






'''simple docstring'''





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase		:		Dict            = logging.get_logger()


@dataclass
class       snake_case__	:
					a_								= 42
					a_								= field(default_factory=__a)
					a_								= field(default_factory=__a)




					def 						A	(   self   :						Optional[Any]							,      _A   :						List[str]							,      _A   :						List[Any]							,      _A   :						Union[str, Any]	)  ->				List[str]:
								UpperCAmelCase_	:    Optional[Any]	  =    len(list(m.modules()	)	) == 1 or isinstance(lowerCAmelCase__							,      nn.Convad	) or isinstance(lowerCAmelCase__							,      nn.BatchNormad	)
								if has_not_submodules:
											self.traced.append(lowerCAmelCase__	)




					def __call__(   self   :						List[str]							,      _A   :						Union[str, Any]	)  ->				Union[str, Any]:
								for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook	)	)
								self.module(lowerCAmelCase__	)
								[x.remove() for x in self.handles]
								return self





					@property
					def 						A	(   self   :						List[str]	)  ->				int:
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda _A	: len(list(x.state_dict().keys()	)	) > 0							,      self.traced	)	)






@dataclass
class       snake_case__	:
					a_								= 42
					a_								= 42
					a_								= 0
					a_								= field(default_factory=__a)
					a_								= field(default_factory=__a)




					def __call__(   self   :						Optional[Any]							,      _A   :						str	)  ->				str:
								UpperCAmelCase_	:    Union[str, Any]	  =    Tracker(self.dest	)(lowerCAmelCase__	).parametrized
								UpperCAmelCase_	:    int	  =    Tracker(self.src	)(lowerCAmelCase__	).parametrized

								UpperCAmelCase_	:    Tuple	  =    list(filter(lambda _A	: type(lowerCAmelCase__	) not in self.src_skip							,      lowerCAmelCase__	)	)
								UpperCAmelCase_	:    List[Any]	  =    list(filter(lambda _A	: type(lowerCAmelCase__	) not in self.dest_skip							,      lowerCAmelCase__	)	)

								if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
											raise Exception(
											    F"Numbers of operations are different. Source module has {len(lowerCAmelCase__	)} operations while"
											    F" destination module has {len(lowerCAmelCase__	)}."	)

								for dest_m, src_m in zip(lowerCAmelCase__							,      lowerCAmelCase__	):
											dest_m.load_state_dict(src_m.state_dict()	)
											if self.verbose == 1:
														print(F"Transfered from={src_m} to={dest_m}"	)






def __UpperCAmelCase					( A :       str       ,    A :       Dict       ,    A :       str       ,    A :       Any = True						)  ->					str:
			print(F"Converting {name}..."						)
			with torch.no_grad():
						UpperCAmelCase_	:    Union[str, Any]	  =    timm.create_model(UpperCamelCase_       ,    pretrained=UpperCamelCase_						).eval()
						UpperCAmelCase_	:    int	  =    ResNetForImageClassification(UpperCamelCase_						).eval()
						UpperCAmelCase_	:    int	  =    ModuleTransfer(src=UpperCamelCase_       ,    dest=UpperCamelCase_						)
						UpperCAmelCase_	:    Union[str, Any]	  =    torch.randn((1, 3, 2_2_4, 2_2_4)						)
						module_transfer(UpperCamelCase_						)

			assert torch.allclose(from_model(UpperCamelCase_						)       ,    our_model(UpperCamelCase_						).logits						), "The model logits don't match the original one."

			UpperCAmelCase_	:    Tuple	  =    F"resnet{'-'.join(name.split('resnet'						)						)}"
			print(UpperCamelCase_						)

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name       ,    commit_message='''Add model'''       ,    use_temp_dir=UpperCamelCase_       ,    )

						# we can use the convnext one
						UpperCAmelCase_	:    Optional[int]	  =    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''						)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name       ,    commit_message='''Add image processor'''       ,    use_temp_dir=UpperCamelCase_       ,    )

						print(F"Pushed {checkpoint_name}"						)
def __UpperCAmelCase					( A :       Optional[int]       ,    A :       Dict = None       ,    A :       Optional[Any] = True						)  ->					Union[str, Any]:
			UpperCAmelCase_	:    Tuple	  =    '''imagenet-1k-id2label.json'''
			UpperCAmelCase_	:    Dict	  =    1_0_0_0
			UpperCAmelCase_	:    Tuple	  =    (1, num_labels)

