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

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


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







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	len(list(m.modules()				)				) == 1 or isinstance(a       ,							nn.Convad				) or isinstance(a       ,							nn.BatchNormad				)
								if has_not_submodules:
										self.traced.append(a				)

						def __call__(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

								for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook				)				)
								self.module(a				)
								[x.remove() for x in self.handles]
								return self







						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		1
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: bool	=		True

						def __call__(			self       ,							a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				) and self.raise_if_mismatch:
										raise Exception(
										    F'Numbers of operations are different. Source module has {len(a				)} operations while'
										    F' destination module has {len(a				)}.'				)

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







class        lowerCAmelCase__      (   nn.Module	):

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__()

								_UpperCamelCase						    =	[]
								# - get the stem
								feature_blocks.append(("""conv1""", model.stem)				)
								# - get all the feature blocks
								for k, v in model.trunk_output.named_children():
										assert k.startswith("""block"""				), F'Unexpected layer name {k}'
										_UpperCamelCase						    =	len(a				) + 1
										feature_blocks.append((F'res{block_index}', v)				)

								_UpperCamelCase						    =	nn.ModuleDict(a				)







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


								'''simple docstring'''

								return get_trunk_forward_outputs(
								    a       ,							out_feat_keys=a       ,							feature_blocks=self._feature_blocks       ,							)







class        lowerCAmelCase__      (   __lowercase	):

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	x.split("""-"""				)
								return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)







						def __getitem__(			self       ,							a				)					->      Callable[[], Tuple[nn.Module, Dict]]:


								'''simple docstring'''

								if x not in self:
										_UpperCamelCase						    =	self.convert_name_to_timm(a				)
										_UpperCamelCase						    =	partial(lambda: (timm.create_model(a       ,							pretrained=a				).eval(), None)				)

								else:
										_UpperCamelCase						    =	super().__getitem__(a				)

								return val







class        lowerCAmelCase__      (   __lowercase	):

						def __getitem__(			self       ,							a				)					->      Callable[[], nn.Module]:


								'''simple docstring'''

								if "seer" in x and "in1k" not in x:
										_UpperCamelCase						    =	RegNetModel
								else:
										_UpperCamelCase						    =	RegNetForImageClassification
								return val




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

		"""simple docstring"""






		for from_key, to_key in keys:
				_UpperCamelCase						    =	from_state_dict[from_key].clone()
				print(F'Copied key={from_key} to={to_key}' )
		return to_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True						,  )		->				Tuple:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase    ,	_UpperCamelCase						    =	from_model_func()
				_UpperCamelCase						    =	our_model_func(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase						,  raise_if_mismatch=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

		if from_state_dict is not None:
				_UpperCamelCase						    =	[]
				# for seer - in1k finetuned we have to manually copy the head
				if "seer" in name and "in1k" in name:
						_UpperCamelCase						    =	[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
				_UpperCamelCase						    =	manually_copy_vissl_head(lowerCAmelCase						,  our_model.state_dict()						,  lowerCAmelCase )
				our_model.load_state_dict(lowerCAmelCase )

		_UpperCamelCase						    =	our_model(lowerCAmelCase						,  output_hidden_states=lowerCAmelCase )
		_UpperCamelCase						    =	(
		    our_outputs.logits if isinstance(lowerCAmelCase						,  lowerCAmelCase ) else our_outputs.last_hidden_state
		)

		_UpperCamelCase						    =	from_model(lowerCAmelCase )
		_UpperCamelCase						    =	from_output[-1] if type(lowerCAmelCase ) is list else from_output

		# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
		if "seer" in name and "in1k" in name:
				_UpperCamelCase						    =	our_outputs.hidden_states[-1]

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

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / name						,  commit_message="""Add model"""						,  use_temp_dir=lowerCAmelCase						,  )

				_UpperCamelCase						    =	2_2_4 if """seer""" not in name else 3_8_4
				# we can use the convnext one
				_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""						,  size=lowerCAmelCase )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name						,  commit_message="""Add image processor"""						,  use_temp_dir=lowerCAmelCase						,  )

