





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A       :   Union[str, Any]					= logging.get_logger(__name__)







def   __lowerCamelCase   (				__a  :Optional[int]       ,	__a  :str=False       )						->							List[Any]:






		"""simple docstring"""

		A__     =  []
		for i in range(config.num_hidden_layers       ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight')       )
				rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')       )
				rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')       )
				rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')       )
				rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')       )
				rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')       )
				rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')       )
				rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')       )
				rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')       )
				rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')       )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ("""cls_token""", """deit.embeddings.cls_token"""),
		        ("""dist_token""", """deit.embeddings.distillation_token"""),
		        ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
		        ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
		        ("""pos_embed""", """deit.embeddings.position_embeddings"""),
		    ]       )

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ("""norm.weight""", """layernorm.weight"""),
				        ("""norm.bias""", """layernorm.bias"""),
				        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
				        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
				    ]       )

				# if just the base model, we should remove "deit" from all keys that start with "deit"
				A__     =  [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""       ) else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ("""norm.weight""", """deit.layernorm.weight"""),
				        ("""norm.bias""", """deit.layernorm.bias"""),
				        ("""head.weight""", """cls_classifier.weight"""),
				        ("""head.bias""", """cls_classifier.bias"""),
				        ("""head_dist.weight""", """distillation_classifier.weight"""),
				        ("""head_dist.bias""", """distillation_classifier.bias"""),
				    ]       )

		return rename_keys







def   __lowerCamelCase   (				__a  :Tuple       ,	__a  :Union[str, Any]       ,	__a  :List[str]=False       )						->							Tuple:






		"""simple docstring"""

		for i in range(config.num_hidden_layers       ):
				if base_model:
						A__     =  """"""
				else:
						A__     =  """deit."""
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				A__     =  state_dict.pop(F'blocks.{i}.attn.qkv.weight'       )
				A__     =  state_dict.pop(F'blocks.{i}.attn.qkv.bias'       )
				# next, add query, keys and values (in that order) to the state dict
				A__     =  in_proj_weight[
				    : config.hidden_size, :
				]
				A__     =  in_proj_bias[: config.hidden_size]
				A__     =  in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				A__     =  in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				A__     =  in_proj_weight[
				    -config.hidden_size :, :
				]
				A__     =  in_proj_bias[-config.hidden_size :]







def   __lowerCamelCase   (				__a  :Optional[Any]       ,	__a  :List[Any]       ,	__a  :Union[str, Any]       )						->							List[str]:






		"""simple docstring"""

		A__     =  dct.pop(__a       )
		A__     =  val







def   __lowerCamelCase   (				)						->							Any:






		"""simple docstring"""

		A__     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       )
		return im







@torch.no_grad()
def   __lowerCamelCase   (				__a  :int       ,	__a  :Union[str, Any]       )						->							int:






		"""simple docstring"""

		A__     =  DeiTConfig()
		# all deit models have fine-tuned heads
		A__     =  False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		A__     =  1_0_0_0
		A__     =  """huggingface/label-files"""
		A__     =  """imagenet-1k-id2label.json"""
		A__     =  json.load(open(hf_hub_download(__a       ,	__a       ,	repo_type="""dataset"""       )       ,	"""r"""       )       )
		A__     =  {int(__a       ): v for k, v in idalabel.items()}
		A__     =  idalabel
		A__     =  {v: k for k, v in idalabel.items()}
		A__     =  int(deit_name[-6:-4]       )
		A__     =  int(deit_name[-3:]       )
		# size of the architecture
		if deit_name[9:].startswith("""tiny"""       ):
				A__     =  1_9_2
				A__     =  7_6_8
				A__     =  1_2
				A__     =  3
		elif deit_name[9:].startswith("""small"""       ):
				A__     =  3_8_4
				A__     =  1_5_3_6
				A__     =  1_2
				A__     =  6
		if deit_name[9:].startswith("""base"""       ):
				pass
		elif deit_name[4:].startswith("""large"""       ):
				A__     =  1_0_2_4
				A__     =  4_0_9_6
				A__     =  2_4
				A__     =  1_6

