


import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__    :Optional[Any]       =				logging.get_logger(__name__)

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      ['input_ids', 'attention_mask']
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE="</s>"     ,       _SCREAMING_SNAKE_CASE="<unk>"     ,       _SCREAMING_SNAKE_CASE="<pad>"     ,       _SCREAMING_SNAKE_CASE=125     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			None:




									"""simple docstring"""





									if extra_ids > 0 and additional_special_tokens is None:
																_UpperCAmelCase											=     [f'''<extra_id_{i}>''' for i in range(_SCREAMING_SNAKE_CASE		)]
									elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra_id special tokens
																_UpperCAmelCase											=     len(set(filter(lambda _SCREAMING_SNAKE_CASE		: bool('extra_id' in str(_SCREAMING_SNAKE_CASE		)		)     ,       _SCREAMING_SNAKE_CASE		)		)		)
																if extra_tokens != extra_ids:
																							raise ValueError(
																							    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																							    ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
																							    ' extra_ids tokens'		)

									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else pad_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else eos_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else unk_token

									super().__init__(
									    eos_token=_SCREAMING_SNAKE_CASE     ,       unk_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       extra_ids=_SCREAMING_SNAKE_CASE     ,       additional_special_tokens=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

									_UpperCAmelCase											=     extra_ids

									_UpperCAmelCase											=     2**8  # utf is 8 bits

									# define special tokens dict
									_UpperCAmelCase											=     {
									    self.pad_token: 0,
									    self.eos_token: 1,
									    self.unk_token: 2,
									}
									_UpperCAmelCase											=     len(self.special_tokens_encoder		)
									_UpperCAmelCase											=     len(_SCREAMING_SNAKE_CASE		)
									for i, token in enumerate(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     self.vocab_size + i - n
									_UpperCAmelCase											=     {v: k for k, v in self.special_tokens_encoder.items()}
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False		)					->			List[int]:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE     ,       token_ids_a=_SCREAMING_SNAKE_CASE     ,       already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									# normal case: some special tokens
									if token_ids_a is None:
																return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[int]:




									"""simple docstring"""





									if len(_SCREAMING_SNAKE_CASE		) > 0 and token_ids[-1] == self.eos_token_id:
																warnings.warn(
																    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
																    ' eos tokens being added.'		)
																return token_ids
									else:
																return token_ids + [self.eos_token_id]
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     [self.eos_token_id]

									if token_ids_a is None:
																return len(token_ids_a + eos		) * [0]
									return len(token_ids_a + eos + token_ids_a + eos		) * [0]
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE		)
									if token_ids_a is None:
																return token_ids_a
									else:
																_UpperCAmelCase											=     self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE		)
																return token_ids_a + token_ids_a
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     [chr(_SCREAMING_SNAKE_CASE		) for i in text.encode('utf-8'		)]
									return tokens
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									if token in self.special_tokens_encoder:
																_UpperCAmelCase											=     self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
																_UpperCAmelCase											=     self.added_tokens_encoder[token]
									elif len(_SCREAMING_SNAKE_CASE		) != 1:
																_UpperCAmelCase											=     self.unk_token_id
									else:
																_UpperCAmelCase											=     ord(_SCREAMING_SNAKE_CASE		) + self._num_special_tokens
									return token_id
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									if index in self.special_tokens_decoder:
																_UpperCAmelCase											=     self.special_tokens_decoder[index]
									else:
																_UpperCAmelCase											=     chr(index - self._num_special_tokens		)
									return token
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     b''
									for token in tokens:
																if token in self.special_tokens_decoder:
																							_UpperCAmelCase											=     self.special_tokens_decoder[token].encode('utf-8'		)
																elif token in self.added_tokens_decoder:
																							_UpperCAmelCase											=     self.special_tokens_decoder[token].encode('utf-8'		)
																elif token in self.special_tokens_encoder:
																							_UpperCAmelCase											=     token.encode('utf-8'		)
																elif token in self.added_tokens_encoder:
																							_UpperCAmelCase											=     token.encode('utf-8'		)
																else:
																							_UpperCAmelCase											=     bytes([ord(_SCREAMING_SNAKE_CASE		)]		)
																bstring += tok_string
									_UpperCAmelCase											=     bstring.decode('utf-8'     ,       errors='ignore'		)
									return string






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			Tuple[str]:




									"""simple docstring"""





									return ()




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__    :List[Any]       =				logging.get_logger(__name__)

lowerCAmelCase__    :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 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'blenderbot-small'
		_a      :    Tuple           =      ['past_key_values']
		_a      :    List[str]           =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     encoder_ffn_dim
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     encoder_attention_heads
									_UpperCAmelCase											=     decoder_ffn_dim
									_UpperCAmelCase											=     decoder_layers
									_UpperCAmelCase											=     decoder_attention_heads
									_UpperCAmelCase											=     dropout
									_UpperCAmelCase											=     attention_dropout
									_UpperCAmelCase											=     activation_dropout
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     init_std
									_UpperCAmelCase											=     encoder_layerdrop
									_UpperCAmelCase											=     decoder_layerdrop
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





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

																if self.use_past:
																							_UpperCAmelCase											=     {0: 'batch'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}

																if self.use_past:
																							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
									elif self.task == "causal-lm":
																# TODO: figure this case out.
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     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		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super().outputs
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).outputs
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									# Generate decoder inputs
									_UpperCAmelCase											=     seq_length if not self.use_past else 1
									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									_UpperCAmelCase											=     dict(**_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

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

																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

																_UpperCAmelCase											=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase											=     min(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     max(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) - min_num_layers
																_UpperCAmelCase											=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																for _ in range(_SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							    )		)
																# TODO: test this.
																_UpperCAmelCase											=     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																for _ in range(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		))		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

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

																_UpperCAmelCase											=     common_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)
																_UpperCAmelCase											=     [
																    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
																]
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       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
									_UpperCAmelCase											=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)

									# Generate dummy inputs according to compute batch and sequence
									_UpperCAmelCase											=     [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
									_UpperCAmelCase											=     dict(tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

									elif self.task == "causal-lm":
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_causal_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

									return common_inputs






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		)._flatten_past_key_values_(
																    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