				print(F'Pushed {name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(cached_download(hf_hub_url(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" ) )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """regnet-x-002""": ImageNetPreTrainedConfig(
		        depths=[1, 1, 4, 7]						,  hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]						,  groups_width=8						,  layer_type="""x""" ),
		    """regnet-x-004""": ImageNetPreTrainedConfig(
		        depths=[1, 2, 7, 1_2]						,  hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]						,  groups_width=1_6						,  layer_type="""x""" ),
		    """regnet-x-006""": ImageNetPreTrainedConfig(
		        depths=[1, 3, 5, 7]						,  hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]						,  groups_width=2_4						,  layer_type="""x""" ),
		    """regnet-x-008""": ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 5]						,  hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]						,  groups_width=1_6						,  layer_type="""x""" ),
		    """regnet-x-016""": ImageNetPreTrainedConfig(
		        depths=[2, 4, 1_0, 2]						,  hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]						,  groups_width=2_4						,  layer_type="""x""" ),
		    """regnet-x-032""": ImageNetPreTrainedConfig(
		        depths=[2, 6, 1_5, 2]						,  hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]						,  groups_width=4_8						,  layer_type="""x""" ),
		    """regnet-x-040""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_4, 2]						,  hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]						,  groups_width=4_0						,  layer_type="""x""" ),
		    """regnet-x-064""": ImageNetPreTrainedConfig(
		        depths=[2, 4, 1_0, 1]						,  hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]						,  groups_width=5_6						,  layer_type="""x""" ),
		    """regnet-x-080""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_5, 1]						,  hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]						,  groups_width=1_2_0						,  layer_type="""x""" ),
		    """regnet-x-120""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_1, 1]						,  hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]						,  groups_width=1_1_2						,  layer_type="""x""" ),
		    """regnet-x-160""": ImageNetPreTrainedConfig(
		        depths=[2, 6, 1_3, 1]						,  hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]						,  groups_width=1_2_8						,  layer_type="""x""" ),
		    """regnet-x-320""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_3, 1]						,  hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]						,  groups_width=1_6_8						,  layer_type="""x""" ),
		    # y variant
		    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]						,  hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]						,  groups_width=8 ),
		    """regnet-y-004""": ImageNetPreTrainedConfig(
		        depths=[1, 3, 6, 6]						,  hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]						,  groups_width=8 ),
		    """regnet-y-006""": ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 4]						,  hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]						,  groups_width=1_6 ),
		    """regnet-y-008""": ImageNetPreTrainedConfig(
		        depths=[1, 3, 8, 2]						,  hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]						,  groups_width=1_6 ),
		    """regnet-y-016""": ImageNetPreTrainedConfig(
		        depths=[2, 6, 1_7, 2]						,  hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]						,  groups_width=2_4 ),
		    """regnet-y-032""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_3, 1]						,  hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]						,  groups_width=2_4 ),
		    """regnet-y-040""": ImageNetPreTrainedConfig(
		        depths=[2, 6, 1_2, 2]						,  hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						,  groups_width=6_4 ),
		    """regnet-y-064""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_4, 2]						,  hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]						,  groups_width=7_2 ),
		    """regnet-y-080""": ImageNetPreTrainedConfig(
		        depths=[2, 4, 1_0, 1]						,  hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]						,  groups_width=5_6 ),
		    """regnet-y-120""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_1, 1]						,  hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]						,  groups_width=1_1_2 ),
		    """regnet-y-160""": ImageNetPreTrainedConfig(
		        depths=[2, 4, 1_1, 1]						,  hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]						,  groups_width=1_1_2 ),
		    """regnet-y-320""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_2, 1]						,  hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]						,  groups_width=2_3_2 ),
		    # models created by SEER -> https://arxiv.org/abs/2202.08360
		    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]						,  hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]						,  groups_width=2_3_2 ),
		    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]						,  hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]						,  groups_width=3_2_8 ),
		    """regnet-y-1280-seer""": RegNetConfig(
		        depths=[2, 7, 1_7, 1]						,  hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]						,  groups_width=2_6_4 ),
		    """regnet-y-2560-seer""": RegNetConfig(
		        depths=[3, 7, 1_6, 1]						,  hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]						,  groups_width=6_4_0 ),
		    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_7, 1]						,  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]						,  groups_width=1_0_1_0 ),
		    # finetuned on imagenet
		    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_2, 1]						,  hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]						,  groups_width=2_3_2 ),
		    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 5, 1_2, 1]						,  hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]						,  groups_width=3_2_8 ),
		    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_7, 1]						,  hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]						,  groups_width=2_6_4 ),
		    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[3, 7, 1_6, 1]						,  hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]						,  groups_width=6_4_0 ),
		    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_7, 1]						,  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]						,  groups_width=1_0_1_0 ),
		}

		_UpperCamelCase						    =	NameToOurModelFuncMap()
		_UpperCamelCase						    =	NameToFromModelFuncMap()
		# add seer weights logic

		def load_using_classy_vision(lowerCAmelCase						,  lowerCAmelCase ) -> Tuple[nn.Module, Dict]:
				_UpperCamelCase						    =	torch.hub.load_state_dict_from_url(lowerCAmelCase						,  model_dir=str(lowerCAmelCase )						,  map_location="""cpu""" )
				_UpperCamelCase						    =	model_func()
				# check if we have a head, if yes add it
				_UpperCamelCase						    =	files["""classy_state_dict"""]["""base_model"""]["""model"""]
				_UpperCamelCase						    =	model_state_dict["""trunk"""]
				model.load_state_dict(lowerCAmelCase )
				return model.eval(), model_state_dict["heads"]

		# pretrained
		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""						,  lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=2_7						,  group_width=1_0_1_0						,  w_a=1_7_4_4						,  w_a=620.83						,  w_m=2.52 ) ) )						,  )

		# IN1K finetuned
		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )						,  )

		_UpperCamelCase						    =	partial(
		    lowerCAmelCase						,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""						,  lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=2_7						,  group_width=1_0_1_0						,  w_a=1_7_4_4						,  w_a=620.83						,  w_m=2.52 ) ) )						,  )

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


if __name__ == "__main__":
		lowerCamelCase__	=							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 regnet* architecture,"
		        " currently: regnetx-*, regnety-*. 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.",
		)

		lowerCamelCase__	=							parser.parse_args()

		lowerCamelCase__	=							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)





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"""       ,							)