		# load original model from timm
		A__     =  timm.create_model(__a       ,	pretrained=__a       )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		A__     =  timm_model.state_dict()
		A__     =  create_rename_keys(__a       ,	__a       )
		for src, dest in rename_keys:
				rename_key(__a       ,	__a       ,	__a       )
		read_in_q_k_v(__a       ,	__a       ,	__a       )

		# load HuggingFace model
		A__     =  DeiTForImageClassificationWithTeacher(__a       ).eval()
		model.load_state_dict(__a       )

		# Check outputs on an image, prepared by DeiTImageProcessor
		A__     =  int(
		    (2_5_6 / 2_2_4) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		A__     =  DeiTImageProcessor(size=__a       ,	crop_size=config.image_size       )
		A__     =  image_processor(images=prepare_img()       ,	return_tensors="""pt"""       )
		A__     =  encoding["""pixel_values"""]
		A__     =  model(__a       )

		A__     =  timm_model(__a       )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a       ,	outputs.logits       ,	atol=1E-3       )

		Path(__a       ).mkdir(exist_ok=__a       )
		print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'       )
		model.save_pretrained(__a       )
		print(F'Saving image processor to {pytorch_dump_folder_path}'       )
		image_processor.save_pretrained(__a       )


if __name__ == "__main__":
					A       :   Optional[int]					= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--deit_name''',
					    default='''vit_deit_base_distilled_patch16_224''',
					    type=str,
					    help='''Name of the DeiT timm model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)

					A       :   Optional[Any]					= parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)










import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					A       :   Optional[Any]					= '''pt'''
elif is_tf_available():
					A       :   List[Any]					= '''tf'''
else:
					A       :   Union[str, Any]					= '''jax'''


class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       ByTaTokenizer
			__lowerCamelCase      :			Tuple							     =       False






			def   a_			(     self  :       Dict  )      ->      Dict:







					"""simple docstring"""






					super().setUp()
					A__     =  ByTaTokenizer()
					tokenizer.save_pretrained(self.tmpdirname  )






			@cached_property
			def   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					return ByTaTokenizer.from_pretrained("""google/byt5-small"""  )






			def   a_			(     self  :       Union[str, Any]				,      **__lowerCAmelCase  :       int  )      ->      ByTaTokenizer:







					"""simple docstring"""






					return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






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







					"""simple docstring"""






					A__     =  []
					for i in range(len(__lowerCAmelCase  )  ):
							try:
									A__     =  tokenizer.decode([i]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							except UnicodeDecodeError:
									pass
							toks.append((i, tok)  )

					A__     =  list(filter(lambda __lowerCAmelCase  : re.match(R"""^[ a-zA-Z]+$"""				,      t[1]  )				,      __lowerCAmelCase  )  )
					A__     =  list(filter(lambda __lowerCAmelCase  : [t[0]] == tokenizer.encode(t[1]				,      add_special_tokens=__lowerCAmelCase  )				,      __lowerCAmelCase  )  )
					if max_length is not None and len(__lowerCAmelCase  ) > max_length:
							A__     =  toks[:max_length]
					if min_length is not None and len(__lowerCAmelCase  ) < min_length and len(__lowerCAmelCase  ) > 0:
							while len(__lowerCAmelCase  ) < min_length:
									A__     =  toks + toks
        # toks_str = [t[1] for t in toks]
					A__     =  [t[0] for t in toks]

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






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]  )
					A__     =  tokenizer(["""hi""", """I went to the gym""", """"""]  )
					self.assertListEqual(batch_with_eos_added["""input_ids"""]				,      batch_without_eos_added["""input_ids"""]  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  """Unicode €."""
					A__     =  tokenizer(__lowerCAmelCase  )
					A__     =  [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )

					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """Unicode €.</s>"""  )

					A__     =  tokenizer("""e è é ê ë"""  )
					A__     =  [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )
					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """e è é ê ë</s>"""  )

