import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
		raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
		raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							"Hello, World!"
lowerCamelCase__	=							"en_XX"


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	Path("""data_bin""" )
		_UpperCamelCase						    =	FairseqXmodModel.from_pretrained(
		    model_name_or_path=str(Path(lowerCAmelCase ).parent )						,  checkpoint_file=Path(lowerCAmelCase ).name						,  _name="""xmod_base"""						,  arch="""xmod_base"""						,  task="""multilingual_masked_lm"""						,  data_name_or_path=str(lowerCAmelCase )						,  bpe="""sentencepiece"""						,  sentencepiece_model=str(Path(lowerCAmelCase ).parent / """sentencepiece.bpe.model""" )						,  src_dict=str(data_dir / """dict.txt""" )						,  )
		xmod.eval()  # disable dropout
		print(lowerCAmelCase )

		_UpperCamelCase						    =	xmod.model.encoder.sentence_encoder
		_UpperCamelCase						    =	XmodConfig(
		    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,  hidden_size=xmod.cfg.model.encoder_embed_dim						,  num_hidden_layers=xmod.cfg.model.encoder_layers						,  num_attention_heads=xmod.cfg.model.encoder_attention_heads						,  intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,  max_position_embeddings=5_1_4						,  type_vocab_size=1						,  layer_norm_eps=1e-5						,  pre_norm=xmod.cfg.model.encoder_normalize_before						,  adapter_reduction_factor=getattr(xmod.cfg.model						,  """bottleneck"""						,  2 )						,  adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,  adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,  ln_before_adapter=xmod.cfg.model.ln_before_adapter						,  languages=xmod.cfg.model.languages						,  )
		if classification_head:
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

		print("""Our X-MOD config:"""						,  lowerCAmelCase )

		_UpperCamelCase						    =	XmodForSequenceClassification(lowerCAmelCase ) if classification_head else XmodForMaskedLM(lowerCAmelCase )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		_UpperCamelCase						    =	xmod_sent_encoder.embed_tokens.weight
		_UpperCamelCase						    =	xmod_sent_encoder.embed_positions.weight
		_UpperCamelCase						    =	torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c xmod doesn't use them.

		_UpperCamelCase						    =	xmod_sent_encoder.layernorm_embedding.weight
		_UpperCamelCase						    =	xmod_sent_encoder.layernorm_embedding.bias

		for i in range(config.num_hidden_layers ):
				# Encoder: start of layer
				_UpperCamelCase						    =	model.roberta.encoder.layer[i]
				_UpperCamelCase						    =	xmod_sent_encoder.layers[i]

				# self attention
				_UpperCamelCase						    =	layer.attention.self
				if not (
				    xmod_layer.self_attn.k_proj.weight.data.shape
				    == xmod_layer.self_attn.q_proj.weight.data.shape
				    == xmod_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size) )
				):
						raise AssertionError("""Dimensions of self-attention weights do not match.""" )

				_UpperCamelCase						    =	xmod_layer.self_attn.q_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.q_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn.k_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.k_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn.v_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.v_proj.bias

				# self-attention output
				_UpperCamelCase						    =	layer.attention.output
				if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
						raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
				_UpperCamelCase						    =	xmod_layer.self_attn.out_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.out_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn_layer_norm.weight
				_UpperCamelCase						    =	xmod_layer.self_attn_layer_norm.bias

				# intermediate
				_UpperCamelCase						    =	layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError("""Dimensions of intermediate weights do not match.""" )
				_UpperCamelCase						    =	xmod_layer.fca.weight
				_UpperCamelCase						    =	xmod_layer.fca.bias

				# output
				_UpperCamelCase						    =	layer.output
				if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
				_UpperCamelCase						    =	xmod_layer.fca.weight
				_UpperCamelCase						    =	xmod_layer.fca.bias
				_UpperCamelCase						    =	xmod_layer.final_layer_norm.weight
				_UpperCamelCase						    =	xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						_UpperCamelCase						    =	xmod_layer.adapter_layer_norm.weight
						_UpperCamelCase						    =	xmod_layer.adapter_layer_norm.bias

