



'''simple docstring'''

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def 						__UpperCamelCase				(							_lowercase=None,				_lowercase=None					) ->			str:
	return field(default_factory=lambda: default,				metadata=a__					)




@dataclass
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						field(
					    metadata={"""help""": """The csv file to plot."""}	,   )
					A_						    =						field(
					    default=__lowercase	,   metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}	,   )
					A_						    =						field(
					    default=__lowercase	,   metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}	,   )
					A_						    =						field(
					    default=__lowercase	,   metadata={"""help""": """Disable logarithmic scale when plotting"""}	,   )
					A_						    =						field(
					    default=__lowercase	,   metadata={
					        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
					    }	,   )
					A_						    =						field(
					    default=__lowercase	,   metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}	,   )
					A_						    =						list_field(
					    default=__lowercase	,   metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}     )







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:






	try:
		int(a__					)
		return True
	except ValueError:
		return False







def 						__UpperCamelCase				(							_lowercase					) ->			Any:






	try:
		float(a__					)
		return True
	except ValueError:
		return False




class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	str     ,  UpperCamelCase_				:	Any					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		args
						_lowercase					:       Tuple       =		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

						with open(self.args.csv_file     ,  newline=''					) as csv_file:
							_lowercase					:       int       =		csv.DictReader(__A					)
							for row in reader:
								_lowercase					:       Tuple       =		row['model']
								self.result_dict[model_name]["bsz"].append(int(row['batch_size']					)					)
								self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']					)					)
								if can_convert_to_int(row['result']					):
									# value is not None
									_lowercase					:       Tuple       =		int(row['result']					)
								elif can_convert_to_float(row['result']					):
									# value is not None
									_lowercase					:       Optional[Any]       =		float(row['result']					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Dict:



						'''simple docstring'''





						_lowercase  ,    _lowercase					:       int       =		plt.subplots()
						_lowercase					:       Optional[Any]       =		'Time usage' if self.args.is_time else 'Memory usage'
						_lowercase					:       Any       =		title_str + ' for training' if self.args.is_train else title_str + ' for inference'

						if not self.args.no_log_scale:
							# set logarithm scales
							ax.set_xscale('log'					)
							ax.set_yscale('log'					)

						for axis in [ax.xaxis, ax.yaxis]:
							axis.set_major_formatter(ScalarFormatter()					)

						for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
							_lowercase					:       str       =		sorted(set(self.result_dict[model_name]['bsz']					)					)
							_lowercase					:       Optional[Any]       =		sorted(set(self.result_dict[model_name]['seq_len']					)					)
							_lowercase					:       Tuple       =		self.result_dict[model_name]['result']

							((_lowercase)  ,    (_lowercase))					:       Optional[int]       =		(
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							_lowercase					:       Dict       =		(
							    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
							)

							for inner_loop_value in inner_loop_array:
								if self.args.plot_along_batch:
									_lowercase					:       Any       =		np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,  dtype=__A     ,  )
								else:
									_lowercase					:       Tuple       =		np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,  dtype=np.floataa     ,  )

								((_lowercase)  ,    (_lowercase))					:       Optional[int]       =		(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								_lowercase					:       Optional[int]       =		np.asarray(__A     ,  __A					)[: len(__A					)]
								plt.scatter(
								    __A     ,  __A     ,  label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
								plt.plot(__A     ,  __A     ,  '--'					)

							title_str += F''' {label_model_name} vs.'''

						_lowercase					:       str       =		title_str[:-4]
						_lowercase					:       Dict       =		'Time in s' if self.args.is_time else 'Memory in MB'

						# plot
						plt.title(__A					)
						plt.xlabel(__A					)
						plt.ylabel(__A					)
						plt.legend()

						if self.args.figure_png_file is not None:
							plt.savefig(self.args.figure_png_file					)
						else:
							plt.show()







def 						__UpperCamelCase				(							) ->			Any:
	_lowercase					:       Dict       =		HfArgumentParser(a__					)
	_lowercase					:       Any       =		parser.parse_args_into_dataclasses()[0]
	_lowercase					:       str       =		Plot(args=a__					)
	plot.plot()


if __name__ == "__main__":
		main()





