



"""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()
A:     Any										=	logging.get_logger()



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     nn.Module
   __lowerCAmelCase						:     List[nn.Module]		=		field(default_factory=UpperCAmelCase__				)
   __lowerCAmelCase						:     list		=		field(default_factory=UpperCAmelCase__				)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			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   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    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 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




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



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     nn.Module
   __lowerCAmelCase						:     nn.Module
   __lowerCAmelCase						:     int		=		1
   __lowerCAmelCase						:     List		=		field(default_factory=UpperCAmelCase__				)
   __lowerCAmelCase						:     List		=		field(default_factory=UpperCAmelCase__				)
   __lowerCAmelCase						:     bool		=		True

   def __call__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			Tracker(self.dest		)(_SCREAMING_SNAKE_CASE		).parametrized
    UpperCAmelCase			:      Tuple			=			Tracker(self.src		)(_SCREAMING_SNAKE_CASE		).parametrized

    UpperCAmelCase			:      Any			=			list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.src_skip   ,  _SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Tuple			=			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    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    super().__init__()

    UpperCAmelCase			:      List[Tuple[str, nn.Module]]			=			[]
    # - 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			:      Optional[int]			=			len(_SCREAMING_SNAKE_CASE		) + 1
     feature_blocks.append((F"res{block_index}", v)		)

    UpperCAmelCase			:      Any			=			nn.ModuleDict(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




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



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




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




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





    '''simple docstring'''




    if x not in self:
     UpperCAmelCase			:      Tuple			=			self.convert_name_to_timm(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Optional[int]			=			partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE   ,  pretrained=_SCREAMING_SNAKE_CASE		).eval(), None)		)

    else:
     UpperCAmelCase			:      Optional[int]			=			super().__getitem__(_SCREAMING_SNAKE_CASE		)

    return val



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

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





    '''simple docstring'''




    if "seer" in x and "in1k" not in x:
     UpperCAmelCase			:      Dict			=			RegNetModel
    else:
     UpperCAmelCase			:      Any			=			RegNetForImageClassification
    return val

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	List[Tuple[str, str]]		):
 for from_key, to_key in keys:
  UpperCAmelCase			:      str			=			from_state_dict[from_key].clone()
  print(F"Copied key={from_key} to={to_key}"		)
 return to_state_dict

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Callable[[], nn.Module]    ,	UpperCamelCase		:	Callable[[], nn.Module]    ,	UpperCamelCase		:	RegNetConfig    ,	UpperCamelCase		:	Path    ,	UpperCamelCase		:	bool = True    ,	):
 print(F"Converting {name}..."		)
 with torch.no_grad():
  UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			from_model_func()
  UpperCAmelCase			:      Dict			=			our_model_func(UpperCamelCase		).eval()
  UpperCAmelCase			:      Optional[Any]			=			ModuleTransfer(src=UpperCamelCase    ,	dest=UpperCamelCase    ,	raise_if_mismatch=UpperCamelCase		)
  UpperCAmelCase			:      Any			=			torch.randn((1, 3, 224, 224)		)
  module_transfer(UpperCamelCase		)

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

 UpperCAmelCase			:      str			=			our_model(UpperCamelCase    ,	output_hidden_states=UpperCamelCase		)
 UpperCAmelCase			:      Dict			=			(
     our_outputs.logits if isinstance(UpperCamelCase    ,	UpperCamelCase		) else our_outputs.last_hidden_state
 )

 UpperCAmelCase			:      List[str]			=			from_model(UpperCamelCase		)
 UpperCAmelCase			:      Optional[int]			=			from_output[-1] if type(UpperCamelCase		) 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			:      List[str]			=			our_outputs.hidden_states[-1]

 assert torch.allclose(UpperCamelCase    ,	UpperCamelCase		), "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=UpperCamelCase    ,	)

  UpperCAmelCase			:      Optional[int]			=			224 if """seer""" not in name else 384
  # we can use the convnext one
  UpperCAmelCase			:      Optional[Any]			=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    ,	size=UpperCamelCase		)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / name    ,	commit_message="""Add image processor"""    ,	use_temp_dir=UpperCamelCase    ,	)

  print(F"Pushed {name}"		)

def    _snake_case		(		UpperCamelCase		:	Path    ,	UpperCamelCase		:	str = None    ,	UpperCamelCase		:	bool = True		):
 UpperCAmelCase			:      Union[str, Any]			=			"""imagenet-1k-id2label.json"""
 UpperCAmelCase			:      List[str]			=			1000
 UpperCAmelCase			:      str			=			(1, num_labels)

