




'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a  :				Optional[Any]									=  logging.get_logger(__name__)

a  :				Union[str, Any]									=  '▁'

a  :				int									=  {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

a  :				str									=  {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

a  :				Optional[int]									=  {'vinai/bartpho-syllable': 1024}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]


				def __init__(		self    :					Optional[int]       ,				lowercase_    :					int       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]="<s>"       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					str="</s>"       ,				lowercase_    :					Union[str, Any]="<s>"       ,				lowercase_    :					Optional[int]="<unk>"       ,				lowercase_    :					Optional[Any]="<pad>"       ,				lowercase_    :					Optional[Any]="<mask>"       ,				lowercase_    :					Optional[Dict[str, Any]] = None       ,				**lowercase_    :					List[Any]       ,				):
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_									=				AddedToken(lowercase_       ,				lstrip=lowercase_       ,				rstrip=lowercase_					) if isinstance(lowercase_       ,				lowercase_					) else mask_token

									snake_case_									=				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=lowercase_       ,				eos_token=lowercase_       ,				unk_token=lowercase_       ,				sep_token=lowercase_       ,				cls_token=lowercase_       ,				pad_token=lowercase_       ,				mask_token=lowercase_       ,				sp_model_kwargs=self.sp_model_kwargs       ,				**lowercase_       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				monolingual_vocab_file
									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(lowercase_					)					)

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									snake_case_									=				{}
									snake_case_									=				0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
														if str(lowercase_					) not in self.fairseq_tokens_to_ids:
																			snake_case_									=				cnt
																			cnt += 1
									with open(lowercase_       ,				'''r'''       ,				encoding='''utf-8'''					) as f:
														for line in f.readlines():
																			snake_case_									=				line.strip().split()[0]
																			snake_case_									=				len(self.fairseq_tokens_to_ids					)
									if str(lowercase_					) not in self.fairseq_tokens_to_ids:
														snake_case_									=				len(self.fairseq_tokens_to_ids					)

									snake_case_									=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}


				def __getstate__(		self    :					Tuple					):
									snake_case_									=				self.__dict__.copy()
									snake_case_									=				None
									snake_case_									=				self.sp_model.serialized_model_proto()
									return state


				def __setstate__(		self    :					Optional[Any]       ,				lowercase_    :					Optional[Any]					):
									snake_case_									=				d

									# for backward compatibility
									if not hasattr(self       ,				'''sp_model_kwargs'''					):
														snake_case_									=				{}

									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):

									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_									=				[self.cls_token_id]
									snake_case_									=				[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep


				def   A_ (		self    :					List[str]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None       ,				lowercase_    :					bool = False					):

									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase_       ,				token_ids_a=lowercase_       ,				already_has_special_tokens=lowercase_					)

									if token_ids_a is None:
														return [1] + ([0] * len(lowercase_					)) + [1]
									return [1] + ([0] * len(lowercase_					)) + [1, 1] + ([0] * len(lowercase_					)) + [1]


				def   A_ (		self    :					str       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.sep_token_id]
									snake_case_									=				[self.cls_token_id]

									if token_ids_a is None:
														return len(cls + token_ids_a + sep					) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]


				@property
				def   A_ (		self    :					Any					):
									return len(self.fairseq_ids_to_tokens					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				{self.convert_ids_to_tokens(lowercase_					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


				def   A_ (		self    :					Dict       ,				lowercase_    :					str					):
									return self.sp_model.encode(lowercase_       ,				out_type=lowercase_					)


				def   A_ (		self    :					str       ,				lowercase_    :					List[Any]					):
									if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
									else:
														return self.unk_token_id


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					int					):
									return self.fairseq_ids_to_tokens[index]


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					Optional[Any]					):
									snake_case_									=				''''''.join(lowercase_					).replace(lowercase_       ,				''' '''					).strip()
									return out_string


