

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__												=						'▁'

a__												=						{'vocab_file': 'spiece.model'}

a__												=						{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

a__												=						{
    'google/pegasus-xsum': 512,
}


a__												=						logging.get_logger(__name__)

class 							UpperCAmelCase_						(     __snake_case  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       VOCAB_FILES_NAMES

							UpperCAmelCase__			:		str             =       VOCAB_FILES_NAMES
							UpperCAmelCase__			:		Optional[Any]             =       PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__			:		int             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__			:		int             =       ["input_ids", "attention_mask"]


							def __init__(     self			,				_a			,				_a="<pad>"			,				_a="</s>"			,				_a="<unk>"			,				_a="<mask_2>"			,				_a="<mask_1>"			,				_a=None			,				_a=1_0_3			,				_a = None			,				**_a			,				)       ->       None:
												_a      :       Tuple   =						offset
												if additional_special_tokens is not None:
																	if not isinstance(_a			,				_a			):
																						raise TypeError(
																						    F"""additional_special_tokens should be of type {type(_a			)}, but is"""
																						    F""" {type(_a			)}"""			)

																	_a      :       List[Any]   =						(
																	    ([mask_token_sent] + additional_special_tokens)
																	    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																	    else additional_special_tokens
																	)
																	# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																	additional_special_tokens_extended += [
																	    F"""<unk_{i}>""" for i in range(len(_a			)			,				self.offset - 1			)
																	]

																	if len(set(_a			)			) != len(_a			):
																						raise ValueError(
																						    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																						    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""			)
																	_a      :       Union[str, Any]   =						additional_special_tokens_extended
												else:
																	_a      :       Tuple   =						[mask_token_sent] if mask_token_sent is not None else []
																	additional_special_tokens += [F"""<unk_{i}>""" for i in range(2			,				self.offset			)]

												_a      :       List[Any]   =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=_a			,				unk_token=_a			,				mask_token=_a			,				pad_token=_a			,				mask_token_sent=_a			,				offset=_a			,				additional_special_tokens=_a			,				sp_model_kwargs=self.sp_model_kwargs			,				**_a			,				)
												_a      :       Dict   =						mask_token_sent
												_a      :       str   =						vocab_file
												_a      :       Optional[int]   =						spm.SentencePieceProcessor(**self.sp_model_kwargs			)
												self.sp_model.Load(_a			)

												# add special tokens to encoder dict
												_a      :       Dict[int, str]   =						{
												    0: self.pad_token,
												    1: self.eos_token,
												}

												if self.mask_token_sent is not None:
																	self.encoder.update(
																	    {
																	        2: self.mask_token_sent,
																	        3: self.mask_token,
																	    }			)

												if self.offset > 0:
																	# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																	# mask_token_sent is already added to list -> so start at 1
																	self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,				self.offset - 1			)}			)

												_a      :       Dict[str, int]   =						{v: k for k, v in self.encoder.items()}


							@property
							def 							__lowercase							(     self			)       ->       int:
												return len(self.sp_model			) + self.offset


							def 							__lowercase							(     self			)       ->       Dict[str, int]:
												_a      :       str   =						{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			)       ->       Union[str, Any]:
												_a      :       Optional[int]   =						self.__dict__.copy()
												_a      :       str   =						None
												return state


							def __setstate__(     self			,				_a			)       ->       Optional[Any]:
												_a      :       Optional[int]   =						d

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

												_a      :       Any   =						spm.SentencePieceProcessor(**self.sp_model_kwargs			)
												self.sp_model.Load(self.vocab_file			)


