

"""simple docstring"""






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 							_lowercase       :
     _lowerCamelCase	 =							42
     _lowerCamelCase	 =							field(default_factory=A__  )
     _lowerCamelCase	 =							field(default_factory=A__  )







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_			,    UpperCamelCase_			,    UpperCamelCase_							):
      __magic_name__	 =   len(list(m.modules()							)							) == 1 or isinstance(__snake_case			,    nn.Convad							) or isinstance(__snake_case			,    nn.BatchNormad							)
      if has_not_submodules:
       self.traced.append(__snake_case							)







     def __call__(   self			,    UpperCamelCase_							):
      for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook							)							)
      self.module(__snake_case							)
      [x.remove() for x in self.handles]
      return self







     @property
     def 						lowerCAmelCase__     (   self							):
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda UpperCamelCase_							: len(list(x.state_dict().keys()							)							) > 0			,    self.traced							)							)


@dataclass
class 							_lowercase       :
     _lowerCamelCase	 =							42
     _lowerCamelCase	 =							42
     _lowerCamelCase	 =							1
     _lowerCamelCase	 =							field(default_factory=A__  )
     _lowerCamelCase	 =							field(default_factory=A__  )
     _lowerCamelCase	 =							True







     def __call__(   self			,    UpperCamelCase_							):
      __magic_name__	 =   Tracker(self.dest							)(__snake_case							).parametrized
      __magic_name__	 =   Tracker(self.src							)(__snake_case							).parametrized

      __magic_name__	 =   list(filter(lambda UpperCamelCase_							: type(__snake_case							) not in self.src_skip			,    __snake_case							)							)
      __magic_name__	 =   list(filter(lambda UpperCamelCase_							: type(__snake_case							) not in self.dest_skip			,    __snake_case							)							)

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

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


class 							_lowercase       (  nn.Module  ):







     def __init__(   self			,    UpperCamelCase_							):
      super().__init__()

      __magic_name__	 =   []
      # - 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}'''
       __magic_name__	 =   len(__snake_case							) + 1
       feature_blocks.append((f'''res{block_index}''', v)							)

      __magic_name__	 =   nn.ModuleDict(__snake_case							)







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_							):
      return get_trunk_forward_outputs(
          __snake_case			,    out_feat_keys=__snake_case			,    feature_blocks=self._feature_blocks			,    )


class 							_lowercase       (  A__  ):







     def 						lowerCAmelCase__     (   self			,    UpperCamelCase_							):
      __magic_name__	 =   x.split('''-'''							)
      return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)







     def __getitem__(   self			,    UpperCamelCase_							):
      # default to timm!
      if x not in self:
       __magic_name__	 =   self.convert_name_to_timm(__snake_case							)
       __magic_name__	 =   partial(lambda: (timm.create_model(__snake_case			,    pretrained=__snake_case							).eval(), None)							)

      else:
       __magic_name__	 =   super().__getitem__(__snake_case							)

      return val


class 							_lowercase       (  A__  ):







     def __getitem__(   self			,    UpperCamelCase_							):
      if "seer" in x and "in1k" not in x:
       __magic_name__	 =   RegNetModel
      else:
       __magic_name__	 =   RegNetForImageClassification
      return val





def 						lowercase   ( __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase    )			->   str:
 for from_key, to_key in keys:
  __magic_name__	 =   from_state_dict[from_key].clone()
  print(f'''Copied key={from_key} to={to_key}'''    )
 return to_state_dict





def 						lowercase   ( __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase = True   , )			->   Union[str, Any]:
 print(f'''Converting {name}...'''    )
 with torch.no_grad():
  __magic_name__ ,    __magic_name__	 =   from_model_func()
  __magic_name__	 =   our_model_func(__lowerCAmelCase    ).eval()
  __magic_name__	 =   ModuleTransfer(src=__lowerCAmelCase   , dest=__lowerCAmelCase   , raise_if_mismatch=__lowerCAmelCase    )
  __magic_name__	 =   torch.randn((1, 3, 224, 224)    )
  module_transfer(__lowerCAmelCase    )

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

 __magic_name__	 =   our_model(__lowerCAmelCase   , output_hidden_states=__lowerCAmelCase    )
 __magic_name__	 =   (
     our_outputs.logits if isinstance(__lowerCAmelCase   , __lowerCAmelCase    ) else our_outputs.last_hidden_state
 )

 __magic_name__	 =   from_model(__lowerCAmelCase    )
 __magic_name__	 =   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:
  __magic_name__	 =   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   , )

  __magic_name__	 =   224 if '''seer''' not in name else 384
  # we can use the convnext one
  __magic_name__	 =   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 						lowercase   ( __UpperCamelCase   , __UpperCamelCase = None   , __UpperCamelCase = True    )			->   Optional[int]:
 __magic_name__	 =   '''imagenet-1k-id2label.json'''
 __magic_name__	 =   1000
 __magic_name__	 =   (1, num_labels)