'''simple docstring'''

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				:    List[str]     						='''pt'''
elif is_tf_available():
		_A				:    Tuple     						='''tf'''
else:
		_A				:    Optional[int]     						='''jax'''



class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						ByTaTokenizer
					A_						    =						False




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



						'''simple docstring'''





						super().setUp()
						_lowercase					:       Any       =		ByTaTokenizer()
						tokenizer.save_pretrained(self.tmpdirname					)




					@cached_property
					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int     ,  **UpperCamelCase_				:	List[Any]					)  ->       ByTaTokenizer:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Union[str, Any]=False     ,  UpperCamelCase_				:	Tuple=20     ,  UpperCamelCase_				:	Optional[int]=5					)  ->       Tuple[str, list]:



						'''simple docstring'''





						_lowercase					:       Dict       =		[]
						for i in range(len(UpperCamelCase_					)					):
							try:
								_lowercase					:       List[Any]       =		tokenizer.decode([i]     ,  clean_up_tokenization_spaces=UpperCamelCase_					)
							except UnicodeDecodeError:
								pass
							toks.append((i, tok)					)

						_lowercase					:       Optional[Any]       =		list(filter(lambda UpperCamelCase_					: re.match(r'^[ a-zA-Z]+$'     ,  t[1]					)     ,  UpperCamelCase_					)					)
						_lowercase					:       List[Any]       =		list(filter(lambda UpperCamelCase_					: [t[0]] == tokenizer.encode(t[1]     ,  add_special_tokens=UpperCamelCase_					)     ,  UpperCamelCase_					)					)
						if max_length is not None and len(UpperCamelCase_					) > max_length:
							_lowercase					:       List[Any]       =		toks[:max_length]
						if min_length is not None and len(UpperCamelCase_					) < min_length and len(UpperCamelCase_					) > 0:
							while len(UpperCamelCase_					) < min_length:
								_lowercase					:       Tuple       =		toks + toks
        # toks_str = [t[1] for t in toks]
						_lowercase					:       Dict       =		[t[0] for t in toks]

						# Ensure consistency
						_lowercase					:       Any       =		tokenizer.decode(UpperCamelCase_     ,  clean_up_tokenization_spaces=UpperCamelCase_					)
						if " " not in output_txt and len(UpperCamelCase_					) > 1:
							_lowercase					:       Any       =		(
							    tokenizer.decode([toks_ids[0]]     ,  clean_up_tokenization_spaces=UpperCamelCase_					)
							    + ' '
							    + tokenizer.decode(toks_ids[1:]     ,  clean_up_tokenization_spaces=UpperCamelCase_					)
							)
						if with_prefix_space:
							_lowercase					:       Union[str, Any]       =		' ' + output_txt
						_lowercase					:       int       =		tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
						return output_txt, output_ids




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.ta_base_tokenizer
						_lowercase					:       Union[str, Any]       =		tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']					)
						_lowercase					:       Tuple       =		tokenizer(['hi', 'I went to the gym', '']					)
						self.assertListEqual(batch_with_eos_added['input_ids']     ,  batch_without_eos_added['input_ids']					)




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



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.ta_base_tokenizer
						_lowercase					:       Tuple       =		'Unicode €.'
						_lowercase					:       List[Any]       =		tokenizer(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
						self.assertEqual(encoded['input_ids']     ,  UpperCamelCase_					)

						# decoding
						_lowercase					:       List[str]       =		tokenizer.decode(UpperCamelCase_					)
						self.assertEqual(UpperCamelCase_     ,  'Unicode €.</s>'					)