 UpperCAmelCase			:      Optional[Any]			=			"""huggingface/label-files"""
 UpperCAmelCase			:      int			=			num_labels
 UpperCAmelCase			:      List[Any]			=			json.load(open(cached_download(hf_hub_url(UpperCamelCase    ,	UpperCamelCase    ,	repo_type="""dataset"""		)		)    ,	"""r"""		)		)
 UpperCAmelCase			:      List[str]			=			{int(UpperCamelCase		): v for k, v in idalabel.items()}

 UpperCAmelCase			:      int			=			idalabel
 UpperCAmelCase			:      Optional[int]			=			{v: k for k, v in idalabel.items()}

 UpperCAmelCase			:      Optional[int]			=			partial(UpperCamelCase    ,	num_labels=UpperCamelCase    ,	idalabel=UpperCamelCase    ,	labelaid=UpperCamelCase		)

 UpperCAmelCase			:      List[str]			=			{
     """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		),
 }

 UpperCAmelCase			:      int			=			NameToOurModelFuncMap()
 UpperCAmelCase			:      Optional[int]			=			NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 UpperCAmelCase			:      List[Any]			=			partial(
     UpperCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)    ,	)

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

 UpperCAmelCase			:      Union[str, Any]			=			partial(
     UpperCamelCase    ,	"""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			:      Tuple			=			partial(
     UpperCamelCase    ,	"""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
 UpperCAmelCase			:      Tuple			=			partial(
     UpperCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)    ,	)

 UpperCAmelCase			:      List[Any]			=			partial(
     UpperCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)    ,	)

 UpperCAmelCase			:      List[str]			=			partial(
     UpperCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)    ,	)

 UpperCAmelCase			:      Dict			=			partial(
     UpperCamelCase    ,	"""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(
      UpperCamelCase    ,	names_to_from_model_map[model_name]    ,	names_to_ours_model_map[model_name]    ,	names_to_config[model_name]    ,	UpperCamelCase    ,	UpperCamelCase    ,	)
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       UpperCamelCase    ,	names_to_from_model_map[model_name]    ,	names_to_ours_model_map[model_name]    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	)
 return config, expected_shape


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

      A:     List[Any]										=	parser.parse_args()

      A:     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.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     List[Any]		=		TransfoXLTokenizer
   __lowerCAmelCase						:     Optional[int]		=		False
   __lowerCAmelCase						:     Optional[Any]		=		False

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





    '''simple docstring'''




    super().setUp()

    UpperCAmelCase			:      Optional[Any]			=			[
        """<unk>""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """unwanted""",
        """wa""",
        """un""",
        """running""",
        """,""",
        """low""",
        """l""",
    ]
    UpperCAmelCase			:      int			=			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]		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			True
    return TransfoXLTokenizer.from_pretrained(self.tmpdirname   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			"""<unk> UNwanted , running"""
    UpperCAmelCase			:      Dict			=			"""<unk> unwanted, running"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			TransfoXLTokenizer(vocab_file=self.vocab_file   ,  lower_case=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			tokenizer.tokenize("""<unk> UNwanted , running"""		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  ["""<unk>""", """unwanted""", """,""", """running"""]		)

    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  [0, 4, 8, 7]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
    UpperCAmelCase			:      Optional[Any]			=			[
        """Hello""",
        """(""",
        """bracket""",
        """)""",
        """and""",
        """side""",
        """@-@""",
        """scrolled""",
        """[""",
        """and""",
        """]""",
        """Henry""",
        """'s""",
        """$""",
        """5""",
        """@,@""",
        """000""",
        """with""",
        """3""",
        """@.@""",
        """34""",
        """m""",
        """.""",
        """What""",
        """'s""",
        """up""",
        """!""",
        """?""",
    ]

    self.assertListEqual(tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)

    self.assertEqual(tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.get_tokenizer()
    UpperCAmelCase			:      Tuple			=			len(_SCREAMING_SNAKE_CASE		)

    tokenizer.add_tokens(["""new1""", """new2"""]		)
    tokenizer.move_added_token("""new1"""   ,  1		)

    # Check that moved token is not copied (duplicate)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  original_len + 2		)
    # Check that token is moved to specified id
    self.assertEqual(tokenizer.encode("""new1"""		)   ,  [1]		)
    self.assertEqual(tokenizer.decode([1]		)   ,  """new1"""		)
