





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 							__lowercase			:






      def __init__(		self			,							lowercase_			,							lowercase_=1_3			,							lowercase_=7			,							lowercase_=True			,							lowercase_=True			,							lowercase_=True			,							lowercase_=True			,							lowercase_=9_9			,							lowercase_=3_2			,							lowercase_=2			,							lowercase_=4			,							lowercase_=3_7			,							lowercase_="gelu"			,							lowercase_=0.1			,							lowercase_=0.1			,							lowercase_=5_1_2			,							lowercase_=1_6			,							lowercase_=2			,							lowercase_=0.02			,							lowercase_=3			,							lowercase_=4			,							lowercase_=None			,							)     ->       str:
       __snake_case         =			parent
       __snake_case         =			1_3
       __snake_case         =			7
       __snake_case         =			True
       __snake_case         =			True
       __snake_case         =			True
       __snake_case         =			True
       __snake_case         =			9_9
       __snake_case         =			3_2
       __snake_case         =			2
       __snake_case         =			4
       __snake_case         =			3_7
       __snake_case         =			"""gelu"""
       __snake_case         =			0.1
       __snake_case         =			0.1
       __snake_case         =			5_1_2
       __snake_case         =			1_6
       __snake_case         =			2
       __snake_case         =			0.02
       __snake_case         =			3
       __snake_case         =			4
       __snake_case         =			None






      def 			_a						(		self)     ->       str:
       __snake_case         =			ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size)

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

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

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

       __snake_case         =			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 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       int:
       __snake_case         =			TFRoFormerModel(config=lowercase__)
       __snake_case         =			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

       __snake_case         =			[input_ids, input_mask]
       __snake_case         =			model(lowercase__)

       __snake_case         =			model(lowercase__)

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






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       int:
       __snake_case         =			True
       __snake_case         =			TFRoFormerForCausalLM(config=lowercase__)
       __snake_case         =			{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __snake_case         =			model(lowercase__)["""logits"""]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape)			,							[self.batch_size, self.seq_length, self.vocab_size])






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       List[Any]:
       __snake_case         =			TFRoFormerForMaskedLM(config=lowercase__)
       __snake_case         =			{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __snake_case         =			model(lowercase__)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       List[Any]:
       __snake_case         =			self.num_labels
       __snake_case         =			TFRoFormerForSequenceClassification(config=lowercase__)
       __snake_case         =			{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

       __snake_case         =			model(lowercase__)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       int:
       __snake_case         =			self.num_choices
       __snake_case         =			TFRoFormerForMultipleChoice(config=lowercase__)
       __snake_case         =			tf.tile(tf.expand_dims(lowercase__			,							1)			,							(1, self.num_choices, 1))
       __snake_case         =			tf.tile(tf.expand_dims(lowercase__			,							1)			,							(1, self.num_choices, 1))
       __snake_case         =			tf.tile(tf.expand_dims(lowercase__			,							1)			,							(1, self.num_choices, 1))
       __snake_case         =			{
           """input_ids""": multiple_choice_inputs_ids,
           """attention_mask""": multiple_choice_input_mask,
           """token_type_ids""": multiple_choice_token_type_ids,
       }
       __snake_case         =			model(lowercase__)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_choices))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       int:
       __snake_case         =			self.num_labels
       __snake_case         =			TFRoFormerForTokenClassification(config=lowercase__)
       __snake_case         =			{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __snake_case         =			model(lowercase__)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.num_labels))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Union[str, Any]:
       __snake_case         =			TFRoFormerForQuestionAnswering(config=lowercase__)
       __snake_case         =			{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

       __snake_case         =			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 			_a						(		self)     ->       Union[str, Any]:
       __snake_case         =			self.prepare_config_and_inputs()
       (
           __snake_case     
       )         =			config_and_inputs
       __snake_case         =			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_tf
class 							__lowercase			(     snake_case_   ,							snake_case_   ,							unittest.TestCase       ):
      __UpperCAmelCase		      =						(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase		      =						(
          {
              '''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 {}
      )

      __UpperCAmelCase		      =						False
      __UpperCAmelCase		      =						False






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Tuple:

       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False






      def 			_a						(		self)     ->       List[Any]:
       __snake_case         =			TFRoFormerModelTester(self)
       __snake_case         =			ConfigTester(self			,							config_class=lowercase__			,							hidden_size=3_7)






      def 			_a						(		self)     ->       Dict:
       self.config_tester.run_common_tests()






      def 			_a						(		self)     ->       Union[str, Any]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase__)






      def 			_a						(		self)     ->       str:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowercase__)






      def 			_a						(		self)     ->       Optional[int]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*lowercase__)






      def 			_a						(		self)     ->       int:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowercase__)






      def 			_a						(		self)     ->       Tuple:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase__)






      def 			_a						(		self)     ->       Tuple:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase__)






      def 			_a						(		self)     ->       Any:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowercase__)







      @slow
      def 			_a						(		self)     ->       Any:
       __snake_case         =			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
       self.assertIsNotNone(lowercase__)






@require_tf
class 							__lowercase			(     unittest.TestCase       ):






      @slow
      def 			_a						(		self)     ->       Optional[Any]:
       __snake_case         =			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
       __snake_case         =			tf.constant([[0, 1, 2, 3, 4, 5]])
       __snake_case         =			model(lowercase__)[0]

       # TODO Replace vocab size
       __snake_case         =			5_0_0_0_0

       __snake_case         =			[1, 6, vocab_size]
       self.assertEqual(output.shape			,							lowercase__)

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

       # TODO Replace values below with what was printed above.
       __snake_case         =			tf.constant(
           [
               [
                   [-0.1205_3341, -1.026_4901, 0.2922_1946],
                   [-1.513_3783, 0.19_7433, 0.1519_0607],
                   [-5.013_5403, -3.90_0256, -0.8403_8764],
               ]
           ])
       tf.debugging.assert_near(output[:, :3, :3]			,							lowercase__			,							atol=1e-4)






@require_tf
class 							__lowercase			(     unittest.TestCase       ):
      __UpperCAmelCase		      =						1e-4






      def 			_a						(		self)     ->       Dict:
       __snake_case         =			tf.constant([[4, 1_0]])
       __snake_case         =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6			,							embedding_dim=6)

       __snake_case         =			emba(input_ids.shape)
       __snake_case         =			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 			_a						(		self)     ->       Optional[int]:
       __snake_case         =			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],
           ])
       __snake_case         =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2			,							embedding_dim=5_1_2)
       emba([2, 1_6, 5_1_2])
       __snake_case         =			emba.weight[:3, :5]

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






@require_tf
class 							__lowercase			(     unittest.TestCase       ):
      __UpperCAmelCase		      =						1e-4






      def 			_a						(		self)     ->       Any:
       __snake_case         =			tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4			,							dtype=tf.floataa)			,							shape=(2, 1_2, 1_6, 6_4)) / 1_0_0

       __snake_case         =			-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4			,							dtype=tf.floataa)			,							shape=(2, 1_2, 1_6, 6_4)) / 1_0_0

       __snake_case         =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2			,							embedding_dim=6_4)
       __snake_case         =			embed_positions([2, 1_6, 7_6_8])[None, None, :, :]

       __snake_case         =			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           lowercase__			,							lowercase__			,							lowercase__)

       __snake_case         =			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],
           ])
       __snake_case         =			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