				if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
						raise AssertionError("""Lists of language adapters do not match.""" )
				for lang_code, adapter in xmod_layer.adapter_modules.items():
						_UpperCamelCase						    =	bert_output.adapter_modules[lang_code]
						_UpperCamelCase						    =	xmod_layer.adapter_modules[lang_code]
						_UpperCamelCase						    =	from_adapter.fca.weight
						_UpperCamelCase						    =	from_adapter.fca.bias
						_UpperCamelCase						    =	from_adapter.fca.weight
						_UpperCamelCase						    =	from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				_UpperCamelCase						    =	xmod_sent_encoder.layer_norm.weight
				_UpperCamelCase						    =	xmod_sent_encoder.layer_norm.bias

		if classification_head:
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].dense.weight
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].dense.bias
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.weight
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.bias
		else:
				# LM Head
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.dense.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.dense.bias
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.layer_norm.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.layer_norm.bias
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		_UpperCamelCase						    =	xmod.encode(lowerCAmelCase ).unsqueeze(0 )  # batch of size 1
		model.roberta.set_default_language(lowerCAmelCase )

		_UpperCamelCase						    =	model(lowerCAmelCase )[0]
		if classification_head:
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""](xmod.extract_features(lowerCAmelCase ) )
		else:
				_UpperCamelCase						    =	xmod.model(lowerCAmelCase						,  lang_id=[SAMPLE_LANGUAGE] )[0]
		print(our_output.shape						,  their_output.shape )
		_UpperCamelCase						    =	torch.max(torch.abs(our_output - their_output ) ).item()
		print(F'max_absolute_diff = {max_absolute_diff}' )  # ~ 1e-7
		_UpperCamelCase						    =	torch.allclose(lowerCAmelCase						,  lowerCAmelCase						,  atol=1e-3 )
		print("""Do both models output the same tensors?"""						,  """🔥""" if success else """💩""" )
		if not success:
				raise Exception("""Something went wRoNg""" )

		Path(lowerCAmelCase ).mkdir(parents=lowerCAmelCase						,  exist_ok=lowerCAmelCase )
		print(F'Saving model to {pytorch_dump_folder_path}' )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--classification_head", action="store_true", help="Whether to convert a final classification head."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__	=							get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		XLNetTokenizer
						UpperCamelCase_							: Optional[int]	=		XLNetTokenizerFast
						UpperCamelCase_							: Dict	=		True
						UpperCamelCase_							: Dict	=		True

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								_UpperCamelCase						    =	XLNetTokenizer(a       ,							keep_accents=a				)
								tokenizer.sanitize_special_tokens()
								tokenizer.save_pretrained(self.tmpdirname				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""<s>"""
								_UpperCamelCase						    =	1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a				)       ,							a				)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a				)       ,							a				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	list(self.get_tokenizer().get_vocab().keys()				)

								self.assertEqual(vocab_keys[0]       ,							"""<unk>"""				)
								self.assertEqual(vocab_keys[1]       ,							"""<s>"""				)
								self.assertEqual(vocab_keys[-1]       ,							"""<eod>"""				)
								self.assertEqual(len(a				)       ,							10_06				)

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


								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size       ,							10_00				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							keep_accents=a				)

								_UpperCamelCase						    =	tokenizer.tokenize("""This is a test"""				)
								self.assertListEqual(a       ,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)       ,							[2_85, 46, 10, 1_70, 3_82]				)

								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """é""",
								        """.""",
								    ]       ,							)
								_UpperCamelCase						    =	tokenizer.convert_tokens_to_ids(a				)
								self.assertListEqual(a       ,							[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				)

								_UpperCamelCase						    =	tokenizer.convert_ids_to_tokens(a				)
								self.assertListEqual(
								    a       ,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """<unk>""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """<unk>""",
								        """.""",
								    ]       ,							)

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							do_lower_case=a				)
								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        SPIECE_UNDERLINE + """""",
								        """i""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """se""",
								        """.""",
								    ]       ,							)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)       ,							["""▁he""", """ll""", """o"""]				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							do_lower_case=a				)
								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """se""",
								        """.""",
								    ]       ,							)

						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer.from_pretrained("""xlnet-base-cased"""				)

								_UpperCamelCase						    =	tokenizer.encode("""sequence builders"""       ,							add_special_tokens=a				)
								_UpperCamelCase						    =	tokenizer.encode("""multi-sequence build"""       ,							add_special_tokens=a				)

								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a				)
								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a       ,							a				)

								assert encoded_sentence == text + [4, 3]
								assert encoded_pair == text + [4] + text_a + [4, 3]







						@slow
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	{"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=a       ,							model_name="""xlnet-base-cased"""       ,							revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""       ,							)



