'''simple docstring'''





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import BertGenerationDecoder, BertGenerationEncoder






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=50    ,							lowercase__=0.0_2    ,							lowercase__=True    ,							lowercase__=None    ,							)							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       parent
         SCREAMING_SNAKE_CASE					:			Tuple    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Dict    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			List[str]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[str]    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			str    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			str    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			str    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scope






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			Dict    	=       random_attention_mask([self.batch_size, self.seq_length] )

         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			Dict    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_config()

         return config, input_ids, input_mask, token_labels






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return BertGenerationConfig(
             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    ,							is_decoder=lowercase__    ,							initializer_range=self.initializer_range    ,							)






    def       _UpperCamelCase    (       self )							-> str:
         (
             (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   
         )					:			List[Any]    	=       self.prepare_config_and_inputs()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True
         SCREAMING_SNAKE_CASE					:			List[str]    	=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2 )

         return (
             config,
             input_ids,
             input_mask,
             token_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BertGenerationEncoder(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							attention_mask=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__ )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       True
         SCREAMING_SNAKE_CASE					:			List[str]    	=       BertGenerationEncoder(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			int    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							)
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       True
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       True
         SCREAMING_SNAKE_CASE					:			Tuple    	=       BertGenerationDecoder(config=lowercase__ ).to(lowercase__ ).eval()

         # first forward pass
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							use_cache=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ids_tensor((self.batch_size, 3)    ,							config.vocab_size )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ids_tensor((self.batch_size, 3)    ,							vocab_size=2 )

         # append to next input_ids and
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.cat([input_ids, next_tokens]    ,							dim=-1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat([input_mask, next_mask]    ,							dim=-1 )

         SCREAMING_SNAKE_CASE					:			Any    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							output_hidden_states=lowercase__    ,							)['hidden_states'][0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							past_key_values=lowercase__    ,							output_hidden_states=lowercase__    ,							)['hidden_states'][0]

         # select random slice
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ids_tensor((1,)    ,							output_from_past.shape[-1] ).item()
         SCREAMING_SNAKE_CASE					:			int    	=       output_from_no_past[:, -3:, random_slice_idx].detach()
         SCREAMING_SNAKE_CASE					:			str    	=       output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowercase__    ,							lowercase__    ,							atol=1E-3 ) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							*lowercase__    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BertGenerationDecoder(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size) )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Dict							=						(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    snake_case__							:   Tuple							=						(BertGenerationDecoder,) if is_torch_available() else ()
    snake_case__							:   str							=						(
        {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
        if is_torch_available()
        else {}
    )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BertGenerationEncoderTester(self )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       'bert'
         self.model_tester.create_and_check_model(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         # This regression test was failing with PyTorch < 1.3
         (
             (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   
         )					:			Optional[Any]    	=       self.model_tester.prepare_config_and_inputs_for_decoder()

         SCREAMING_SNAKE_CASE					:			str    	=       None

         self.model_tester.create_and_check_model_as_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         self.assertIsNotNone(lowercase__ )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			int    	=       BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.Size([1, 8, 1_024] )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(
             [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.Size([1, 8, 50_358] )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(
             [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )




'''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  :Union[str, Any]  				=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :int  				=    {
    """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  :str  				=    {
    """allenai/led-base-16384""": 16_384,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Union[str, Any]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						LEDTokenizer
    snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]






    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__="replace"    ,							lowercase__="<s>"    ,							lowercase__="</s>"    ,							lowercase__="</s>"    ,							lowercase__="<s>"    ,							lowercase__="<unk>"    ,							lowercase__="<pad>"    ,							lowercase__="<mask>"    ,							lowercase__=False    ,							lowercase__=True    ,							**lowercase__    ,							)							-> str:
         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__    ,							)

         SCREAMING_SNAKE_CASE					:			Dict    	=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       getattr(lowercase__    ,							pre_tok_state.pop('type' ) )
              SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
              SCREAMING_SNAKE_CASE					:			str    	=       pre_tok_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       add_prefix_space

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

              SCREAMING_SNAKE_CASE					:			str    	=       False

              if state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True

              if state.get('trim_offsets'    ,							lowercase__ ) != trim_offsets:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       trim_offsets
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							state.pop('type' ) )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       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       _UpperCamelCase    (       self )							-> str:
         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       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else value
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       value






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       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       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._tokenizer.model.save(lowercase__    ,							name=lowercase__ )
         return tuple(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.sep_token_id]
         SCREAMING_SNAKE_CASE					:			str    	=       [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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = PaddingStrategy.DO_NOT_PAD    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> dict:
         SCREAMING_SNAKE_CASE					:			int    	=       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:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       'attention_mask' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              SCREAMING_SNAKE_CASE					:			str    	=       len(encoded_inputs['global_attention_mask'] ) != len(lowercase__ )

              if needs_to_be_padded:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       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`
                        SCREAMING_SNAKE_CASE					:			str    	=       (
                            encoded_inputs['global_attention_mask'] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       [-1] * difference + encoded_inputs[
                            'global_attention_mask'
                        ]
                   else:
                        raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

         return encoded_inputs



