


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__  :Dict             =							logging.get_logger(__name__)

lowerCAmelCase__  :Dict             =							{
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 		__a				(    UpperCAmelCase      ):
		_a     :  List[str]           =     'big_bird'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=50358      ,   _SCREAMING_SNAKE_CASE=768      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=3072      ,   _SCREAMING_SNAKE_CASE="gelu_new"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=1e-1_2      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=66      ,   _SCREAMING_SNAKE_CASE="block_sparse"      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									super().__init__(
									    pad_token_id=_SCREAMING_SNAKE_CASE      ,   bos_token_id=_SCREAMING_SNAKE_CASE      ,   eos_token_id=_SCREAMING_SNAKE_CASE      ,   sep_token_id=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      type_vocab_size
									_UpperCAmelCase					=      layer_norm_eps
									_UpperCAmelCase					=      use_cache

									_UpperCAmelCase					=      rescale_embeddings
									_UpperCAmelCase					=      attention_type
									_UpperCAmelCase					=      use_bias
									_UpperCAmelCase					=      block_size
									_UpperCAmelCase					=      num_random_blocks
									_UpperCAmelCase					=      classifier_dropout



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




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase					=      {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase					=      {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]						)




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=9      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=5      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE=8      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.002      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      encoder_seq_length
									_UpperCAmelCase					=      decoder_seq_length
									# For common tests
									_UpperCAmelCase					=      self.decoder_seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_attention_mask
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      d_ff
									_UpperCAmelCase					=      relative_attention_num_buckets
									_UpperCAmelCase					=      dropout_rate
									_UpperCAmelCase					=      initializer_factor
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      pad_token_id
									_UpperCAmelCase					=      decoder_start_token_id
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      decoder_layers
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									return TaConfig.from_pretrained('google/umt5-base'						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	List[str]:




									"""simple docstring"""





									if attention_mask is None:
																_UpperCAmelCase					=      input_ids.ne(config.pad_token_id						)
									if decoder_attention_mask is None:
																_UpperCAmelCase					=      decoder_input_ids.ne(config.pad_token_id						)
									if head_mask is None:
																_UpperCAmelCase					=      torch.ones(config.num_hidden_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									if decoder_head_mask is None:
																_UpperCAmelCase					=      torch.ones(config.num_decoder_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									if cross_attn_head_mask is None:
																_UpperCAmelCase					=      torch.ones(
																    config.num_decoder_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									return {
									    "input_ids": input_ids,
									    "decoder_input_ids": decoder_input_ids,
									    "attention_mask": attention_mask,
									    "decoder_attention_mask": decoder_attention_mask,
									    "head_mask": head_mask,
									    "decoder_head_mask": decoder_head_mask,
									    "cross_attn_head_mask": cross_attn_head_mask,
									}
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.encoder_seq_length]      ,   self.vocab_size						)
									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.decoder_seq_length]      ,   self.vocab_size						)

									# we need to clamp the input ids here to avoid having pad token in between
									# this is because for NllbMoe the position_ids are prepared such that
									# all pad tokens have pos id = 2 and rest are between 2..seq_length
									# and the seq_length here is seq_length - num_pad_tokens
									# but when using past, there is no way of knowing if the past input ids had
									# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
									# position_ids being off by num_pad_tokens in past input
									_UpperCAmelCase					=      input_ids.clamp(self.pad_token_id + 1						)
									_UpperCAmelCase					=      decoder_input_ids.clamp(self.pad_token_id + 1						)

									_UpperCAmelCase					=      self.get_config()
									_UpperCAmelCase					=      config.num_attention_heads
									_UpperCAmelCase					=      self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, input_dict
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.prepare_config_and_inputs()
									return config, inputs_dict
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return TaConfig(
									    vocab_size=166      ,   d_model=self.hidden_size      ,   d_ff=self.d_ff      ,   d_kv=self.hidden_size // self.num_attention_heads      ,   num_layers=self.num_hidden_layers      ,   num_decoder_layers=self.decoder_layers      ,   num_heads=self.num_attention_heads      ,   relative_attention_num_buckets=self.relative_attention_num_buckets      ,   dropout_rate=self.dropout_rate      ,   initializer_factor=self.initializer_factor      ,   eos_token_id=self.eos_token_id      ,   bos_token_id=self.pad_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.decoder_start_token_id      ,   )
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return TaConfig(
									    vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   d_ff=self.d_ff      ,   d_kv=self.hidden_size // self.num_attention_heads      ,   num_layers=self.num_hidden_layers      ,   num_decoder_layers=self.decoder_layers      ,   num_heads=self.num_attention_heads      ,   relative_attention_num_buckets=self.relative_attention_num_buckets      ,   dropout_rate=self.dropout_rate      ,   initializer_factor=self.initializer_factor      ,   eos_token_id=self.eos_token_id      ,   bos_token_id=self.pad_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.decoder_start_token_id      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(
									    input_ids=_SCREAMING_SNAKE_CASE      ,   decoder_input_ids=_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   decoder_attention_mask=_SCREAMING_SNAKE_CASE      ,   )
									_UpperCAmelCase					=      model(input_ids=_SCREAMING_SNAKE_CASE      ,   decoder_input_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      result.last_hidden_state
									_UpperCAmelCase					=      result.past_key_values
									_UpperCAmelCase					=      result.encoder_last_hidden_state

									self.parent.assertEqual(encoder_output.size()      ,   (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
									self.parent.assertEqual(decoder_output.size()      ,   (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
									# There should be `num_layers` key value embeddings stored in decoder_past
									self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   config.num_layers						)
									# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
									self.parent.assertEqual(len(decoder_past[0]						)      ,   4						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						).get_decoder().to(_SCREAMING_SNAKE_CASE						).eval()
									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE						) == len(_SCREAMING_SNAKE_CASE						)						)
									self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE						) == len(_SCREAMING_SNAKE_CASE						) + 1						)

									_UpperCAmelCase		,	_UpperCAmelCase					=      outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									_UpperCAmelCase					=      ids_tensor((self.batch_size, 1)      ,   config.vocab_size						)

									# append to next input_ids and
									_UpperCAmelCase					=      torch.cat([input_ids, next_tokens]      ,   dim=-1						)

									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)['last_hidden_state']
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   past_key_values=_SCREAMING_SNAKE_CASE						)['last_hidden_state']

									# select random slice
									_UpperCAmelCase					=      ids_tensor((1,)      ,   output_from_past.shape[-1]						).item()
									_UpperCAmelCase					=      output_from_no_past[:, -1, random_slice_idx].detach()
									_UpperCAmelCase					=      output_from_past[:, 0, random_slice_idx].detach()

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






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




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						).half().eval()
									_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)['last_hidden_state']
									self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE						).any().item()						)



@require_torch
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Union[str, Any]           =     (
		    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
		)
		_a     :  List[Any]           =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
		_a     :  Tuple           =     (
		    {
		        'conversational': UMTaForConditionalGeneration,
		        'feature-extraction': UMTaModel,
		        'summarization': UMTaForConditionalGeneration,
		        'text2text-generation': UMTaForConditionalGeneration,
		        'translation': UMTaForConditionalGeneration,
		        'question-answering': UMTaForQuestionAnswering,
		    }
		    if is_torch_available()
		    else {}
		)
		_a     :  List[str]           =     True
		_a     :  List[Any]           =     False
		_a     :  Tuple           =     False
		_a     :  List[Any]           =     True
		_a     :  str           =     True
		# The small UMT5 model needs higher percentages for CPU/MP tests
		_a     :  Tuple           =     [0.8, 0.9]
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModelTester(self						)
		@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase					=      UMTaModel(config_and_inputs[0]						).to(_SCREAMING_SNAKE_CASE						)
									with tempfile.TemporaryDirectory() as tmpdirname:
																torch.onnx.export(
																    _SCREAMING_SNAKE_CASE      ,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,   f'''{tmpdirname}/t5_test.onnx'''      ,   export_params=_SCREAMING_SNAKE_CASE      ,   opset_version=9      ,   input_names=['input_ids', 'decoder_input_ids']      ,   )
		@unittest.skipIf(torch_device == 'cpu'      ,   'Cant do half precision'						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase					=      config_and_inputs[0]
									_UpperCAmelCase					=      UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE						).eval()
									model.to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      {
									    'head_mask': torch.zeros(config.num_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									    'decoder_head_mask': torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									}

									for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE      ,   head_masking.items()						):
																_UpperCAmelCase					=      {name: mask}
																# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																if name == "head_mask":
																							_UpperCAmelCase					=      torch.ones(
																							    config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model.generate(
																    config_and_inputs[1]['input_ids']      ,   num_beams=1      ,   max_length=3      ,   output_attentions=_SCREAMING_SNAKE_CASE      ,   return_dict_in_generate=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
																# We check the state of decoder_attentions and cross_attentions just from the last step
																_UpperCAmelCase					=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																self.assertEqual(sum([w.sum().item() for w in attn_weights]						)      ,   0.0						)






		@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 		__a				(    unittest.TestCase      ):
		@slow
		@unittest.skip(
		    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaForConditionalGeneration.from_pretrained('google/umt5-small'      ,   return_dict=_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      AutoTokenizer.from_pretrained('google/umt5-small'      ,   use_fast=_SCREAMING_SNAKE_CASE      ,   legacy=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      [
									    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
									    'No se como puedo <extra_id_0>.',
									    'This is the reason why we <extra_id_0> them.',
									    'The <extra_id_0> walks in <extra_id_1>, seats',
									    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
									]
									_UpperCAmelCase					=      tokenizer(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'      ,   padding=_SCREAMING_SNAKE_CASE						).input_ids
									# fmt: off
									_UpperCAmelCase					=      torch.tensor(
									    [
									        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
									    ]						)
									# fmt: on
									torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      model.generate(input_ids.to(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      [
									    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
									    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									]
									_UpperCAmelCase					=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)
									self.assertEqual(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