			UpperCAmelCase_	:    Dict	  =    '''huggingface/label-files'''
			UpperCAmelCase_	:    Any	  =    num_labels
			UpperCAmelCase_	:    Optional[Any]	  =    json.load(open(hf_hub_download(UpperCamelCase_       ,    UpperCamelCase_       ,    repo_type='''dataset'''						)       ,    '''r'''						)						)
			UpperCAmelCase_	:    Optional[Any]	  =    {int(UpperCamelCase_						): v for k, v in idalabel.items()}

			UpperCAmelCase_	:    List[Any]	  =    idalabel
			UpperCAmelCase_	:    List[Any]	  =    {v: k for k, v in idalabel.items()}

			UpperCAmelCase_	:    Union[str, Any]	  =    partial(UpperCamelCase_       ,    num_labels=UpperCamelCase_       ,    idalabel=UpperCamelCase_       ,    labelaid=UpperCamelCase_						)

			UpperCAmelCase_	:    Union[str, Any]	  =    {
			    '''resnet18''': ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]       ,    hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]       ,    layer_type='''basic'''						),
			    '''resnet26''': ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]       ,    hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,    layer_type='''bottleneck'''						),
			    '''resnet34''': ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]       ,    hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]       ,    layer_type='''basic'''						),
			    '''resnet50''': ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]       ,    hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,    layer_type='''bottleneck'''						),
			    '''resnet101''': ImageNetPreTrainedConfig(
			        depths=[3, 4, 2_3, 3]       ,    hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,    layer_type='''bottleneck'''						),
			    '''resnet152''': ImageNetPreTrainedConfig(
			        depths=[3, 8, 3_6, 3]       ,    hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,    layer_type='''bottleneck'''						),
			}

			if model_name:
						convert_weight_and_push(UpperCamelCase_       ,    names_to_config[model_name]       ,    UpperCamelCase_       ,    UpperCamelCase_						)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(UpperCamelCase_       ,    UpperCamelCase_       ,    UpperCamelCase_       ,    UpperCamelCase_						)
			return config, expected_shape


if __name__ == "__main__":
						_UpperCamelCase		:		Union[str, Any]            = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default=None,
						    type=str,
						    help=(
						        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
						        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=Path,
						    required=True,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    default=True,
						    type=bool,
						    required=False,
						    help='If True, push model and image processor to the hub.',
						)

						_UpperCamelCase		:		List[Any]            = parser.parse_args()
						_UpperCamelCase		:		Any            = args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




"""simple docstring"""





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


__magic_name__       =			get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		XLNetTokenizer
		__lowercase							:  List[str]						   =		XLNetTokenizerFast
		__lowercase							:  List[Any]						   =		True
		__lowercase							:  int						   =		True




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

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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """<s>"""
				__SCREAMING_SNAKE_CASE            = 1

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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = 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(lowerCAmelCase__)							,  1_0_0_6)




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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = XLNetTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__)

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

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

				__SCREAMING_SNAKE_CASE            = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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""",
				        """.""",
				    ]							,  )
				__SCREAMING_SNAKE_CASE            = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
				self.assertListEqual(lowerCAmelCase__							,  [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])

				__SCREAMING_SNAKE_CASE            = tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = XLNetTokenizer(lowerCAmelCase__							,  do_lower_case=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = XLNetTokenizer(lowerCAmelCase__							,  do_lower_case=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = XLNetTokenizer.from_pretrained("""xlnet-base-cased""")

				__SCREAMING_SNAKE_CASE            = tokenizer.encode("""sequence builders"""							,  add_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.encode("""multi-sequence build"""							,  add_special_tokens=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__							,  lowerCAmelCase__)

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




		@slow
		def 	snake_case_     (     self):
				# fmt: off
				__SCREAMING_SNAKE_CASE            = {"""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=lowerCAmelCase__							,  model_name="""xlnet-base-cased"""							,  revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""							,  )




