






"""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()
_SCREAMING_SNAKE_CASE					:				int					    =						logging.get_logger()






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     List[nn.Module]						= field(default_factory=__snake_case		)
    SCREAMING_SNAKE_CASE						:     list						= field(default_factory=__snake_case		)






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Tensor       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		Any:
      lowerCamelCase_         =						len(list(m.modules()					)					) == 1 or isinstance(__SCREAMING_SNAKE_CASE       ,						nn.Convad					) or isinstance(__SCREAMING_SNAKE_CASE       ,						nn.BatchNormad					)
      if has_not_submodules:
        self.traced.append(__SCREAMING_SNAKE_CASE					)






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






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






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     nn.Module
    SCREAMING_SNAKE_CASE						:     int						= 1
    SCREAMING_SNAKE_CASE						:     List						= field(default_factory=__snake_case		)
    SCREAMING_SNAKE_CASE						:     List						= field(default_factory=__snake_case		)
    SCREAMING_SNAKE_CASE						:     bool						= True






    def __call__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		Tuple:
      lowerCamelCase_         =						Tracker(self.dest					)(__SCREAMING_SNAKE_CASE					).parametrized
      lowerCamelCase_         =						Tracker(self.src					)(__SCREAMING_SNAKE_CASE					).parametrized

      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: type(__SCREAMING_SNAKE_CASE					) not in self.src_skip       ,						__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: type(__SCREAMING_SNAKE_CASE					) not in self.dest_skip       ,						__SCREAMING_SNAKE_CASE					)					)

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

      for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_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      a						(					nn.Module		):






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							nn.Module					)     ->		Tuple:
      super().__init__()

      lowerCamelCase_         =						[]
      # - 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}'''
        lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					) + 1
        feature_blocks.append((F'''res{block_index}''', v)					)

      lowerCamelCase_         =						nn.ModuleDict(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tensor					)     ->		int:
      return get_trunk_forward_outputs(
          __SCREAMING_SNAKE_CASE       ,						out_feat_keys=__SCREAMING_SNAKE_CASE       ,						feature_blocks=self._feature_blocks       ,						)






class      a						(					__snake_case		):






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		str:
      lowerCamelCase_         =						x.split('-'					)
      return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)






    def __getitem__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		Callable[[], Tuple[nn.Module, Dict]]:
      # default to timm!
      if x not in self:
        lowerCamelCase_         =						self.convert_name_to_timm(__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						partial(lambda: (timm.create_model(__SCREAMING_SNAKE_CASE       ,						pretrained=__SCREAMING_SNAKE_CASE					).eval(), None)					)

      else:
        lowerCamelCase_         =						super().__getitem__(__SCREAMING_SNAKE_CASE					)

      return val






class      a						(					__snake_case		):






    def __getitem__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		Callable[[], nn.Module]:
      if "seer" in x and "in1k" not in x:
        lowerCamelCase_         =						RegNetModel
      else:
        lowerCamelCase_         =						RegNetForImageClassification
      return val







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		List[Tuple[str, str]] )      ->				Tuple:
  for from_key, to_key in keys:
    lowerCamelCase_         =						from_state_dict[from_key].clone()
    print(F'''Copied key={from_key} to={to_key}''' )
  return to_state_dict







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Callable[[], nn.Module]		,				_lowerCamelCase       :		Callable[[], nn.Module]		,				_lowerCamelCase       :		RegNetConfig		,				_lowerCamelCase       :		Path		,				_lowerCamelCase       :		bool = True		,				)      ->				Dict:
  print(F'''Converting {name}...''' )
  with torch.no_grad():
    lowerCamelCase_     ,	lowerCamelCase_         =						from_model_func()
    lowerCamelCase_         =						our_model_func(_lowerCamelCase ).eval()
    lowerCamelCase_         =						ModuleTransfer(src=_lowerCamelCase		,				dest=_lowerCamelCase		,				raise_if_mismatch=_lowerCamelCase )
    lowerCamelCase_         =						torch.randn((1, 3, 224, 224) )
    module_transfer(_lowerCamelCase )

