_attention_mask"""] + [-1] * difference
                          )
                     elif self.padding_side == "left":
                          a_     :							Dict     						=  [-1] * difference + encoded_inputs[
                              """global_attention_mask"""
                          ]
                     else:
                          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

           return encoded_inputs
