


'''simple docstring'''






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_   :      Optional[Any]				    =      get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
class 	a       (       a__					,			unittest.TestCase    ):
	_lowerCAmelCase	   =		XLMProphetNetTokenizer
	_lowerCAmelCase	   =		False
	_lowerCAmelCase	   =		True
	def 							__UpperCAmelCase						(	self  )   ->							Dict:
						super().setUp()

						# We have a SentencePiece fixture for testing
						_a      =							XLMProphetNetTokenizer(_lowerCamelCase ,			keep_accents=_lowerCamelCase  )
						tokenizer.save_pretrained(self.tmpdirname  )
	def 							__UpperCAmelCase						(	self  )   ->							Tuple:
						_a      =							'''[PAD]'''
						_a      =							0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase  ) ,			_lowerCamelCase  )
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase  ) ,			_lowerCamelCase  )
	def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
						_a      =							list(self.get_tokenizer().get_vocab().keys()  )

						self.assertEqual(vocab_keys[0] ,			'[PAD]'  )
						self.assertEqual(vocab_keys[1] ,			'[CLS]'  )
						self.assertEqual(vocab_keys[-1] ,			'j'  )
						self.assertEqual(len(_lowerCamelCase  ) ,			10_12  )
	def 							__UpperCAmelCase						(	self  )   ->							List[str]:
						self.assertEqual(self.get_tokenizer().vocab_size ,			10_12  )
	def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
						_a      =							XLMProphetNetTokenizer(_lowerCamelCase ,			keep_accents=_lowerCamelCase  )

						_a      =							tokenizer.tokenize('This is a test'  )
						self.assertListEqual(_lowerCamelCase ,			['▁This', '▁is', '▁a', '▁t', 'est']  )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(_lowerCamelCase  ) ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,			)

						_a      =							tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
						self.assertListEqual(
						    _lowerCamelCase ,			[
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '9',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        'é',
						        '.',
						    ] ,			)
						_a      =							tokenizer.convert_tokens_to_ids(_lowerCamelCase  )
						self.assertListEqual(
						    _lowerCamelCase ,			[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ] ,			)

						_a      =							tokenizer.convert_ids_to_tokens(_lowerCamelCase  )
						self.assertListEqual(
						    _lowerCamelCase ,			[
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '[UNK]',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        '[UNK]',
						        '.',
						    ] ,			)
	@cached_property
	def 							__UpperCAmelCase						(	self  )   ->							List[str]:
						return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'  )
	@slow
	def 							__UpperCAmelCase						(	self  )   ->							Any:
						_a      =							'''Hello World!'''
						_a      =							[3_53_89, 66_72, 49, 2]
						self.assertListEqual(_lowerCamelCase ,			self.big_tokenizer.encode(_lowerCamelCase  )  )




	@slow
	def 							__UpperCAmelCase						(	self  )   ->							int:
						# fmt: off
						_a      =							{'''input_ids''': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 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, 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], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 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, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=_lowerCamelCase ,			model_name='microsoft/xprophetnet-large-wiki100-cased' ,			revision='1acad1643ddd54a44df6a1b797ada8373685d90e' ,			)





'''simple docstring'''






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_   :      Union[str, Any]				    =      logging.get_logger(__name__)

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _a      =							d

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

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      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(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

      return (out_vocab_file,)