						_lowercase					:       Any       =		tokenizer('e è é ê ë'					)
						_lowercase					:       Optional[int]       =		[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
						self.assertEqual(encoded['input_ids']     ,  UpperCamelCase_					)
						# decoding
						_lowercase					:       Tuple       =		tokenizer.decode(UpperCamelCase_					)
						self.assertEqual(UpperCamelCase_     ,  '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 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.ta_base_tokenizer
						_lowercase					:       int       =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
						# fmt: off
						_lowercase					:       Any       =		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
						# fmt: on
						_lowercase					:       Dict       =		tokenizer(UpperCamelCase_     ,  padding=UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)
						self.assertIsInstance(UpperCamelCase_     ,  UpperCamelCase_					)

						if FRAMEWORK != "jax":
							_lowercase					:       Optional[Any]       =		list(batch.input_ids.numpy()[0]					)
						else:
							_lowercase					:       List[str]       =		list(batch.input_ids.tolist()[0]					)

						self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_					)

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




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



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.ta_base_tokenizer
						_lowercase					:       List[str]       =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
						_lowercase					:       str       =		tokenizer(UpperCamelCase_     ,  padding=UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn('input_ids'     ,  UpperCamelCase_					)
						self.assertIn('attention_mask'     ,  UpperCamelCase_					)
						self.assertNotIn('decoder_input_ids'     ,  UpperCamelCase_					)
						self.assertNotIn('decoder_attention_mask'     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Any					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.ta_base_tokenizer
						_lowercase					:       Optional[Any]       =		[
						    'Summary of the text.',
						    'Another summary.',
						]
						_lowercase					:       str       =		tokenizer(
						    text_target=UpperCamelCase_     ,  max_length=32     ,  padding='max_length'     ,  truncation=UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)
						self.assertEqual(32     ,  targets['input_ids'].shape[1]					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       str       =		self.ta_base_tokenizer
						_lowercase					:       str       =		['A long paragraph for summarization. </s>']
						_lowercase					:       Optional[int]       =		['Summary of the text. </s>']
						# fmt: off
						_lowercase					:       Optional[Any]       =		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
						_lowercase					:       Optional[Any]       =		[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
						# fmt: on

						_lowercase					:       Any       =		tokenizer(UpperCamelCase_     ,  text_target=UpperCamelCase_					)

						self.assertEqual(UpperCamelCase_     ,  batch['input_ids'][0]					)
						self.assertEqual(UpperCamelCase_     ,  batch['labels'][0]					)




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



						'''simple docstring'''





						_lowercase					:       Dict       =		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
						_lowercase					:       List[Any]       =		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
								_lowercase					:       List[Any]       =		tempfile.mkdtemp()

								_lowercase					:       Any       =		' He is very happy, UNwant\u00E9d,running'
								_lowercase					:       Union[str, Any]       =		tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								tokenizer.save_pretrained(UpperCamelCase_					)

								_lowercase					:       Optional[int]       =		tokenizer.__class__.from_pretrained(UpperCamelCase_					)
								_lowercase					:       Tuple       =		after_tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_					)

								shutil.rmtree(UpperCamelCase_					)

						_lowercase					:       str       =		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
								_lowercase					:       Dict       =		tempfile.mkdtemp()

								_lowercase					:       List[Any]       =		' He is very happy, UNwant\u00E9d,running'
								tokenizer.add_tokens(['bim', 'bambam']					)
								_lowercase					:       Optional[int]       =		tokenizer.additional_special_tokens
								additional_special_tokens.append('new_additional_special_token'					)
								tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}					)
								_lowercase					:       Optional[Any]       =		tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								tokenizer.save_pretrained(UpperCamelCase_					)

								_lowercase					:       List[str]       =		tokenizer.__class__.from_pretrained(UpperCamelCase_					)
								_lowercase					:       Dict       =		after_tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_					)
								self.assertIn('new_additional_special_token'     ,  after_tokenizer.additional_special_tokens					)
								self.assertEqual(after_tokenizer.model_max_length     ,  42					)

