




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCamelCase   :						int				=			logging.getLogger(__name__)







def 		_A   (		):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							argparse.ArgumentParser(
					    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''				)
					parser.add_argument(
					    '''--dataset_name'''							,type=__A							,default='''wikitext'''							,help='''Name of the training. Explore datasets at: hf.co/datasets.'''							,)
					parser.add_argument(
					    '''--dataset_config'''							,type=__A							,default='''wikitext-103-raw-v1'''							,help='''Configuration name of the dataset.'''				)
					parser.add_argument(
					    '''--tokenizer_name_or_path'''							,type=__A							,default='''sayakpaul/unigram-tokenizer-wikitext'''							,help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''							,)
					parser.add_argument(
					    '''--shard_size'''							,type=__A							,default=1_0_0_0							,help='''Number of entries to go in a single shard.'''							,)
					parser.add_argument('''--split'''							,type=__A							,default='''train'''							,choices=['''train''', '''test''', '''validation''']				)
					parser.add_argument(
					    '''--limit'''							,default=__A							,type=__A							,help='''Limit the number of shards (used for debugging).'''							,)
					parser.add_argument(
					    '''--max_length'''							,type=__A							,default=5_1_2							,help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
					    ''' sequence length that is a multiple of 8.'''							,)
					parser.add_argument(
					    '''--output_dir'''							,default='''tf-tpu'''							,type=__A							,help='''Output directory where the TFRecord shards will be saved. If the'''
					    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
					    ''' shards will be directly saved to a Google Cloud Storage bucket.'''							,)

					__magic_name__							:	Union[str, Any]    							=							parser.parse_args()
					return args







def 		_A   (		__A:	int				):





					'''simple docstring'''






					def fn(__A:	Optional[int]				):
										return tokenizer(examples['''text''']				)

					return fn







def 		_A   (		__A:	Union[str, Any]				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							[]
					for i in range(len(tokenized_data['''input_ids''']				)				):
										__magic_name__							:	Any    							=							{
										    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]				)				),
										    '''attention_mask''': tf.train.Feature(
										        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]				)				),
										}
										__magic_name__							:	Dict    							=							tf.train.Features(feature=__A				)
										__magic_name__							:	str    							=							tf.train.Example(features=__A				)
										__magic_name__							:	Optional[Any]    							=							example.SerializeToString()
										records.append(__A				)
					return records







def 		_A   (		__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							datasets.load_dataset(args.dataset_name							,args.dataset_config							,split=args.split				)

					if args.limit is not None:
										__magic_name__							:	Union[str, Any]    							=							min(len(__A				)							,args.limit				)
										__magic_name__							:	Optional[Any]    							=							dataset.select(range(__A				)				)
										print(F'''Limiting the dataset to {args.limit} entries.'''				)

					__magic_name__							:	Optional[int]    							=							AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

					# Handle output directory creation.
					# For serializing into a Google Cloud Storage Bucket, one needs to first
					# create a bucket.
					if "gs" not in args.output_dir:
										if not os.path.exists(args.output_dir				):
															os.makedirs(args.output_dir				)
										__magic_name__							:	Union[str, Any]    							=							os.path.join(args.output_dir							,args.split				)
										if not os.path.exists(__A				):
															os.makedirs(__A				)
					else:
										__magic_name__							:	int    							=							os.path.join(args.output_dir							,args.split				)

					# Tokenize the whole dataset at once.
					__magic_name__							:	int    							=							tokenize_function(__A				)
					__magic_name__							:	Optional[Any]    							=							dataset.map(__A							,batched=__A							,num_proc=4							,remove_columns=['''text''']				)

					# We need to concatenate all our texts together, and then split the result
					# into chunks of a fixed size, which we will call block_size. To do this, we
					# will use the map method again, with the option batched=True. When we use batched=True,
					# the function we pass to map() will be passed multiple inputs at once, allowing us
					# to group them into more or fewer examples than we had in the input.
					# This allows us to create our new fixed-length samples. The advantage of this
					# method is that we don't lose a whole lot of content from the dataset compared to the
					# case where we simply tokenize with a pre-defined max_length.

					def group_texts(__A:	Dict				):
										# Concatenate all texts.
										__magic_name__							:	Optional[Any]    							=							{k: sum(examples[k]							,[]				) for k in examples.keys()}
										__magic_name__							:	int    							=							len(concatenated_examples[list(examples.keys()				)[0]]				)
										# We drop the small remainder, though you could add padding instead if the model supports it
										# In this, as in all things, we advise you to follow your heart 🫀
										__magic_name__							:	str    							=							(total_length // args.max_length) * args.max_length
										# Split by chunks of max_len.
										__magic_name__							:	Dict    							=							{
										    k: [t[i : i + args.max_length] for i in range(0							,__A							,args.max_length				)]
										    for k, t in concatenated_examples.items()
										}
										return result

					__magic_name__							:	Tuple    							=							dataset_tokenized.map(__A							,batched=__A							,batch_size=1_0_0_0							,num_proc=4				)

					__magic_name__							:	Any    							=							0
					__magic_name__							:	List[str]    							=							0
					for shard in range(0							,len(__A				)							,args.shard_size				):
										__magic_name__							:	Optional[Any]    							=							grouped_dataset[shard : shard + args.shard_size]
										__magic_name__							:	Tuple    							=							len(dataset_snapshot['''input_ids''']				)
										__magic_name__							:	Tuple    							=							os.path.join(__A							,F'''dataset-{shard_count}-{records_containing}.tfrecord'''				)
										__magic_name__							:	List[str]    							=							get_serialized_examples(__A				)

										with tf.io.TFRecordWriter(__A				) as out_file:
															for i in range(len(__A				)				):
																				__magic_name__							:	str    							=							serialized_examples[i]
																				out_file.write(__A				)
															print('''Wrote file {} containing {} records'''.format(__A							,__A				)				)

										shard_count += 1
										total_records += records_containing

					with open(F'''split-{args.split}-records-count.txt'''							,'''w'''				) as f:
										print(F'''Total {args.split} records: {total_records}'''							,file=__A				)


if __name__ == "__main__":
	__lowerCamelCase   :						Optional[int]				=			parse_args()
	main(args)









import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=CanineTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   List[str]:
								super().setUp()
								__magic_name__							:	Optional[int]    							=							CanineTokenizer()
								tokenizer.save_pretrained(self.tmpdirname  )

			@cached_property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return CanineTokenizer.from_pretrained('''google/canine-s'''  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						**lowerCamelCase_			:   Optional[int]  )						->   CanineTokenizer:
								__magic_name__							:	int    							=							self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							1024
								return tokenizer

			@require_torch
			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	List[Any]    							=							self.canine_tokenizer
								__magic_name__							:	Any    							=							['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
								# fmt: off
								__magic_name__							:	Optional[int]    							=							[57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
								# fmt: on
								__magic_name__							:	List[Any]    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						return_tensors='''pt'''  )
								self.assertIsInstance(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							list(batch.input_ids.numpy()[0]  )

								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								self.assertEqual((2, 39)       ,						batch.input_ids.shape  )
								self.assertEqual((2, 39)       ,						batch.attention_mask.shape  )

			@require_torch
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[Any]:
								__magic_name__							:	Any    							=							self.canine_tokenizer
								__magic_name__							:	Dict    							=							['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
								__magic_name__							:	Dict    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						return_tensors='''pt'''  )
								# check if input_ids, attention_mask and token_type_ids are returned
								self.assertIn('''input_ids'''       ,						lowerCamelCase_  )
								self.assertIn('''attention_mask'''       ,						lowerCamelCase_  )
								self.assertIn('''token_type_ids'''       ,						lowerCamelCase_  )

			@require_torch
			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								__magic_name__							:	int    							=							self.canine_tokenizer
								__magic_name__							:	Dict    							=							[
								    '''What\'s the weater?''',
								    '''It\'s about 25 degrees.''',
								]
								__magic_name__							:	List[Any]    							=							tokenizer(
								    text_target=lowerCamelCase_       ,						max_length=32       ,						padding='''max_length'''       ,						truncation=lowerCamelCase_       ,						return_tensors='''pt'''  )
								self.assertEqual(32       ,						targets['''input_ids'''].shape[1]  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Dict:
								# safety check on max_len default value so we are sure the test works
								__magic_name__							:	str    							=							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
								__magic_name__							:	Union[str, 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
																		__magic_name__							:	str    							=							tempfile.mkdtemp()

																		__magic_name__							:	Any    							=							''' He is very happy, UNwant\u00E9d,running'''
																		__magic_name__							:	str    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		tokenizer.save_pretrained(lowerCamelCase_  )

																		__magic_name__							:	str    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	Tuple    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

																		shutil.rmtree(lowerCamelCase_  )

								__magic_name__							:	Any    							=							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
																		__magic_name__							:	Any    							=							tempfile.mkdtemp()

																		__magic_name__							:	Any    							=							''' He is very happy, UNwant\u00E9d,running'''

																		__magic_name__							:	str    							=							tokenizer.additional_special_tokens

																		# We can add a new special token for Canine as follows:
																		__magic_name__							:	str    							=							chr(0XE007  )
																		additional_special_tokens.append(lowerCamelCase_  )
																		tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}  )
																		__magic_name__							:	Tuple    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		tokenizer.save_pretrained(lowerCamelCase_  )

																		__magic_name__							:	List[Any]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	Optional[int]    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )
																		self.assertIn(lowerCamelCase_       ,						after_tokenizer.additional_special_tokens  )
																		self.assertEqual(after_tokenizer.model_max_length       ,						42  )

																		__magic_name__							:	List[Any]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_       ,						model_max_length=43  )
																		self.assertEqual(tokenizer.model_max_length       ,						43  )

																		shutil.rmtree(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								__magic_name__							:	Any    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							,    __magic_name__							:	str    							=							self.get_clean_sequence(lowerCamelCase_  )

																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	Optional[Any]    							=							0XE005
																		__magic_name__							:	Optional[Any]    							=							chr(lowerCamelCase_  )

																		tokenizer.add_special_tokens({'''cls_token''': special_token}  )
																		__magic_name__							:	Optional[int]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )

																		__magic_name__							:	Union[str, Any]    							=							tokenizer.decode(ids + encoded_special_token       ,						clean_up_tokenization_spaces=lowerCamelCase_  )
																		__magic_name__							:	str    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )

																		__magic_name__							:	Optional[Any]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		__magic_name__							:	Union[str, Any]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertEqual(lowerCamelCase_       ,						input_encoded + special_token_id  )

																		__magic_name__							:	Any    							=							tokenizer.decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_  )
																		self.assertTrue(special_token not in decoded  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								__magic_name__							:	str    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	Tuple    							=							chr(0XE005  )
																		__magic_name__							:	Optional[int]    							=							chr(0XE006  )

																		# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																		tokenizer.add_tokens([SPECIAL_TOKEN_1]       ,						special_tokens=lowerCamelCase_  )
																		# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																		# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																		tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}  )

																		__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
																		__magic_name__							:	Optional[int]    							=							tokenizer.tokenize(lowerCamelCase_  )

																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )
																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )
																		self.assertEqual(token_a[0]       ,						lowerCamelCase_  )
																		self.assertEqual(token_a[0]       ,						lowerCamelCase_  )

			@require_tokenizers
			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								__magic_name__							:	List[Any]    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	str    							=							0XE006
																		__magic_name__							:	Optional[int]    							=							chr(lowerCamelCase_  )

																		__magic_name__							:	Optional[Any]    							=							AddedToken(lowerCamelCase_       ,						lstrip=lowerCamelCase_  )
																		tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}  )

																		with tempfile.TemporaryDirectory() as tmp_dir_name:
																							tokenizer.save_pretrained(lowerCamelCase_  )
																							tokenizer.from_pretrained(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	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(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''special_tokens_map.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	List[Any]    							=							json.load(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''tokenizer_config.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	int    							=							json.load(lowerCamelCase_  )

																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	List[str]    							=							0XE006
																		__magic_name__							:	List[str]    							=							chr(lowerCamelCase_  )

																		__magic_name__							:	int    							=							[new_token_a]
																		__magic_name__							:	str    							=							[new_token_a]

																		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
																		__magic_name__							:	int    							=							tokenizer_class.from_pretrained(lowerCamelCase_       ,						extra_ids=0  )
																		self.assertIn(lowerCamelCase_       ,						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(
																		    [new_token_a]       ,						tokenizer_without_change_in_init.convert_ids_to_tokens(
																		        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]  )  )       ,						)

																		__magic_name__							:	Optional[int]    							=							0XE007
																		__magic_name__							:	List[str]    							=							chr(lowerCamelCase_  )
																		# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																		__magic_name__							:	List[str]    							=							[AddedToken(lowerCamelCase_       ,						lstrip=lowerCamelCase_  )]
																		__magic_name__							:	str    							=							tokenizer_class.from_pretrained(
																		    lowerCamelCase_       ,						additional_special_tokens=lowerCamelCase_       ,						extra_ids=0  )

																		self.assertIn(lowerCamelCase_       ,						tokenizer.additional_special_tokens  )
																		# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]       ,						tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]  )  )  )

			@require_tokenizers
			def 							UpperCAmelCase__       ( self			:   Any  )						->   List[str]:
								__magic_name__							:	Optional[int]    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	Union[str, Any]    							=							'''hello world'''
																		if self.space_between_special_tokens:
																							__magic_name__							:	List[Any]    							=							'''[CLS] hello world [SEP]'''
																		else:
																							__magic_name__							:	List[str]    							=							input
																		__magic_name__							:	Dict    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		__magic_name__							:	Union[str, Any]    							=							tokenizer.decode(lowerCamelCase_       ,						spaces_between_special_tokens=self.space_between_special_tokens  )
																		self.assertIn(lowerCamelCase_       ,						[output, output.lower()]  )

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[Any]:
								__magic_name__							:	Dict    							=							self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	str    							=							[
																		    '''bos_token''',
																		    '''eos_token''',
																		    '''unk_token''',
																		    '''sep_token''',
																		    '''pad_token''',
																		    '''cls_token''',
																		    '''mask_token''',
																		]

								__magic_name__							:	Any    							=							'''a'''
								__magic_name__							:	List[str]    							=							ord(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'''  )       ,						[]  )

								__magic_name__							:	Any    							=							0XE006
								__magic_name__							:	str    							=							chr(lowerCamelCase_  )
								setattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''       ,						[additional_special_token_id]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens'''  )       ,						[additional_special_token]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''  )       ,						[additional_special_token_id]  )

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

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[int]:
								pass

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Any:
								pass

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								pass

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

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								pass

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


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