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

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

 __magic_name__	 =   partial(__lowerCAmelCase   , num_labels=__lowerCAmelCase   , idalabel=__lowerCAmelCase   , labelaid=__lowerCAmelCase    )

 __magic_name__	 =   {
     '''regnet-x-002''': ImageNetPreTrainedConfig(
         depths=[1, 1, 4, 7]   , hidden_sizes=[24, 56, 152, 368]   , groups_width=8   , layer_type='''x'''    ),
     '''regnet-x-004''': ImageNetPreTrainedConfig(
         depths=[1, 2, 7, 12]   , hidden_sizes=[32, 64, 160, 384]   , groups_width=16   , layer_type='''x'''    ),
     '''regnet-x-006''': ImageNetPreTrainedConfig(
         depths=[1, 3, 5, 7]   , hidden_sizes=[48, 96, 240, 528]   , groups_width=24   , layer_type='''x'''    ),
     '''regnet-x-008''': ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 5]   , hidden_sizes=[64, 128, 288, 672]   , groups_width=16   , layer_type='''x'''    ),
     '''regnet-x-016''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 2]   , hidden_sizes=[72, 168, 408, 912]   , groups_width=24   , layer_type='''x'''    ),
     '''regnet-x-032''': ImageNetPreTrainedConfig(
         depths=[2, 6, 15, 2]   , hidden_sizes=[96, 192, 432, 1008]   , groups_width=48   , layer_type='''x'''    ),
     '''regnet-x-040''': ImageNetPreTrainedConfig(
         depths=[2, 5, 14, 2]   , hidden_sizes=[80, 240, 560, 1360]   , groups_width=40   , layer_type='''x'''    ),
     '''regnet-x-064''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]   , hidden_sizes=[168, 392, 784, 1624]   , groups_width=56   , layer_type='''x'''    ),
     '''regnet-x-080''': ImageNetPreTrainedConfig(
         depths=[2, 5, 15, 1]   , hidden_sizes=[80, 240, 720, 1920]   , groups_width=120   , layer_type='''x'''    ),
     '''regnet-x-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]   , hidden_sizes=[224, 448, 896, 2240]   , groups_width=112   , layer_type='''x'''    ),
     '''regnet-x-160''': ImageNetPreTrainedConfig(
         depths=[2, 6, 13, 1]   , hidden_sizes=[256, 512, 896, 2048]   , groups_width=128   , layer_type='''x'''    ),
     '''regnet-x-320''': ImageNetPreTrainedConfig(
         depths=[2, 7, 13, 1]   , hidden_sizes=[336, 672, 1344, 2520]   , groups_width=168   , layer_type='''x'''    ),
     # y variant
     '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]   , hidden_sizes=[24, 56, 152, 368]   , groups_width=8    ),
     '''regnet-y-004''': ImageNetPreTrainedConfig(
         depths=[1, 3, 6, 6]   , hidden_sizes=[48, 104, 208, 440]   , groups_width=8    ),
     '''regnet-y-006''': ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 4]   , hidden_sizes=[48, 112, 256, 608]   , groups_width=16    ),
     '''regnet-y-008''': ImageNetPreTrainedConfig(
         depths=[1, 3, 8, 2]   , hidden_sizes=[64, 128, 320, 768]   , groups_width=16    ),
     '''regnet-y-016''': ImageNetPreTrainedConfig(
         depths=[2, 6, 17, 2]   , hidden_sizes=[48, 120, 336, 888]   , groups_width=24    ),
     '''regnet-y-032''': ImageNetPreTrainedConfig(
         depths=[2, 5, 13, 1]   , hidden_sizes=[72, 216, 576, 1512]   , groups_width=24    ),
     '''regnet-y-040''': ImageNetPreTrainedConfig(
         depths=[2, 6, 12, 2]   , hidden_sizes=[128, 192, 512, 1088]   , groups_width=64    ),
     '''regnet-y-064''': ImageNetPreTrainedConfig(
         depths=[2, 7, 14, 2]   , hidden_sizes=[144, 288, 576, 1296]   , groups_width=72    ),
     '''regnet-y-080''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]   , hidden_sizes=[168, 448, 896, 2016]   , groups_width=56    ),
     '''regnet-y-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]   , hidden_sizes=[224, 448, 896, 2240]   , groups_width=112    ),
     '''regnet-y-160''': ImageNetPreTrainedConfig(
         depths=[2, 4, 11, 1]   , hidden_sizes=[224, 448, 1232, 3024]   , groups_width=112    ),
     '''regnet-y-320''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]   , hidden_sizes=[232, 696, 1392, 3712]   , groups_width=232    ),
     # models created by SEER -> https://arxiv.org/abs/2202.08360
     '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]   , hidden_sizes=[232, 696, 1392, 3712]   , groups_width=232    ),
     '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]   , hidden_sizes=[328, 984, 1968, 4920]   , groups_width=328    ),
     '''regnet-y-1280-seer''': RegNetConfig(
         depths=[2, 7, 17, 1]   , hidden_sizes=[528, 1056, 2904, 7392]   , groups_width=264    ),
     '''regnet-y-2560-seer''': RegNetConfig(
         depths=[3, 7, 16, 1]   , hidden_sizes=[640, 1696, 2544, 5088]   , groups_width=640    ),
     '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]   , hidden_sizes=[2020, 4040, 11110, 28280]   , groups_width=1010    ),
     # finetuned on imagenet
     '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]   , hidden_sizes=[232, 696, 1392, 3712]   , groups_width=232    ),
     '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]   , hidden_sizes=[328, 984, 1968, 4920]   , groups_width=328    ),
     '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]   , hidden_sizes=[528, 1056, 2904, 7392]   , groups_width=264    ),
     '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[3, 7, 16, 1]   , hidden_sizes=[640, 1696, 2544, 5088]   , groups_width=640    ),
     '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]   , hidden_sizes=[2020, 4040, 11110, 28280]   , groups_width=1010    ),
 }

