

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


A_			:					str    			=     '▁'

A_			:					Optional[int]    			=     {'vocab_file': 'spiece.model'}

A_			:					Dict    			=     {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A_			:					int    			=     {
    'google/reformer-crime-and-punishment': 52_4288,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[Any]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  Union[str, Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  Any    				=			['''input_ids''', '''attention_mask''']






					def __init__(					self       ,			A__       ,			A__="</s>"       ,			A__="<unk>"       ,			A__=[]       ,			A__ = None       ,			**A__       ,			):
						A__				:       str	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=A__       ,			unk_token=A__       ,			additional_special_tokens=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       List[str]	  =						vocab_file
						A__				:       Any	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






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






					def __getstate__(					self     ):
						A__				:       Dict	  =						self.__dict__.copy()
						A__				:       str	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       List[Any]	  =						d

						# for backward compatibility
						if not hasattr(self       ,			"""sp_model_kwargs"""     ):
							A__				:       List[Any]	  =						{}

						A__				:       List[Any]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						if index < self.sp_model.get_piece_size():
							A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       Optional[int]	  =						[]
						A__				:       Optional[Any]	  =						""""""
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								out_string += self.sp_model.decode(A__     ) + token
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       List[str]	  =						os.path.join(
						    A__       ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

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

						return (out_vocab_file,)






import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class        _a		(__magic_name__						,  unittest.TestCase							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			RoCBertTokenizer
					UpperCAmelCase__:  Dict    				=			None
					UpperCAmelCase__:  Optional[Any]    				=			False
					UpperCAmelCase__:  Union[str, Any]    				=			True
					UpperCAmelCase__:  Union[str, Any]    				=			filter_non_english






					def     __A     (					self     ):
						super().setUp()

						A__				:       Tuple	  =						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
						A__				:       Union[str, Any]	  =						{}
						A__				:       Dict	  =						{}
						for i, value in enumerate(A__     ):
							A__				:       Optional[int]	  =						i
							A__				:       Optional[int]	  =						i
						A__				:       Dict	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""vocab_file"""]     )
						A__				:       Optional[Any]	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""word_shape_file"""]     )
						A__				:       int	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""word_pronunciation_file"""]     )
						with open(self.vocab_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )
						with open(self.word_shape_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as word_shape_writer:
							json.dump(A__       ,			A__       ,			ensure_ascii=A__     )
						with open(self.word_pronunciation_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as word_pronunciation_writer:
							json.dump(A__       ,			A__       ,			ensure_ascii=A__     )






					def     __A     (					self     ):
						A__				:       Dict	  =						self.tokenizer_class(self.vocab_file       ,			self.word_shape_file       ,			self.word_pronunciation_file     )

						A__				:       Optional[Any]	  =						tokenizer.tokenize("""你好[SEP]你是谁"""     )
						self.assertListEqual(A__       ,			["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]     )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )
						self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )
						self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer()

						self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""     )       ,			["""ah""", """\u535A""", """\u63A8""", """zz"""]     )






					def     __A     (					self     ):
						A__				:       List[str]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )       ,			["""hello""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       int	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hällo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""h\u00E9llo"""]     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

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






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

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






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

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






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			never_split=["""[UNK]"""]     )

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






					def     __A     (					self     ):
						A__				:       List[Any]	  =						["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						A__				:       Any	  =						{}
						for i, token in enumerate(A__     ):
							A__				:       Optional[int]	  =						i
						A__				:       Optional[Any]	  =						RoCBertWordpieceTokenizer(vocab=A__       ,			unk_token="""[UNK]"""     )

						self.assertListEqual(tokenizer.tokenize(""""""     )       ,			[]     )

						self.assertListEqual(tokenizer.tokenize("""unwanted running"""     )       ,			["""un""", """##want""", """##ed""", """runn""", """##ing"""]     )

						self.assertListEqual(tokenizer.tokenize("""unwantedX running"""     )       ,			["""[UNK]""", """runn""", """##ing"""]     )






					def     __A     (					self     ):
						self.assertTrue(_is_whitespace(""" """     )     )
						self.assertTrue(_is_whitespace("""\t"""     )     )
						self.assertTrue(_is_whitespace("""\r"""     )     )
						self.assertTrue(_is_whitespace("""\n"""     )     )
						self.assertTrue(_is_whitespace("""\u00A0"""     )     )

						self.assertFalse(_is_whitespace("""A"""     )     )
						self.assertFalse(_is_whitespace("""-"""     )     )






					def     __A     (					self     ):
						self.assertTrue(_is_control("""\u0005"""     )     )

						self.assertFalse(_is_control("""A"""     )     )
						self.assertFalse(_is_control(""" """     )     )
						self.assertFalse(_is_control("""\t"""     )     )
						self.assertFalse(_is_control("""\r"""     )     )






					def     __A     (					self     ):
						self.assertTrue(_is_punctuation("""-"""     )     )
						self.assertTrue(_is_punctuation("""$"""     )     )
						self.assertTrue(_is_punctuation("""`"""     )     )
						self.assertTrue(_is_punctuation("""."""     )     )

						self.assertFalse(_is_punctuation("""A"""     )     )
						self.assertFalse(_is_punctuation(""" """     )     )






