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()
a_							=      logging.get_logger()


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	List[nn.Module]    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	list    		= field(default_factory=snake_case					)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Tensor				,				__lowerCAmelCase:			Tensor   ) -> List[str]:




    '''simple docstring'''




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






  def __call__(				self:			Dict				,				__lowerCAmelCase:			Tensor   ) -> Any:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> int:




    '''simple docstring'''




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






@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	List    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	List    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	bool    		= True






  def __call__(				self:			Union[str, Any]				,				__lowerCAmelCase:			Tensor   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       Tracker(self.dest   )(__lowerCAmelCase   ).parametrized
    __UpperCAmelCase						    =       Tracker(self.src   )(__lowerCAmelCase   ).parametrized

    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : type(__lowerCAmelCase   ) not in self.src_skip				,				__lowerCAmelCase   )   )
    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : type(__lowerCAmelCase   ) not in self.dest_skip				,				__lowerCAmelCase   )   )

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

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






class 			UpperCAmelCase__    (		nn.Module					):



  """simple docstring"""






  def __init__(				self:			List[str]				,				__lowerCAmelCase:			nn.Module   ) -> 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(__lowerCAmelCase   ) + 1
      feature_blocks.append((F'''res{block_index}''', v)   )

    __UpperCAmelCase						    =       nn.ModuleDict(__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Tensor   ) -> List[Any]:




    '''simple docstring'''




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






class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			str   ) -> str:




    '''simple docstring'''




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






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




    '''simple docstring'''




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

    else:
      __UpperCAmelCase						    =       super().__getitem__(__lowerCAmelCase   )

    return val






class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






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




    '''simple docstring'''




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






def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : Dict						,	A_   : List[Tuple[str, str]]					)    ->  int:
  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       __lowerCAmelCase      (						A_   : str						,	A_   : Callable[[], nn.Module]						,	A_   : Callable[[], nn.Module]						,	A_   : RegNetConfig						,	A_   : Path						,	A_   : bool = True						,	)    ->  Tuple:
  print(F'''Converting {name}...'''					)
  with torch.no_grad():
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       from_model_func()
    __UpperCAmelCase						    =       our_model_func(A_					).eval()
    __UpperCAmelCase						    =       ModuleTransfer(src=A_						,	dest=A_						,	raise_if_mismatch=A_					)
    __UpperCAmelCase						    =       torch.randn((1, 3, 2_24, 2_24)					)
    module_transfer(A_					)

  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(A_						,	our_model.state_dict()						,	A_					)
    our_model.load_state_dict(A_					)

  __UpperCAmelCase						    =       our_model(A_						,	output_hidden_states=A_					)
  __UpperCAmelCase						    =       (
      our_outputs.logits if isinstance(A_						,	A_					) else our_outputs.last_hidden_state
  )

  __UpperCAmelCase						    =       from_model(A_					)
  __UpperCAmelCase						    =       from_output[-1] if type(A_					) 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(A_						,	A_					), "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=A_						,	)

    __UpperCAmelCase						    =       2_24 if "seer" not in name else 3_84
    # we can use the convnext one
    __UpperCAmelCase						    =       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						,	size=A_					)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name						,	commit_message="Add image processor"						,	use_temp_dir=A_						,	)

    print(F'''Pushed {name}'''					)




def       __lowerCAmelCase      (						A_   : Path						,	A_   : str = None						,	A_   : bool = True					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       10_00
  __UpperCAmelCase						    =       (1, num_labels)