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']       ,				)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					) and os.path.isfile(self.vocab_file					):
														copyfile(self.vocab_file       ,				lowercase_					)
									elif not os.path.isfile(self.vocab_file					):
														with open(lowercase_       ,				'''wb'''					) as fi:
																			snake_case_									=				self.sp_model.serialized_model_proto()
																			fi.write(lowercase_					)

									if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
									    lowercase_					) and os.path.isfile(self.monolingual_vocab_file					):
														copyfile(self.monolingual_vocab_file       ,				lowercase_					)
									elif not os.path.isfile(self.monolingual_vocab_file					):
														with open(lowercase_       ,				'''w'''       ,				encoding='''utf-8'''					) as fp:
																			for token in self.fairseq_tokens_to_ids:
																								if token not in self.all_special_tokens:
																													fp.write(F"{str(lowercase_					)} \n"					)

									return out_vocab_file, out_monolingual_vocab_file




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    GPTaTokenizer
							UpperCAmelCase__       :						str	    =    GPTaTokenizerFast
							UpperCAmelCase__       :						Tuple	    =    True
							UpperCAmelCase__       :						List[Any]	    =    {"add_prefix_space": True}
							UpperCAmelCase__       :						int	    =    False


							def 				__lowercase				(			self       )    ->    int:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												_a	:    str         =		[
												    '''l''',
												    '''o''',
												    '''w''',
												    '''e''',
												    '''r''',
												    '''s''',
												    '''t''',
												    '''i''',
												    '''d''',
												    '''n''',
												    '''\u0120''',
												    '''\u0120l''',
												    '''\u0120n''',
												    '''\u0120lo''',
												    '''\u0120low''',
												    '''er''',
												    '''\u0120lowest''',
												    '''\u0120newer''',
												    '''\u0120wider''',
												    '''<unk>''',
												    '''<|endoftext|>''',
												]
												_a	:    List[Any]         =		dict(zip(_a       ,							range(len(_a       )       )       )       )
												_a	:    Tuple         =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
												_a	:    List[str]         =		{'''unk_token''': '''<unk>'''}

												_a	:    List[str]         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
												_a	:    Tuple         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''merges_file''']       )
												with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write(json.dumps(_a       ) + '''\n'''       )
												with open(self.merges_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write('''\n'''.join(_a       )       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[str]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizer.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[Any]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[Any]:
												_a	:    Tuple         =		'''lower newer'''
												_a	:    Tuple         =		'''lower newer'''
												return input_text, output_text


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Any         =		GPTaTokenizer(self.vocab_file       ,							self.merges_file       ,							**self.special_tokens_map       )
												_a	:    int         =		'''lower newer'''
												_a	:    int         =		['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												self.assertListEqual(_a       ,							_a       )

												_a	:    Optional[int]         =		tokens + [tokenizer.unk_token]
												_a	:    List[Any]         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												if not self.test_rust_tokenizer:
																	return

												_a	:    int         =		self.get_tokenizer()
												_a	:    Tuple         =		self.get_rust_tokenizer(add_prefix_space=_a       )

												_a	:    Tuple         =		'''lower newer'''

												# Testing tokenization
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												_a	:    Optional[Any]         =		rust_tokenizer.tokenize(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids without special tokens
												_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       ,							add_prefix_space=_a       )
												_a	:    List[Any]         =		rust_tokenizer.encode(_a       ,							add_special_tokens=_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids with special tokens
												_a	:    List[str]         =		self.get_rust_tokenizer(add_prefix_space=_a       )
												_a	:    List[str]         =		tokenizer.encode(_a       ,							add_prefix_space=_a       )
												_a	:    Tuple         =		rust_tokenizer.encode(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing the unknown token
												_a	:    Optional[Any]         =		tokens + [rust_tokenizer.unk_token]
												_a	:    str         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    int:
												# It's very difficult to mix/test pretokenization with byte-level
												# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
												pass


							def 				__lowercase				(			self       ,							_a=1_5       )    ->    str:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																						_a	:    Optional[int]         =		self.rust_tokenizer_class.from_pretrained(_a       ,							**_a       )

																						# Simple input
																						_a	:    List[str]         =		'''This is a simple input'''
																						_a	:    Optional[Any]         =		['''This is a simple input 1''', '''This is a simple input 2''']
																						_a	:    Tuple         =		('''This is a simple input''', '''This is a pair''')
																						_a	:    Optional[Any]         =		[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    int         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							pad_token='''<pad>'''       )

												# Simple input
												_a	:    int         =		'''This is a simple input'''
												_a	:    int         =		['''This is a simple input looooooooong''', '''This is a simple input''']
												_a	:    Any         =		('''This is a simple input''', '''This is a pair''')
												_a	:    Dict         =		[
												    ('''This is a simple input loooooong''', '''This is a simple input'''),
												    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
												]