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						self.get_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(A__     ) for t in ["""Test""", """\xad""", """test"""]]       ,			[["""[UNK]"""], [], ["""[UNK]"""]]     )

						if self.test_rust_tokenizer:
							A__				:       Tuple	  =						self.get_rust_tokenizer()
							self.assertListEqual(
							    [rust_tokenizer.tokenize(A__     ) for t in ["""Test""", """\xad""", """test"""]]       ,			[["""[UNK]"""], [], ["""[UNK]"""]]     )






					def     __A     (					self     ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
								A__				:       Dict	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       str	  =						F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
								A__				:       Union[str, Any]	  =						tokenizer_r.encode_plus(
								    A__       ,			return_attention_mask=A__       ,			return_token_type_ids=A__       ,			return_offsets_mapping=A__       ,			add_special_tokens=A__       ,			)

								A__				:       Any	  =						tokenizer_r.do_lower_case if hasattr(A__       ,			"""do_lower_case"""     ) else False
								A__				:       List[str]	  =						(
								    [
								        ((0, 0), tokenizer_r.cls_token),
								        ((0, 1), """A"""),
								        ((1, 2), ""","""),
								        ((3, 5), """na"""),
								        ((5, 6), """##ï"""),
								        ((6, 8), """##ve"""),
								        ((9, 15), tokenizer_r.mask_token),
								        ((16, 21), """Allen"""),
								        ((21, 23), """##NL"""),
								        ((23, 24), """##P"""),
								        ((25, 33), """sentence"""),
								        ((33, 34), """."""),
								        ((0, 0), tokenizer_r.sep_token),
								    ]
								    if not do_lower_case
								    else [
								        ((0, 0), tokenizer_r.cls_token),
								        ((0, 1), """a"""),
								        ((1, 2), ""","""),
								        ((3, 8), """naive"""),
								        ((9, 15), tokenizer_r.mask_token),
								        ((16, 21), """allen"""),
								        ((21, 23), """##nl"""),
								        ((23, 24), """##p"""),
								        ((25, 33), """sentence"""),
								        ((33, 34), """."""),
								        ((0, 0), tokenizer_r.sep_token),
								    ]
								)

								self.assertEqual(
								    [e[1] for e in expected_results]       ,			tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]     )     )
								self.assertEqual([e[0] for e in expected_results]       ,			tokens["""offset_mapping"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						["""的""", """人""", """有"""]
						A__				:       List[str]	  =						"""""".join(A__     )
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
								A__				:       Any	  =						True
								A__				:       int	  =						self.tokenizer_class.from_pretrained(A__       ,			**A__     )
								A__				:       Dict	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       List[str]	  =						tokenizer_p.encode(A__       ,			add_special_tokens=A__     )
								A__				:       List[Any]	  =						tokenizer_r.encode(A__       ,			add_special_tokens=A__     )

								A__				:       Tuple	  =						tokenizer_r.convert_ids_to_tokens(A__     )
								A__				:       int	  =						tokenizer_p.convert_ids_to_tokens(A__     )

								# it is expected that each Chinese character is not preceded by "##"
								self.assertListEqual(A__       ,			A__     )
								self.assertListEqual(A__       ,			A__     )

								A__				:       Optional[int]	  =						False
								A__				:       Union[str, Any]	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )
								A__				:       str	  =						self.tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       Tuple	  =						tokenizer_r.encode(A__       ,			add_special_tokens=A__     )
								A__				:       List[str]	  =						tokenizer_p.encode(A__       ,			add_special_tokens=A__     )

								A__				:       Union[str, Any]	  =						tokenizer_r.convert_ids_to_tokens(A__     )
								A__				:       Tuple	  =						tokenizer_p.convert_ids_to_tokens(A__     )

								# it is expected that only the first Chinese character is not preceded by "##".
								A__				:       str	  =						[
								    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(A__     )
								]
								self.assertListEqual(A__       ,			A__     )
								self.assertListEqual(A__       ,			A__     )






					@slow
					def     __A     (					self     ):
						A__				:       str	  =						self.tokenizer_class(self.vocab_file       ,			self.word_shape_file       ,			self.word_pronunciation_file     )

						A__				:       Optional[Any]	  =						tokenizer.encode("""你好"""       ,			add_special_tokens=A__     )
						A__				:       List[Any]	  =						tokenizer.encode("""你是谁"""       ,			add_special_tokens=A__     )

						A__				:       Any	  =						tokenizer.build_inputs_with_special_tokens(A__     )
						A__				:       Any	  =						tokenizer.build_inputs_with_special_tokens(A__       ,			A__     )

						assert encoded_sentence == [1] + text + [2]
						assert encoded_pair == [1] + text + [2] + text_a + [2]






					def     __A     (					self     ):
						A__				:       List[str]	  =						self.get_tokenizers(do_lower_case=A__     )
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
								A__				:       Optional[int]	  =						"""你好，你是谁"""
								A__				:       List[str]	  =						tokenizer.tokenize(A__     )
								A__				:       Union[str, Any]	  =						tokenizer.convert_tokens_to_ids(A__     )
								A__				:       str	  =						tokenizer.convert_tokens_to_shape_ids(A__     )
								A__				:       Optional[int]	  =						tokenizer.convert_tokens_to_pronunciation_ids(A__     )
								A__				:       Union[str, Any]	  =						tokenizer.prepare_for_model(
								    A__       ,			A__       ,			A__       ,			add_special_tokens=A__     )

								A__				:       int	  =						tokenizer.encode_plus(A__       ,			add_special_tokens=A__     )

								self.assertEqual(A__       ,			A__     )