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

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

 # pretrained
 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''   , lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )   , )

 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''   , lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )   , )

 __magic_name__	 =   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()    )   , )

 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''   , lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27   , group_width=1010   , w_a=1744   , w_a=620.83   , w_m=2.52    )    )    )   , )

 # IN1K finetuned
 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''   , lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )   , )

 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''   , lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )   , )

 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''   , lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )   , )

 __magic_name__	 =   partial(
     __lowerCAmelCase   , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''   , lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27   , group_width=1010   , w_a=1744   , 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)








'''simple docstring'''




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 			_lowerCAmelCase		(			A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   DebertaTokenizer
     snake_case_						      =   True
     snake_case_						      =   DebertaTokenizerFast



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        snake_case 						=   [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """[UNK]""",
        ]
        snake_case 						=   dict(zip(__snake_case   ,       range(len(__snake_case						)						)						)						)
        snake_case 						=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        snake_case 						=   {"""unk_token""": """[UNK]"""}

        snake_case 						=   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
        snake_case 						=   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""merges_file"""]						)
        with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""						) as fp:
           fp.write(json.dumps(__snake_case						) + """\n"""						)
        with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""						) as fp:
           fp.write("""\n""".join(__snake_case						)						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       **__snake_case							:			Union[str, Any]						)->	int:
        kwargs.update(self.special_tokens_map						)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			int						)->	Any:
        snake_case 						=   """lower newer"""
        snake_case 						=   """lower newer"""
        return input_text, output_text



     def      lowerCAmelCase			(   self							:			str						)->	Any:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   """lower newer"""
        snake_case 						=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        snake_case 						=   tokenizer.tokenize(__snake_case						)
        self.assertListEqual(__snake_case   ,       __snake_case						)

        snake_case 						=   tokens + [tokenizer.unk_token]
        snake_case 						=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case						)   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	List[str]:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   tokenizer("""Hello"""   ,       """World"""						)
        snake_case 						=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["""token_type_ids"""]   ,       __snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case 						=   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

        snake_case 						=   tokenizer.encode("""sequence builders"""   ,       add_special_tokens=__snake_case						)
        snake_case 						=   tokenizer.encode("""multi-sequence build"""   ,       add_special_tokens=__snake_case						)

        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)
        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       """multi-sequence build"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)

        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case						)
        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case   ,       __snake_case						)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case 						=   [self.tokenizer_class]
        if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

        for tokenizer_class in tokenizer_classes:
           snake_case 						=   tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

           snake_case 						=   [
               """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
               """ALBERT incorporates two parameter reduction techniques""",
               """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
               """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
               """ vocabulary embedding.""",
           ]

           snake_case 						=   tokenizer(__snake_case   ,       padding=__snake_case						)
           snake_case 						=   [tokenizer.decode(__snake_case   ,       skip_special_tokens=__snake_case						) for seq in encoding["""input_ids"""]]

           # fmt: off
           snake_case 						=   {
               """input_ids""": [
                   [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
               ],
               """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               """attention_mask""": [
                   [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],
                   [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],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           snake_case 						=   [
               """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
               """ALBERT incorporates two parameter reduction techniques""",
               """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
               """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
               """ vocabulary embedding.""",
           ]

           self.assertDictEqual(encoding.data   ,       __snake_case						)

           for expected, decoded in zip(__snake_case   ,       __snake_case						):
              self.assertEqual(__snake_case   ,       __snake_case						)