					# encode/decode, but with `encode` instead of `__call__`
					self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""  )  )				,      """e è é ê ë</s>"""  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
					# fmt: on
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )

					if FRAMEWORK != "jax":
							A__     =  list(batch.input_ids.numpy()[0]  )
					else:
							A__     =  list(batch.input_ids.tolist()[0]  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

					self.assertEqual((2, 37)				,      batch.input_ids.shape  )
					self.assertEqual((2, 37)				,      batch.attention_mask.shape  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					# check if input_ids are returned and no decoder_input_ids
					self.assertIn("""input_ids"""				,      __lowerCAmelCase  )
					self.assertIn("""attention_mask"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_input_ids"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_attention_mask"""				,      __lowerCAmelCase  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					A__     =  tokenizer(
					    text_target=__lowerCAmelCase				,      max_length=32				,      padding="""max_length"""				,      truncation=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertEqual(32				,      targets["""input_ids"""].shape[1]  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization. </s>"""]
					A__     =  ["""Summary of the text. </s>"""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
					A__     =  [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
					# fmt: on

					A__     =  tokenizer(__lowerCAmelCase				,      text_target=__lowerCAmelCase  )

					self.assertEqual(__lowerCAmelCase				,      batch["""input_ids"""][0]  )
					self.assertEqual(__lowerCAmelCase				,      batch["""labels"""][0]  )






			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									self.assertNotEqual(tokenizer.model_max_length				,      42  )

        # Now let's start the test
					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

									shutil.rmtree(__lowerCAmelCase  )

					A__     =  self.get_tokenizers(model_max_length=42  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									tokenizer.add_tokens(["""bim""", """bambam"""]  )
									A__     =  tokenizer.additional_special_tokens
									additional_special_tokens.append("""new_additional_special_token"""  )
									tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )
									self.assertIn("""new_additional_special_token"""				,      after_tokenizer.additional_special_tokens  )
									self.assertEqual(after_tokenizer.model_max_length				,      42  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase				,      model_max_length=43  )
									self.assertEqual(tokenizer.model_max_length				,      43  )

									shutil.rmtree(__lowerCAmelCase  )






			def   a_			(     self  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									A__     =  [f'<extra_id_{i}>' for i in range(1_25  )]

									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]
									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )
									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )

									# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
									# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
									# "special_tokens_map.json" files
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      )
									self.assertIn(
									    """an_additional_special_token"""				,      tokenizer_without_change_in_init.additional_special_tokens  )
									# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
									self.assertEqual(
									    ["""an_additional_special_token"""]				,      tokenizer_without_change_in_init.convert_ids_to_tokens(
									        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]  )  )				,      )

									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
									A__     =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,      lstrip=__lowerCAmelCase  )]
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      additional_special_tokens=__lowerCAmelCase				,      )

									self.assertIn("""a_new_additional_special_token"""				,      tokenizer.additional_special_tokens  )
									self.assertEqual(
									    ["""a_new_additional_special_token"""]				,      tokenizer.convert_ids_to_tokens(
									        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]  )  )				,      )






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







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer_class.from_pretrained(__lowerCAmelCase  )

									self.assertTrue(tokenizer.decode([2_55]  ) == """"""  )






			def   a_			(     self  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					pass






			def   a_			(     self  :       Optional[int]  )      ->      Any:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       str  )      ->      Optional[int]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers(fast=__lowerCAmelCase				,      do_lower_case=__lowerCAmelCase  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
									A__     =  tokenizer.convert_tokens_to_string(__lowerCAmelCase  )

									self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[Any]  )      ->      int:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  [
									    """bos_token""",
									    """eos_token""",
									    """unk_token""",
									    """sep_token""",
									    """pad_token""",
									    """cls_token""",
									    """mask_token""",
									]

									A__     =  0
									A__     =  tokenizer.convert_ids_to_tokens(
									    __lowerCAmelCase				,      skip_special_tokens=__lowerCAmelCase  )

									for attr in attributes_list:
											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      []  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      [token_id_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      [token_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      [token_id_to_test_setters]  )