												_a	:    Optional[int]         =		tokenizer.pad_token_id

												_a	:    List[Any]         =		tokenizer(_a       ,							padding='''max_length'''       ,							max_length=3_0       ,							return_tensors='''np'''       )
												_a	:    Optional[int]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )
												_a	:    int         =		tokenizer(*_a       ,							padding='''max_length'''       ,							max_length=6_0       ,							return_tensors='''np'''       )
												_a	:    Union[str, Any]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )

												# s
												# test single string max_length padding
												self.assertEqual(out_s['''input_ids'''].shape[-1]       ,							3_0       )
												self.assertTrue(pad_token_id in out_s['''input_ids''']       )
												self.assertTrue(0 in out_s['''attention_mask''']       )

												# s2
												# test automatic padding
												self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,							3_3       )
												# long slice doesn't have padding
												self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]       )
												self.assertFalse(0 in out_sa['''attention_mask'''][0]       )
												# short slice does have padding
												self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]       )
												self.assertTrue(0 in out_sa['''attention_mask'''][1]       )

												# p
												# test single pair max_length padding
												self.assertEqual(out_p['''input_ids'''].shape[-1]       ,							6_0       )
												self.assertTrue(pad_token_id in out_p['''input_ids''']       )
												self.assertTrue(0 in out_p['''attention_mask''']       )

												# p2
												# test automatic padding pair
												self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,							5_2       )
												# long slice pair doesn't have padding
												self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]       )
												self.assertFalse(0 in out_pa['''attention_mask'''][0]       )
												# short slice pair does have padding
												self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]       )
												self.assertTrue(0 in out_pa['''attention_mask'''][1]       )


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		'''$$$'''
												_a	:    List[Any]         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							bos_token=_a       ,							add_bos_token=_a       )

												_a	:    Any         =		'''This is a simple input'''
												_a	:    str         =		['''This is a simple input 1''', '''This is a simple input 2''']

												_a	:    Tuple         =		tokenizer.bos_token_id

												_a	:    Optional[Any]         =		tokenizer(_a       )
												_a	:    str         =		tokenizer(_a       )

												self.assertEqual(out_s.input_ids[0]       ,							_a       )
												self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids       )       )

												_a	:    str         =		tokenizer.decode(out_s.input_ids       )
												_a	:    Optional[Any]         =		tokenizer.batch_decode(out_sa.input_ids       )

												self.assertEqual(decode_s.split()[0]       ,							_a       )
												self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa       )       )


							def 				__lowercase				(			self       )    ->    str:
												pass




							def 				__lowercase				(			self       )    ->    Dict:
												# TODO: change to self.get_tokenizers() when the fast version is implemented
												_a	:    Optional[int]         =		[self.get_tokenizer(do_lower_case=_a       ,							add_bos_token=_a       )]
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
																						_a	:    Tuple         =		'''Encode this.'''
																						_a	:    Optional[Any]         =		'''This one too please.'''
																						_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						encoded_sequence += tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						_a	:    List[str]         =		tokenizer.encode_plus(
																						    _a       ,							_a       ,							add_special_tokens=_a       ,							return_special_tokens_mask=_a       ,							)
																						_a	:    int         =		encoded_sequence_dict['''input_ids''']
																						_a	:    int         =		encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(_a       )       ,							len(_a       )       )

																						_a	:    List[Any]         =		[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a       )
																						]
																						_a	:    str         =		[x for x in filtered_sequence if x is not None]
																						self.assertEqual(_a       ,							_a       )



@require_tokenizers
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    List[Any]:
												# More context:
												# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
												# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
												# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

												_a	:    Any         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )
												_a	:    int         =		'''A photo of a cat'''

												_a	:    List[Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''test_opt'''       )

												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''./test_opt'''       )
												_a	:    Any         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							use_slow=_a       )
												_a	:    Any         =		'''A photo of a cat'''

												_a	:    Optional[int]         =		tokenizer.encode(
												    _a       ,							)
												# Same as above
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )




							@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''       )
							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )

												_a	:    Optional[Any]         =		'''bos'''
												_a	:    Optional[Any]         =		tokenizer.get_vocab()['''bos''']

												_a	:    str         =		'''A photo of a cat'''
												_a	:    int         =		tokenizer.encode(
												    _a       ,							)
												# We changed the bos token
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''./tok'''       )
												_a	:    Dict         =		AutoTokenizer.from_pretrained('''./tok'''       )
												self.assertTrue(tokenizer.is_fast       )
												_a	:    Union[str, Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )



