code: 
  """simple docstring"""
  
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  @require_tokenizers
  class A_	(lowerCAmelCase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Any						  =	BartTokenizer
    SCREAMING_SNAKE_CASE__    :		str						  =	BartTokenizerFast
    SCREAMING_SNAKE_CASE__    :		Any						  =	True
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	filter_roberta_detectors
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().setUp()
       UpperCAmelCase_      :      Any      =					[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCAmelCase_      :      Tuple      =					dict(zip(lowerCAmelCase_       ,     range(len(lowerCAmelCase_						)						)						)						)
       UpperCAmelCase_      :      int      =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCAmelCase_      :      Optional[Any]      =					{"unk_token": "<unk>"}
  
       UpperCAmelCase_      :      int      =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["vocab_file"]						)
       UpperCAmelCase_      :      Union[str, Any]      =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file       ,     "w"       ,     encoding="utf-8"						) as fp:
          fp.write(json.dumps(lowerCAmelCase_						) + "\n"						)
       with open(self.merges_file       ,     "w"       ,     encoding="utf-8"						) as fp:
          fp.write("\n".join(lowerCAmelCase_						)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     **lowercase_						):
  
       """simple docstring"""
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowerCAmelCase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     **lowercase_						):
  
       """simple docstring"""
       kwargs.update(self.special_tokens_map						)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowerCAmelCase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return "lower newer", "lower newer"
  
  
  
  
  
  
  
    @cached_property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return BartTokenizer.from_pretrained("facebook/bart-large"						)
  
  
  
  
  
  
  
    @cached_property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return BartTokenizerFast.from_pretrained("facebook/bart-large"						)
  
  
  
  
  
  
  
    @require_torch
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
       UpperCAmelCase_      :      int      =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
  
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          UpperCAmelCase_      :      int      =					tokenizer(lowerCAmelCase_       ,     max_length=len(lowerCAmelCase_						)       ,     padding=lowerCAmelCase_       ,     return_tensors="pt"						)
          self.assertIsInstance(lowerCAmelCase_       ,     lowerCAmelCase_						)
  
          self.assertEqual((2, 9)       ,     batch.input_ids.shape						)
          self.assertEqual((2, 9)       ,     batch.attention_mask.shape						)
          UpperCAmelCase_      :      int      =					batch.input_ids.tolist()[0]
          self.assertListEqual(lowerCAmelCase_       ,     lowerCAmelCase_						)
          # Test that special tokens are reset
  
  
  
  
  
  
  
    @require_torch
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          UpperCAmelCase_      :      Optional[Any]      =					tokenizer(lowerCAmelCase_       ,     padding=lowerCAmelCase_       ,     return_tensors="pt"						)
          # check if input_ids are returned and no labels
          self.assertIn("input_ids"       ,     lowerCAmelCase_						)
          self.assertIn("attention_mask"       ,     lowerCAmelCase_						)
          self.assertNotIn("labels"       ,     lowerCAmelCase_						)
          self.assertNotIn("decoder_attention_mask"       ,     lowerCAmelCase_						)
  
  
  
  
  
  
  
    @require_torch
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					[
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          UpperCAmelCase_      :      int      =					tokenizer(text_target=lowerCAmelCase_       ,     max_length=32       ,     padding="max_length"       ,     return_tensors="pt"						)
          self.assertEqual(32       ,     targets["input_ids"].shape[1]						)
  
  
  
  
  
  
  
    @require_torch
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
  
  
  
  
  
  
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          UpperCAmelCase_      :      str      =					tokenizer(
              ["I am a small frog" * 1024, "I am a small frog"]       ,     padding=lowerCAmelCase_       ,     truncation=lowerCAmelCase_       ,     return_tensors="pt"						)
          self.assertIsInstance(lowerCAmelCase_       ,     lowerCAmelCase_						)
          self.assertEqual(batch.input_ids.shape       ,     (2, 1024)						)
  
  
  
  
  
  
  
