


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							int	    =	[]
						embed.append(
						    (
						        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
						        f"stage{idx}.patch_embed.proj.weight",
						    )      )
						embed.append(
						    (
						        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
						        f"stage{idx}.patch_embed.proj.bias",
						    )      )
						embed.append(
						    (
						        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
						        f"stage{idx}.patch_embed.norm.weight",
						    )      )
						embed.append(
						    (
						        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
						        f"stage{idx}.patch_embed.norm.bias",
						    )      )
						return embed




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	[]
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
						        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
						        f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
						        f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
						        f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
						        f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
						        f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
						        f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
						        f"stage{idx}.blocks.{cnt}.attn.proj.weight",
						    )      )
						attention_weights.append(
						    (
						        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
						        f"stage{idx}.blocks.{cnt}.attn.proj.bias",
						    )      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")      )
						attention_weights.append(
						    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")      )
						return attention_weights




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	[]
						token.append((f"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''')      )
						return token




def     __SCREAMING_SNAKE_CASE							(       )		->     Tuple:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	[]
						head.append(('''layernorm.weight''', '''norm.weight''')      )
						head.append(('''layernorm.bias''', '''norm.bias''')      )
						head.append(('''classifier.weight''', '''head.weight''')      )
						head.append(('''classifier.bias''', '''head.bias''')      )
						return head




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     List[str]:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	'''imagenet-1k-id2label.json'''
						__UpperCAmelCase			:							List[str]	    =	1000

						__UpperCAmelCase			:							Tuple	    =	'''huggingface/label-files'''
						__UpperCAmelCase			:							Tuple	    =	num_labels
						__UpperCAmelCase			:							int	    =	json.load(open(cached_download(hf_hub_url(lowercase_      ,       lowercase_      ,       repo_type='''dataset'''      )      )      ,       '''r'''      )      )
						__UpperCAmelCase			:							Optional[Any]	    =	{int(lowercase_      ): v for k, v in idalabel.items()}

						__UpperCAmelCase			:							Dict	    =	idalabel
						__UpperCAmelCase			:							Optional[Any]	    =	{v: k for k, v in idalabel.items()}

						__UpperCAmelCase			:							Optional[Any]	    =	CvtConfig(num_labels=lowercase_      ,       idalabel=lowercase_      ,       labelaid=lowercase_      )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit('''/'''      ,       1      )[-1][4:6] == "13":
												__UpperCAmelCase			:							Any	    =	[1, 2, 10]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit('''/'''      ,       1      )[-1][4:6] == "21":
												__UpperCAmelCase			:							Any	    =	[1, 4, 16]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												__UpperCAmelCase			:							Optional[int]	    =	[2, 2, 20]
												__UpperCAmelCase			:							str	    =	[3, 12, 16]
												__UpperCAmelCase			:							str	    =	[192, 768, 1024]

						__UpperCAmelCase			:							Optional[int]	    =	CvtForImageClassification(lowercase_      )
						__UpperCAmelCase			:							Tuple	    =	AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''      )
						__UpperCAmelCase			:							Optional[Any]	    =	image_size
						__UpperCAmelCase			:							Any	    =	torch.load(lowercase_      ,       map_location=torch.device('''cpu'''      )      )

						__UpperCAmelCase			:							List[Any]	    =	OrderedDict()
						__UpperCAmelCase			:							Optional[Any]	    =	[]

						for idx in range(len(config.depth      )      ):
												if config.cls_token[idx]:
																		__UpperCAmelCase			:							List[Any]	    =	list_of_state_dict + cls_token(lowercase_      )
												__UpperCAmelCase			:							Any	    =	list_of_state_dict + embeddings(lowercase_      )
												for cnt in range(config.depth[idx]      ):
																		__UpperCAmelCase			:							List[str]	    =	list_of_state_dict + attention(lowercase_      ,       lowercase_      )

						__UpperCAmelCase			:							List[Any]	    =	list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(lowercase_      )
						for i in range(len(lowercase_      )      ):
												__UpperCAmelCase			:							Dict	    =	original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(lowercase_      )
						model.save_pretrained(lowercase_      )
						image_processor.save_pretrained(lowercase_      )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
				lowerCAmelCase       		=    argparse.ArgumentParser()
				parser.add_argument(
				    """--cvt_model""",
				    default="""cvt-w24""",
				    type=str,
				    help="""Name of the cvt model you'd like to convert.""",
				)
				parser.add_argument(
				    """--image_size""",
				    default=384,
				    type=int,
				    help="""Input Image Size""",
				)
				parser.add_argument(
				    """--cvt_file_name""",
				    default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
				    type=str,
				    help="""Input Image Size""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)

				lowerCAmelCase       		=    parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





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   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						XLNetTokenizer
	_lowerCAmelCase     :		int										=						XLNetTokenizerFast
	_lowerCAmelCase     :		Optional[Any]										=						True
	_lowerCAmelCase     :		Optional[Any]										=						True


	def  A(    self):
							super().setUp()

							# We have a SentencePiece fixture for testing
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)
							tokenizer.sanitize_special_tokens()
							tokenizer.save_pretrained(self.tmpdirname)


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	'''<s>'''
							__UpperCAmelCase			:							List[str]	    =	1

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


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	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(lowercase__)				,     1_0_0_6)


	def  A(    self):
							self.assertEqual(self.get_tokenizer().vocab_size				,     1_0_0_0)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	XLNetTokenizer(lowercase__				,     keep_accents=lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.tokenize('''This is a test''')
							self.assertListEqual(lowercase__				,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)				,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

							__UpperCAmelCase			:							Tuple	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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			:							Union[str, Any]	    =	tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(lowercase__				,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

							__UpperCAmelCase			:							int	    =	tokenizer.convert_ids_to_tokens(lowercase__)
							self.assertListEqual(
							    lowercase__				,     [
							        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):
							__UpperCAmelCase			:							str	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							int	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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):
							__UpperCAmelCase			:							Optional[Any]	    =	XLNetTokenizer(lowercase__				,     do_lower_case=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    lowercase__				,     [
							        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):
							__UpperCAmelCase			:							Tuple	    =	XLNetTokenizer.from_pretrained('''xlnet-base-cased''')

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''sequence builders'''				,     add_special_tokens=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.encode('''multi-sequence build'''				,     add_special_tokens=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.build_inputs_with_special_tokens(lowercase__				,     lowercase__)

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


	@slow
	def  A(    self):
							# fmt: off
							__UpperCAmelCase			:							Optional[Any]	    =	{'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 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_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 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, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 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=lowercase__				,     model_name='''xlnet-base-cased'''				,     revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''				,     )