								_lowercase					:       Dict       =		tokenizer.__class__.from_pretrained(UpperCamelCase_     ,  model_max_length=43					)
								self.assertEqual(tokenizer.model_max_length     ,  43					)

								shutil.rmtree(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		[]
						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(UpperCamelCase_					)

								with open(os.path.join(UpperCamelCase_     ,  'special_tokens_map.json'					)     ,  encoding='utf-8'					) as json_file:
									_lowercase					:       int       =		json.load(UpperCamelCase_					)

								with open(os.path.join(UpperCamelCase_     ,  'tokenizer_config.json'					)     ,  encoding='utf-8'					) as json_file:
									_lowercase					:       Tuple       =		json.load(UpperCamelCase_					)

								_lowercase					:       List[Any]       =		[F'''<extra_id_{i}>''' for i in range(125					)]

								_lowercase					:       Any       =		added_tokens_extra_ids + [
								    'an_additional_special_token'
								]
								_lowercase					:       int       =		added_tokens_extra_ids + [
								    'an_additional_special_token'
								]

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

								# 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
								_lowercase					:       Optional[Any]       =		tokenizer_class.from_pretrained(
								    UpperCamelCase_     ,  )
								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
								_lowercase					:       List[str]       =		added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,  lstrip=UpperCamelCase_					)]
								_lowercase					:       Tuple       =		tokenizer_class.from_pretrained(
								    UpperCamelCase_     ,  additional_special_tokens=UpperCamelCase_     ,  )

								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 						__UpperCAmelCase     (  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		[]
						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(UpperCamelCase_					)

								_lowercase					:       str       =		tokenizer_class.from_pretrained(UpperCamelCase_					)

								self.assertTrue(tokenizer.decode([255]					) == ''					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Tuple:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       List[Any]:



						'''simple docstring'''





						pass




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



						'''simple docstring'''





						pass




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



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.get_tokenizers(fast=UpperCamelCase_     ,  do_lower_case=UpperCamelCase_					)
						for tokenizer in tokenizers:
							with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
								_lowercase					:       Any       =		['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
								_lowercase					:       Tuple       =		tokenizer.convert_tokens_to_string(UpperCamelCase_					)

								self.assertIsInstance(UpperCamelCase_     ,  UpperCamelCase_					)




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



						'''simple docstring'''





						_lowercase					:       Dict       =		self.get_tokenizers()
						for tokenizer in tokenizers:
							with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
								_lowercase					:       Optional[int]       =		[
								    'bos_token',
								    'eos_token',
								    'unk_token',
								    'sep_token',
								    'pad_token',
								    'cls_token',
								    'mask_token',
								]

								_lowercase					:       Optional[int]       =		0
								_lowercase					:       int       =		tokenizer.convert_ids_to_tokens(
								    UpperCamelCase_     ,  skip_special_tokens=UpperCamelCase_					)

								for attr in attributes_list:
									setattr(UpperCamelCase_     ,  attr + '_id'     ,  UpperCamelCase_					)
									self.assertEqual(getattr(UpperCamelCase_     ,  UpperCamelCase_					)     ,  UpperCamelCase_					)
									self.assertEqual(getattr(UpperCamelCase_     ,  attr + '_id'					)     ,  UpperCamelCase_					)

									setattr(UpperCamelCase_     ,  attr + '_id'     ,  UpperCamelCase_					)
									self.assertEqual(getattr(UpperCamelCase_     ,  UpperCamelCase_					)     ,  UpperCamelCase_					)
									self.assertEqual(getattr(UpperCamelCase_     ,  attr + '_id'					)     ,  UpperCamelCase_					)

								setattr(UpperCamelCase_     ,  'additional_special_tokens_ids'     ,  []					)
								self.assertListEqual(getattr(UpperCamelCase_     ,  'additional_special_tokens'					)     ,  []					)
								self.assertListEqual(getattr(UpperCamelCase_     ,  'additional_special_tokens_ids'					)     ,  []					)

								setattr(UpperCamelCase_     ,  'additional_special_tokens_ids'     ,  [token_id_to_test_setters]					)
								self.assertListEqual(getattr(UpperCamelCase_     ,  'additional_special_tokens'					)     ,  [token_to_test_setters]					)
								self.assertListEqual(getattr(UpperCamelCase_     ,  'additional_special_tokens_ids'					)     ,  [token_id_to_test_setters]					)
