'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						"blenderbot-small"
    snake_case__							:   Any							=						["past_key_values"]
    snake_case__							:   Tuple							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self    ,							lowercase__=50_265    ,							lowercase__=512    ,							lowercase__=8    ,							lowercase__=2_048    ,							lowercase__=16    ,							lowercase__=8    ,							lowercase__=2_048    ,							lowercase__=16    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__="gelu"    ,							lowercase__=512    ,							lowercase__=0.1    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=1    ,							lowercase__=False    ,							lowercase__=0    ,							lowercase__=1    ,							lowercase__=2    ,							lowercase__=2    ,							**lowercase__    ,							)							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Any    	=       d_model
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			int    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			str    	=       encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			str    	=       decoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       decoder_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       decoder_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       dropout
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_dropout
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       activation_dropout
         SCREAMING_SNAKE_CASE					:			Tuple    	=       activation_function
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       init_std
         SCREAMING_SNAKE_CASE					:			List[str]    	=       encoder_layerdrop
         SCREAMING_SNAKE_CASE					:			List[str]    	=       decoder_layerdrop
         SCREAMING_SNAKE_CASE					:			Dict    	=       use_cache
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=__lowerCAmelCase    ,							bos_token_id=__lowerCAmelCase    ,							eos_token_id=__lowerCAmelCase    ,							is_encoder_decoder=__lowerCAmelCase    ,							decoder_start_token_id=__lowerCAmelCase    ,							forced_eos_token_id=__lowerCAmelCase    ,							**__lowerCAmelCase    ,							)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @property
    def       _UpperCamelCase    (       self )							-> Tuple:







         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )

              if self.use_past:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       {0: 'batch'}
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       {0: 'batch', 1: 'decoder_sequence'}
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(__lowerCAmelCase    ,							direction='inputs' )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE					:			str    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       self.num_layers
                   for i in range(__lowerCAmelCase ):
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ] )

         return common_inputs






    @property
    def       _UpperCamelCase    (       self )							-> Optional[int]:







         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       super().outputs
         else:
              SCREAMING_SNAKE_CASE					:			Any    	=       super(__lowerCAmelCase    ,							self ).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       self.num_layers
                   for i in range(__lowerCAmelCase ):
                        SCREAMING_SNAKE_CASE					:			str    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Any    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase )

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE					:			List[str]    	=       seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE					:			str    	=       dict(**__lowerCAmelCase    ,							**__lowerCAmelCase )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       common_inputs['input_ids'].shape
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       common_inputs['decoder_input_ids'].shape[1]
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       decoder_seq_length + 3
              SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(__lowerCAmelCase    ,							__lowerCAmelCase )]    ,							dim=1 )

              SCREAMING_SNAKE_CASE					:			Any    	=       []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
              SCREAMING_SNAKE_CASE					:			Dict    	=       min(__lowerCAmelCase    ,							__lowerCAmelCase )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       max(__lowerCAmelCase    ,							__lowerCAmelCase ) - min_num_layers
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(__lowerCAmelCase ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(__lowerCAmelCase ),
                           torch.zeros(__lowerCAmelCase ),
                           torch.zeros(__lowerCAmelCase ),
                           torch.zeros(__lowerCAmelCase ),
                       ) )
              # TODO: test this.
              SCREAMING_SNAKE_CASE					:			int    	=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(__lowerCAmelCase    ,							__lowerCAmelCase ):
                   common_inputs["past_key_values"].append((torch.zeros(__lowerCAmelCase ), torch.zeros(__lowerCAmelCase )) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE					:			Any    	=       seqlen + 2
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_layers
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       common_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat(
                  [common_inputs['attention_mask'], torch.ones(__lowerCAmelCase    ,							__lowerCAmelCase    ,							dtype=__lowerCAmelCase )]    ,							dim=1 )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       [
                  (torch.zeros(__lowerCAmelCase ), torch.zeros(__lowerCAmelCase )) for _ in range(__lowerCAmelCase )
              ]
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       compute_effective_axis_dimension(
             __lowerCAmelCase    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0 )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE					:			int    	=       tokenizer.num_special_tokens_to_add(__lowerCAmelCase )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       compute_effective_axis_dimension(
             __lowerCAmelCase    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=__lowerCAmelCase )

         # Generate dummy inputs according to compute batch and sequence
         SCREAMING_SNAKE_CASE					:			Any    	=       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       dict(tokenizer(__lowerCAmelCase    ,							return_tensors=__lowerCAmelCase ) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Any:







         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  __lowerCAmelCase    ,							batch_size=__lowerCAmelCase    ,							seq_length=__lowerCAmelCase    ,							is_pair=__lowerCAmelCase    ,							framework=__lowerCAmelCase )

         elif self.task == "causal-lm":
              SCREAMING_SNAKE_CASE					:			int    	=       self._generate_dummy_inputs_for_causal_lm(
                  __lowerCAmelCase    ,							batch_size=__lowerCAmelCase    ,							seq_length=__lowerCAmelCase    ,							is_pair=__lowerCAmelCase    ,							framework=__lowerCAmelCase )
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __lowerCAmelCase    ,							batch_size=__lowerCAmelCase    ,							seq_length=__lowerCAmelCase    ,							is_pair=__lowerCAmelCase    ,							framework=__lowerCAmelCase )

         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:







         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       super()._flatten_past_key_values_(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase )
         else:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       super(__lowerCAmelCase    ,							self )._flatten_past_key_values_(
                  __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase )



'''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