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

  lowerCamelCase_         =						our_model(_lowerCamelCase		,				output_hidden_states=_lowerCamelCase )
  lowerCamelCase_         =						(
      our_outputs.logits if isinstance(_lowerCamelCase		,				_lowerCamelCase ) else our_outputs.last_hidden_state
  )

  lowerCamelCase_         =						from_model(_lowerCamelCase )
  lowerCamelCase_         =						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:
    lowerCamelCase_         =						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		,				)

    lowerCamelCase_         =						224 if 'seer' not in name else 384
    # we can use the convnext one
    lowerCamelCase_         =						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      lowerCamelCase__       (							_lowerCamelCase       :		Path		,				_lowerCamelCase       :		str = None		,				_lowerCamelCase       :		bool = True )      ->				Tuple:
  lowerCamelCase_         =						'imagenet-1k-id2label.json'
  lowerCamelCase_         =						1000
  lowerCamelCase_         =						(1, num_labels)

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

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

  lowerCamelCase_         =						partial(_lowerCamelCase		,				num_labels=_lowerCamelCase		,				idalabel=_lowerCamelCase		,				labelaid=_lowerCamelCase )

  lowerCamelCase_         =						{
      '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 ),
  }

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

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

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

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

  lowerCamelCase_         =						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() )		,				)

  lowerCamelCase_         =						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=6_20.83		,				w_m=2.52 ) ) )		,				)

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

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

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

  lowerCamelCase_         =						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=6_20.83		,				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__":
  _SCREAMING_SNAKE_CASE					:				Optional[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 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.''',
  )

  _SCREAMING_SNAKE_CASE					:				Dict					    =						parser.parse_args()

  _SCREAMING_SNAKE_CASE					:				Path					    =						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 os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     List[str]						= MobileBertTokenizer
    SCREAMING_SNAKE_CASE						:     int						= MobileBertTokenizerFast
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= True
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= True
    SCREAMING_SNAKE_CASE						:     Dict						= filter_non_english
    SCREAMING_SNAKE_CASE						:     str						= """google/mobilebert-uncased"""






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Dict:
      super().setUp()

      lowerCamelCase_         =						[
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      lowerCamelCase_         =						os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['vocab_file']					)
      with open(self.vocab_file       ,						'w'       ,						encoding='utf-8'					) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

      lowerCamelCase_         =						[
          (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
          for tokenizer_def in self.tokenizers_list
      ]






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		int:
      lowerCamelCase_         =						'UNwant\u00E9d,running'
      lowerCamelCase_         =						'unwanted, running'
      return input_text, output_text






    def    UpperCamelCase       (       self     :							Dict					)     ->		Any:
      lowerCamelCase_         =						self.tokenizer_class(self.vocab_file					)

      lowerCamelCase_         =						tokenizer.tokenize('UNwant\u00E9d,running'					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						['un', '##want', '##ed', ',', 'runn', '##ing']					)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)       ,						[9, 6, 7, 12, 10, 11]					)






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      if not self.test_rust_tokenizer:
        return

      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      lowerCamelCase_         =						'UNwant\u00E9d,running'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      # With lower casing
      lowerCamelCase_         =						self.get_tokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.get_rust_tokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						'UNwant\u00E9d,running'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		str:
      lowerCamelCase_         =						BasicTokenizer()

      self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'					)       ,						['ah', '\u535A', '\u63A8', 'zz']					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)       ,						['hello', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Any:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hällo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['h\u00E9llo']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Union[str, Any]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hallo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		List[str]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hallo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Any:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)       ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Tuple:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['HäLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[Any]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['HaLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		str:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						never_split=['[UNK]']					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'					)       ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Any:
      lowerCamelCase_         =						['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

      lowerCamelCase_         =						{}
      for i, token in enumerate(__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						i
      lowerCamelCase_         =						WordpieceTokenizer(vocab=__SCREAMING_SNAKE_CASE       ,						unk_token='[UNK]'					)

      self.assertListEqual(tokenizer.tokenize(''					)       ,						[]					)

      self.assertListEqual(tokenizer.tokenize('unwanted running'					)       ,						['un', '##want', '##ed', 'runn', '##ing']					)

      self.assertListEqual(tokenizer.tokenize('unwantedX running'					)       ,						['[UNK]', 'runn', '##ing']					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Any:
      self.assertTrue(_is_whitespace(' '					)					)
      self.assertTrue(_is_whitespace('\t'					)					)
      self.assertTrue(_is_whitespace('\r'					)					)
      self.assertTrue(_is_whitespace('\n'					)					)
      self.assertTrue(_is_whitespace('\u00A0'					)					)

      self.assertFalse(_is_whitespace('A'					)					)
      self.assertFalse(_is_whitespace('-'					)					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      self.assertTrue(_is_control('\u0005'					)					)

      self.assertFalse(_is_control('A'					)					)
      self.assertFalse(_is_control(' '					)					)
      self.assertFalse(_is_control('\t'					)					)
      self.assertFalse(_is_control('\r'					)					)






    def    UpperCamelCase       (       self     :							str					)     ->		Optional[Any]:
      self.assertTrue(_is_punctuation('-'					)					)
      self.assertTrue(_is_punctuation('$'					)					)
      self.assertTrue(_is_punctuation('`'					)					)
      self.assertTrue(_is_punctuation('.'					)					)

      self.assertFalse(_is_punctuation('A'					)					)
      self.assertFalse(_is_punctuation(' '					)					)






    def    UpperCamelCase       (       self     :							int					)     ->		List[Any]:
      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
      self.assertListEqual([tokenizer.tokenize(__SCREAMING_SNAKE_CASE					) for t in ['Test', '\xad', 'test']]       ,						[['[UNK]'], [], ['[UNK]']]					)

      self.assertListEqual(
          [rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					) for t in ['Test', '\xad', 'test']]       ,						[['[UNK]'], [], ['[UNK]']]					)






    @slow
    def    UpperCamelCase       (       self     :							Dict					)     ->		List[str]:
      lowerCamelCase_         =						self.tokenizer_class.from_pretrained('google/mobilebert-uncased'					)

      lowerCamelCase_         =						tokenizer.encode('sequence builders'       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer.encode('multi-sequence build'       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

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

      assert encoded_sentence == [101] + text + [102]
      assert encoded_pair == [101] + text + [102] + text_a + [102]






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Union[str, Any]:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
          lowerCamelCase_         =						tokenizer_r.encode_plus(
              __SCREAMING_SNAKE_CASE       ,						return_attention_mask=__SCREAMING_SNAKE_CASE       ,						return_token_type_ids=__SCREAMING_SNAKE_CASE       ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE       ,						)

          lowerCamelCase_         =						tokenizer_r.do_lower_case if hasattr(__SCREAMING_SNAKE_CASE       ,						'do_lower_case'					) else False
          lowerCamelCase_         =						(
              [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), 'A'),
                  ((1, 2), ','),
                  ((3, 5), 'na'),
                  ((5, 6), '##ï'),
                  ((6, 8), '##ve'),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), 'Allen'),
                  ((21, 23), '##NL'),
                  ((23, 24), '##P'),
                  ((25, 33), 'sentence'),
                  ((33, 34), '.'),
                  ((0, 0), tokenizer_r.sep_token),
              ]
              if not do_lower_case
              else [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), 'a'),
                  ((1, 2), ','),
                  ((3, 8), 'naive'),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), 'allen'),
                  ((21, 23), '##nl'),
                  ((23, 24), '##p'),
                  ((25, 33), 'sentence'),
                  ((33, 34), '.'),
                  ((0, 0), tokenizer_r.sep_token),
              ]
          )

          self.assertEqual(
              [e[1] for e in expected_results]       ,						tokenizer_r.convert_ids_to_tokens(tokens['input_ids']					)					)
          self.assertEqual([e[0] for e in expected_results]       ,						tokens['offset_mapping']					)






    def    UpperCamelCase       (       self     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						['的', '人', '有']
      lowerCamelCase_         =						''.join(__SCREAMING_SNAKE_CASE					)
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						True
          lowerCamelCase_         =						self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_p.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_r.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)

          # it is expected that each Chinese character is not preceded by "##"
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						False
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)

          # it is expected that only the first Chinese character is not preceded by "##".
          lowerCamelCase_         =						[
              F'''##{token}''' if idx != 0 else token for idx, token in enumerate(__SCREAMING_SNAKE_CASE					)
          ]
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)