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												return self.sp_model.encode(_a			,				out_type=_a			)


							def 							__lowercase							(     self			,				_a			)       ->       int:






												if token in self.decoder:
																	return self.decoder[token]
												elif token in self.added_tokens_decoder:
																	return self.added_tokens_decoder[token]
												_a      :       str   =						self.sp_model.piece_to_id(_a			)
												return sp_id + self.offset


							def 							__lowercase							(     self			,				_a			)       ->       str:






												if index in self.encoder:
																	return self.encoder[index]
												elif index in self.added_tokens_encoder:
																	return self.added_tokens_encoder[index]
												else:
																	_a      :       List[Any]   =						self.sp_model.IdToPiece(index - self.offset			)
												return token


							def 							__lowercase							(     self			,				_a			)       ->       Optional[Any]:
												_a      :       Tuple   =						[]
												_a      :       int   =						''''''
												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      :       str   =						[]
																	else:
																						current_sub_tokens.append(_a			)
												out_string += self.sp_model.decode(_a			)
												return out_string.strip()


							def 							__lowercase							(     self			,				_a=False			)       ->       str:
												return 1


							def 							__lowercase							(     self			,				_a			)       ->       Any:
												_a      :       str   =						set(self.all_special_ids			)  # call it once instead of inside list comp
												all_special_ids.remove(self.unk_token_id			)  # <unk> is only sometimes special

												return [1 if x in all_special_ids else 0 for x in seq]


							def 							__lowercase							(     self			,				_a			,				_a = None			,				_a = False			)       ->       List[int]:






												if already_has_special_tokens:
																	return self._special_token_mask(_a			)
												elif token_ids_a is None:
																	return self._special_token_mask(_a			) + [1]
												else:
																	return self._special_token_mask(token_ids_a + token_ids_a			) + [1]


							def 							__lowercase							(     self			,				_a			,				_a=None			)       ->       List[int]:






												if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]




							def 							__lowercase							(     self			,				_a			,				_a = None			)       ->       Tuple[str]:






												if not os.path.isdir(_a			):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
																	return
												_a      :       Any   =						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      :       List[Any]   =						self.sp_model.serialized_model_proto()
																						fi.write(_a			)

												return (out_vocab_file,)





'''simple docstring'''




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						DebertaTokenizer
     __magic_name__          =						True
     __magic_name__          =						DebertaTokenizerFast





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         super().setUp()

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

         A			:  Optional[int]   =		os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']      )
         A			:  str   =		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(SCREAMING_SNAKE_CASE      ) + '''\n'''      )
         with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''      ) as fp:
             fp.write('''\n'''.join(SCREAMING_SNAKE_CASE      )      )





     def 							__lowerCAmelCase				(				self		,     **SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         kwargs.update(self.special_tokens_map      )
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		'''lower newer'''
         A			:  List[Any]   =		'''lower newer'''
         return input_text, output_text





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  str   =		self.get_tokenizer()
         A			:  List[str]   =		'''lower newer'''
         A			:  int   =		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         A			:  Any   =		tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
         self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         A			:  str   =		tokens + [tokenizer.unk_token]
         A			:  str   =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  List[Any]   =		self.get_tokenizer()
         A			:  Tuple   =		tokenizer('''Hello'''		,     '''World'''      )
         A			:  str   =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['''token_type_ids''']		,     SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Union[str, Any]   =		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

         A			:  Union[str, Any]   =		tokenizer.encode('''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
         A			:  Any   =		tokenizer.encode('''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

         A			:  Tuple   =		tokenizer.encode(
             '''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		tokenizer.encode(
             '''sequence builders'''		,     '''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )

         A			:  List[str]   =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Any   =		[self.tokenizer_class]
         if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class      )

         for tokenizer_class in tokenizer_classes:
             A			:  List[str]   =		tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

             A			:  Dict   =		[
                 '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                 '''ALBERT incorporates two parameter reduction techniques''',
                 '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                 ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                 ''' vocabulary embedding.''',
             ]

             A			:  List[Any]   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE      )
             A			:  List[str]   =		[tokenizer.decode(SCREAMING_SNAKE_CASE		,     skip_special_tokens=SCREAMING_SNAKE_CASE      ) for seq in encoding['''input_ids''']]

             # fmt: off
             A			:  List[str]   =		{
                 '''input_ids''': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 '''token_type_ids''': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 '''attention_mask''': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             A			:  Any   =		[
                 '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                 '''ALBERT incorporates two parameter reduction techniques''',
                 '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                 ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                 ''' vocabulary embedding.''',
             ]

             self.assertDictEqual(encoding.data		,     SCREAMING_SNAKE_CASE      )

             for expected, decoded in zip(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )