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

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

  __UpperCAmelCase						    =       partial(A_						,	num_labels=A_						,	idalabel=A_						,	labelaid=A_					)

  __UpperCAmelCase						    =       {
      "regnet-x-002": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]						,	hidden_sizes=[24, 56, 1_52, 3_68]						,	groups_width=8						,	layer_type="x"					),
      "regnet-x-004": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]						,	hidden_sizes=[32, 64, 1_60, 3_84]						,	groups_width=16						,	layer_type="x"					),
      "regnet-x-006": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]						,	hidden_sizes=[48, 96, 2_40, 5_28]						,	groups_width=24						,	layer_type="x"					),
      "regnet-x-008": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]						,	hidden_sizes=[64, 1_28, 2_88, 6_72]						,	groups_width=16						,	layer_type="x"					),
      "regnet-x-016": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]						,	hidden_sizes=[72, 1_68, 4_08, 9_12]						,	groups_width=24						,	layer_type="x"					),
      "regnet-x-032": ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]						,	hidden_sizes=[96, 1_92, 4_32, 10_08]						,	groups_width=48						,	layer_type="x"					),
      "regnet-x-040": ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]						,	hidden_sizes=[80, 2_40, 5_60, 13_60]						,	groups_width=40						,	layer_type="x"					),
      "regnet-x-064": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]						,	hidden_sizes=[1_68, 3_92, 7_84, 16_24]						,	groups_width=56						,	layer_type="x"					),
      "regnet-x-080": ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]						,	hidden_sizes=[80, 2_40, 7_20, 19_20]						,	groups_width=1_20						,	layer_type="x"					),
      "regnet-x-120": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]						,	hidden_sizes=[2_24, 4_48, 8_96, 22_40]						,	groups_width=1_12						,	layer_type="x"					),
      "regnet-x-160": ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]						,	hidden_sizes=[2_56, 5_12, 8_96, 20_48]						,	groups_width=1_28						,	layer_type="x"					),
      "regnet-x-320": ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]						,	hidden_sizes=[3_36, 6_72, 13_44, 25_20]						,	groups_width=1_68						,	layer_type="x"					),
      # y variant
      "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]						,	hidden_sizes=[24, 56, 1_52, 3_68]						,	groups_width=8					),
      "regnet-y-004": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]						,	hidden_sizes=[48, 1_04, 2_08, 4_40]						,	groups_width=8					),
      "regnet-y-006": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]						,	hidden_sizes=[48, 1_12, 2_56, 6_08]						,	groups_width=16					),
      "regnet-y-008": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]						,	hidden_sizes=[64, 1_28, 3_20, 7_68]						,	groups_width=16					),
      "regnet-y-016": ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]						,	hidden_sizes=[48, 1_20, 3_36, 8_88]						,	groups_width=24					),
      "regnet-y-032": ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]						,	hidden_sizes=[72, 2_16, 5_76, 15_12]						,	groups_width=24					),
      "regnet-y-040": ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]						,	hidden_sizes=[1_28, 1_92, 5_12, 10_88]						,	groups_width=64					),
      "regnet-y-064": ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]						,	hidden_sizes=[1_44, 2_88, 5_76, 12_96]						,	groups_width=72					),
      "regnet-y-080": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]						,	hidden_sizes=[1_68, 4_48, 8_96, 20_16]						,	groups_width=56					),
      "regnet-y-120": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]						,	hidden_sizes=[2_24, 4_48, 8_96, 22_40]						,	groups_width=1_12					),
      "regnet-y-160": ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]						,	hidden_sizes=[2_24, 4_48, 12_32, 30_24]						,	groups_width=1_12					),
      "regnet-y-320": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]						,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,	groups_width=2_32					),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]						,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,	groups_width=2_32					),
      "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]						,	hidden_sizes=[3_28, 9_84, 19_68, 49_20]						,	groups_width=3_28					),
      "regnet-y-1280-seer": RegNetConfig(
          depths=[2, 7, 17, 1]						,	hidden_sizes=[5_28, 10_56, 29_04, 73_92]						,	groups_width=2_64					),
      "regnet-y-2560-seer": RegNetConfig(
          depths=[3, 7, 16, 1]						,	hidden_sizes=[6_40, 16_96, 25_44, 50_88]						,	groups_width=6_40					),
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]						,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,	groups_width=10_10					),
      # finetuned on imagenet
      "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]						,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,	groups_width=2_32					),
      "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]						,	hidden_sizes=[3_28, 9_84, 19_68, 49_20]						,	groups_width=3_28					),
      "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]						,	hidden_sizes=[5_28, 10_56, 29_04, 73_92]						,	groups_width=2_64					),
      "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]						,	hidden_sizes=[6_40, 16_96, 25_44, 50_88]						,	groups_width=6_40					),
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]						,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,	groups_width=10_10					),
  }

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

  def load_using_classy_vision(A_   : str						,	A_   : Callable[[], nn.Module]					) -> Tuple[nn.Module, Dict]:
    __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	model_dir=str(A_					)						,	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(A_					)
    return model.eval(), model_state_dict["heads"]

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

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

  __UpperCAmelCase						    =       partial(
      A_						,	"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(
      A_						,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,	lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27						,	group_width=10_10						,	w_a=17_44						,	w_a=6_20.83						,	w_m=2.52					)					)					)						,	)

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

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

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

  __UpperCAmelCase						    =       partial(
      A_						,	"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=10_10						,	w_a=17_44						,	w_a=6_20.83						,	w_m=2.52					)					)					)						,	)

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


if __name__ == "__main__":
       a_							=      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.""",
       )

       a_							=      parser.parse_args()

       a_							=      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 json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin


@require_phonemizer
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Any    		= WavaVecaPhonemeCTCTokenizer
  lowerCAmelCase__		:	List[str]    		= False






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    super().setUp()

    __UpperCAmelCase						    =       (
        "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
        "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
        "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
        "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
        "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
        "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
        "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
        "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
        "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
        "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
        "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
        "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
        "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
    ).split(" "   )
    __UpperCAmelCase						    =       dict(zip(__lowerCAmelCase				,				range(len(__lowerCAmelCase   )   )   )   )

    __UpperCAmelCase						    =       {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Dict=False				,				__lowerCAmelCase:			Union[str, Any]=20				,				__lowerCAmelCase:			Tuple=5   ) -> Tuple[str, list]:




    '''simple docstring'''




    __UpperCAmelCase						    =       [(i, tokenizer.decode([i]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )) for i in range(len(__lowerCAmelCase   )   )]
    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : [t[0]] == tokenizer.encode(t[1]				,				do_phonemize=__lowerCAmelCase   )				,				__lowerCAmelCase   )   )
    if max_length is not None and len(__lowerCAmelCase   ) > max_length:
      __UpperCAmelCase						    =       toks[:max_length]
    if min_length is not None and len(__lowerCAmelCase   ) < min_length and len(__lowerCAmelCase   ) > 0:
      while len(__lowerCAmelCase   ) < min_length:
        __UpperCAmelCase						    =       toks + toks
        # toks_str = [t[1] for t in toks]
    __UpperCAmelCase						    =       [t[0] for t in toks]

    # Ensure consistency
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
    if " " not in output_txt and len(__lowerCAmelCase   ) > 1:
      __UpperCAmelCase						    =       (
          tokenizer.decode([toks_ids[0]]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
          + " "
          + tokenizer.decode(toks_ids[1:]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
      )
    if with_prefix_space:
      __UpperCAmelCase						    =       " " + output_txt
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    return output_txt, output_ids






  def 	_UpperCAmelCase				(				self:			str				,				**__lowerCAmelCase:			List[Any]   ) -> str:




    '''simple docstring'''




    kwargs.update(self.special_tokens_map   )
    return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname				,				**__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    # check adding a single token
    tokenizer.add_tokens("xxx"   )
    __UpperCAmelCase						    =       tokenizer("m xxx ɪ"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[13, 392, 17]   )  # xxx should be last token

    tokenizer.add_tokens(["aaa", "bbb", "ccc"]   )
    __UpperCAmelCase						    =       tokenizer("m aaa ɪ ccc"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[13, 393, 17, 395]   )  # aaa and ccc should be after xxx and 2 after aaa

    __UpperCAmelCase						    =       tokenizer("maɪ c"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[3, 200]   )  # mai should be <unk> (=3)






  def 	_UpperCAmelCase				(				self:			int   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ h aʊ ɑːɹ j uː"   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(tokenizer(__lowerCAmelCase   ).input_ids				,				tokenizer(__lowerCAmelCase				,				do_phonemize=__lowerCAmelCase   ).input_ids   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids   )

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
        [24, 22, 5, 24, 22, 5, 77],
    ]
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]   )






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ | h aʊ | ɑːɹ | j uː |"   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(tokenizer(__lowerCAmelCase   ).input_ids				,				tokenizer(__lowerCAmelCase				,				do_phonemize=__lowerCAmelCase   ).input_ids   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
        [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
    ]
    # fmt: on

    # decode with word_del_token filter
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]   )

    # decode with no word_del_token filter
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]				,				filter_word_delimiter_token=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				filter_word_delimiter_token=__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids				,				filter_word_delimiter_token=__lowerCAmelCase   )

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids				,				filter_word_delimiter_token=__lowerCAmelCase   )

    self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"   )]   ).strip()				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token=__lowerCAmelCase   )
    __UpperCAmelCase						    =       "Hello how are you"

    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				phonemizer_lang="en-us"   ).input_ids
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				phonemizer_lang="fr-fr"   ).input_ids

    self.assertNotEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )

    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ h aʊ ɑːɹ j uː"   )
    self.assertEqual(__lowerCAmelCase				,				"ɛ l o h aʊ a ʁ j u"   )






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    __UpperCAmelCase						    =       "Hello how Are you"
    __UpperCAmelCase						    =       "hello how are you"

    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase   ).input_ids
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase   ).input_ids

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    tokenizer.add_tokens(["!", "?"]   )
    tokenizer.add_special_tokens({"cls_token": "$$$"}   )

    # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
        [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
    ]
    # fmt: on

    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]   )






  @staticmethod
  def 	_UpperCAmelCase				(				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			List[str]   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       [d[key] for d in offsets]
    return retrieved_list






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer(word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    # fmt: off
    # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
    __UpperCAmelCase						    =       [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
    # fmt: on

    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase				,				filter_word_delimiter_token=__lowerCAmelCase   )
    # check Wav2Vec2CTCTokenizerOutput keys for char
    self.assertEqual(len(outputs.keys()   )				,				2   )
    self.assertTrue("text" in outputs   )
    self.assertTrue("char_offsets" in outputs   )
    self.assertTrue(isinstance(__lowerCAmelCase				,				__lowerCAmelCase   )   )

    # check that order of chars is correct and identical for both outputs
    self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"]				,				"char"   )   )				,				outputs.text   )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"char"   )				,				["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]   )

    # check that offsets are actually correct for char
    # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
    # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"start_offset"   )				,				[0, 1, 4, 7, 9, 11, 12, 15, 16]   )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"end_offset"   )				,				[1, 4, 6, 9, 10, 12, 15, 16, 17]   )






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer(word_delimiter_token="|"   )

    def check_list_tuples_equal(__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Optional[int]   ):
      self.assertTrue(isinstance(__lowerCAmelCase				,				__lowerCAmelCase   )   )
      self.assertTrue(isinstance(outputs_list[0]				,				__lowerCAmelCase   )   )

      # transform list to ModelOutput
      __UpperCAmelCase						    =       WavaVecaPhonemeCTCTokenizerOutput(
          {k: [d[k] for d in outputs_list] for k in outputs_list[0]}   )

      self.assertListEqual(outputs_batch["text"]				,				outputs_batch_a["text"]   )

      def recursive_check(__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			List[Any]   ):
        if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
          [recursive_check(__lowerCAmelCase				,				__lowerCAmelCase   ) for la, la in zip(__lowerCAmelCase				,				__lowerCAmelCase   )]
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

      if "char_offsets" in outputs_batch:
        recursive_check(outputs_batch["char_offsets"]				,				outputs_batch_a["char_offsets"]   )

        # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
        [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
    ]
    # fmt: on

    # We assume that `decode` works as expected. All we will check now is
    # the output type is correct and the output is identical to `decode`

    # char
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase   )
    __UpperCAmelCase						    =       [tokenizer.decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase   ) for ids in sample_ids]
    check_list_tuples_equal(__lowerCAmelCase				,				__lowerCAmelCase   )






  @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"   )
  def 	_UpperCAmelCase				(				self:			Dict   ) -> Optional[int]:




    '''simple docstring'''




    pass






  @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"   )
  def 	_UpperCAmelCase				(				self:			Dict   ) -> int:




    '''simple docstring'''




    pass






  @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"   )
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Any:




    '''simple docstring'''




    pass






  @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"   )
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[Any]:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			str   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizers(do_lower_case=__lowerCAmelCase   )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )

        # We usually have added tokens from the start in tests because our vocab fixtures are
        # smaller than the original vocabs - let's not assert this
        # self.assertEqual(vocab_size, all_size)

        __UpperCAmelCase						    =       ["aaaaa bbbbbb", "cccccccccdddddddd"]
        __UpperCAmelCase						    =       tokenizer.add_tokens(__lowerCAmelCase   )
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
        self.assertEqual(__lowerCAmelCase				,				len(__lowerCAmelCase   )   )
        self.assertEqual(__lowerCAmelCase				,				all_size + len(__lowerCAmelCase   )   )

        __UpperCAmelCase						    =       tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"				,				add_special_tokens=__lowerCAmelCase   )

        self.assertGreaterEqual(len(__lowerCAmelCase   )				,				4   )
        self.assertGreater(tokens[0]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[-3]				,				tokenizer.vocab_size - 1   )

        __UpperCAmelCase						    =       {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
        __UpperCAmelCase						    =       tokenizer.add_special_tokens(__lowerCAmelCase   )
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
        self.assertEqual(__lowerCAmelCase				,				len(__lowerCAmelCase   )   )
        self.assertEqual(__lowerCAmelCase				,				all_size_a + len(__lowerCAmelCase   )   )

        __UpperCAmelCase						    =       tokenizer.encode(
            ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"				,				add_special_tokens=__lowerCAmelCase   )

        self.assertGreaterEqual(len(__lowerCAmelCase   )				,				6   )
        self.assertGreater(tokens[0]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[0]				,				tokens[1]   )
        self.assertGreater(tokens[-3]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[-3]				,				tokens[-4]   )
        self.assertEqual(tokens[0]				,				tokenizer.eos_token_id   )
        self.assertEqual(tokens[-3]				,				tokenizer.pad_token_id   )






  @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."   )
  def 	_UpperCAmelCase				(				self:			Any   ) -> List[Any]:




    '''simple docstring'''




    pass






  @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."   )
  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizers(fast=__lowerCAmelCase				,				do_lower_case=__lowerCAmelCase   )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
        __UpperCAmelCase						    =       ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
        __UpperCAmelCase						    =       tokenizer.convert_tokens_to_string(__lowerCAmelCase   )

        self.assertIsInstance(output["text"]				,				__lowerCAmelCase   )