    @require_torch
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					["A long paragraph for summarization."]
       UpperCAmelCase_      :      Tuple      =					[
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          UpperCAmelCase_      :      Optional[int]      =					tokenizer(lowerCAmelCase_       ,     return_tensors="pt"						)
          UpperCAmelCase_      :      Dict      =					tokenizer(text_target=lowerCAmelCase_       ,     return_tensors="pt"						)
          UpperCAmelCase_      :      int      =					inputs["input_ids"]
          UpperCAmelCase_      :      List[str]      =					targets["input_ids"]
          self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
          self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
          self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
          self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       pass
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
  
  
  
  
  
  
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             UpperCAmelCase_      :      Optional[Any]      =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_       ,     **lowerCAmelCase_						)
             UpperCAmelCase_      :      Union[str, Any]      =					self.tokenizer_class.from_pretrained(lowerCAmelCase_       ,     **lowerCAmelCase_						)
             UpperCAmelCase_      :      Optional[int]      =					"A, <mask> AllenNLP sentence."
             UpperCAmelCase_      :      List[Any]      =					tokenizer_r.encode_plus(lowerCAmelCase_       ,     add_special_tokens=lowerCAmelCase_       ,     return_token_type_ids=lowerCAmelCase_						)
             UpperCAmelCase_      :      List[Any]      =					tokenizer_p.encode_plus(lowerCAmelCase_       ,     add_special_tokens=lowerCAmelCase_       ,     return_token_type_ids=lowerCAmelCase_						)
  
             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r["token_type_ids"]						)       ,     sum(tokens_p["token_type_ids"]						)						)
  
             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r["attention_mask"]						) / len(tokens_r["attention_mask"]						)       ,     sum(tokens_p["attention_mask"]						) / len(tokens_p["attention_mask"]						)       ,     )
  
             UpperCAmelCase_      :      int      =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]						)
             UpperCAmelCase_      :      List[str]      =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]						)
  
             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p["input_ids"]       ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)
             self.assertSequenceEqual(tokens_r["input_ids"]       ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)
  
             self.assertSequenceEqual(
                 lowerCAmelCase_       ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]						)
             self.assertSequenceEqual(
                 lowerCAmelCase_       ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      'google/bigbird-pegasus-large-arxiv': (
          'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-pubmed': (
          'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-bigpatent': (
          'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	"""bigbird_pegasus"""
    SCREAMING_SNAKE_CASE__    :		Any						  =	["""past_key_values"""]
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=9_6103       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_="gelu_new"       ,     lowercase_=1024       ,     lowercase_=0.1       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.02       ,     lowercase_=2       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=2       ,     lowercase_=1       ,     lowercase_="block_sparse"       ,     lowercase_=64       ,     lowercase_=3       ,     lowercase_=False       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
       UpperCAmelCase_      :      Optional[Any]      =					max_position_embeddings
       UpperCAmelCase_      :      List[str]      =					d_model
       UpperCAmelCase_      :      str      =					encoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					encoder_layers
       UpperCAmelCase_      :      Optional[Any]      =					encoder_attention_heads
       UpperCAmelCase_      :      str      =					decoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					decoder_layers
       UpperCAmelCase_      :      Optional[int]      =					decoder_attention_heads
       UpperCAmelCase_      :      Dict      =					dropout
       UpperCAmelCase_      :      Optional[int]      =					attention_dropout
       UpperCAmelCase_      :      Any      =					activation_dropout
       UpperCAmelCase_      :      str      =					activation_function
       UpperCAmelCase_      :      Tuple      =					init_std
       UpperCAmelCase_      :      Optional[int]      =					encoder_layerdrop
       UpperCAmelCase_      :      Union[str, Any]      =					decoder_layerdrop
       UpperCAmelCase_      :      int      =					classifier_dropout
       UpperCAmelCase_      :      Union[str, Any]      =					use_cache
       UpperCAmelCase_      :      List[str]      =					encoder_layers
       UpperCAmelCase_      :      str      =					scale_embedding  # scale factor will be sqrt(d_model) if True
  
       # extra config
       UpperCAmelCase_      :      List[Any]      =					attention_type
       UpperCAmelCase_      :      Any      =					block_size
       UpperCAmelCase_      :      str      =					num_random_blocks
       UpperCAmelCase_      :      Optional[Any]      =					use_bias
  
       super().__init__(
           pad_token_id=lowercase_       ,     bos_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     decoder_start_token_id=lowercase_       ,     **lowercase_       ,     )
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      List[str]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
  
          if self.use_past:
             UpperCAmelCase_      :      List[str]      =					{0: "batch"}
             UpperCAmelCase_      :      Any      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
             UpperCAmelCase_      :      List[str]      =					{0: "batch", 1: "decoder_sequence"}
             UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}
  
          if self.use_past:
             self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCAmelCase_      :      str      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Tuple      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      List[str]      =					{0: "batch", 2: "past_sequence + sequence"}
       else:
          UpperCAmelCase_      :      Optional[Any]      =					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						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super().outputs
       else:
          UpperCAmelCase_      :      Optional[Any]      =					super(lowercase_       ,     self						).outputs
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Any      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      int      =					{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       # Generate decoder inputs
       UpperCAmelCase_      :      Union[str, Any]      =					seq_length if not self.use_past else 1
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_      :      Optional[Any]      =					dict(**lowercase_       ,     **lowercase_						)
  
       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_      :      Dict      =					common_inputs["input_ids"].shape
          UpperCAmelCase_      :      Union[str, Any]      =					common_inputs["decoder_input_ids"].shape[1]
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					self.num_attention_heads
          UpperCAmelCase_      :      Tuple      =					(
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          UpperCAmelCase_      :      Union[str, Any]      =					decoder_seq_length + 3
          UpperCAmelCase_      :      List[Any]      =					(
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
  
          UpperCAmelCase_      :      List[str]      =					torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(lowercase_       ,     lowercase_						)]       ,     dim=1						)
  
          UpperCAmelCase_      :      Dict      =					[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					self.num_layers
          UpperCAmelCase_      :      List[str]      =					min(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					max(lowercase_       ,     lowercase_						) - min_num_layers
          UpperCAmelCase_      :      List[Any]      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"
  
          for _ in range(lowercase_						):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                 )						)
          # TODO: test this.
          UpperCAmelCase_      :      Optional[int]      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(lowercase_       ,     lowercase_						):
             common_inputs["past_key_values"].append((torch.zeros(lowercase_						), torch.zeros(lowercase_						))						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       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_      :      Tuple      =					common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          UpperCAmelCase_      :      str      =					seqlen + 2
          UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.num_layers
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_attention_heads
          UpperCAmelCase_      :      Optional[int]      =					(
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
  
          UpperCAmelCase_      :      str      =					common_inputs["attention_mask"].dtype
          UpperCAmelCase_      :      Union[str, Any]      =					torch.cat(
              [common_inputs["attention_mask"], torch.ones(lowercase_       ,     lowercase_       ,     dtype=lowercase_						)]       ,     dim=1						)
          UpperCAmelCase_      :      List[str]      =					[
              (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
          ]
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase_      :      Any      =					compute_effective_axis_dimension(
           lowercase_       ,     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_      :      List[str]      =					tokenizer.num_special_tokens_to_add(lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowercase_						)
  
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_      :      str      =					[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_      :      int      =					dict(tokenizer(lowercase_       ,     return_tensors=lowercase_						)						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Dict      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       elif self.task == "causal-lm":
          UpperCAmelCase_      :      List[str]      =					self._generate_dummy_inputs_for_causal_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
       else:
          UpperCAmelCase_      :      int      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super()._flatten_past_key_values_(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       else:
          UpperCAmelCase_      :      Dict      =					super(lowercase_       ,     self						)._flatten_past_key_values_(
              lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  