




"""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_      =    logging.get_logger(__name__)

A_      =    '''▁'''

A_      =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

A_      =    {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

A_      =    {
    '''facebook/xglm-564M''': 20_48,
}






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = VOCAB_FILES_NAMES
 lowercase__			 = PRETRAINED_VOCAB_FILES_MAP
 lowercase__			 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__			 = ["input_ids", "attention_mask"]


 def __init__(    self:		List[str],				a_:		Any,				a_:		Union[str, Any]="<s>",				a_:		int="</s>",				a_:		Optional[Any]="</s>",				a_:		str="<s>",				a_:		Union[str, Any]="<unk>",				a_:		Optional[Any]="<pad>",				a_:		Optional[Dict[str, Any]] = None,				**a_:		Any,				):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  {} if sp_model_kwargs is None else sp_model_kwargs

   # Compatibility with the original tokenizer
   _snake_case							:		Optional[Any]								=  7
   _snake_case							:		Union[str, Any]								=  [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

   _snake_case							:		Dict								=  kwargs.get("""additional_special_tokens""",				[]		)
   kwargs["additional_special_tokens"] += [
       word for word in madeup_words if word not in kwargs["additional_special_tokens"]
   ]

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

   _snake_case							:		Tuple								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.Load(str(a_		)		)
   _snake_case							:		int								=  vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   _snake_case							:		Any								=  1

   # Mimic fairseq token-to-id alignment for the first 4 token
   _snake_case							:		Optional[Any]								=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

   _snake_case							:		Optional[int]								=  len(self.sp_model		)
   _snake_case							:		Dict								=  {f"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
   self.fairseq_tokens_to_ids.update(a_		)

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


 def __getstate__(    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.__dict__.copy()
   _snake_case							:		List[str]								=  None
   _snake_case							:		List[Any]								=  self.sp_model.serialized_model_proto()
   return state


 def __setstate__(    self:		Any,				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  d

   # for backward compatibility
   if not hasattr(self,				"""sp_model_kwargs"""		):
     _snake_case							:		Tuple								=  {}

   _snake_case							:		str								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		List[int],				a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   if token_ids_a is None:
     return [self.sep_token_id] + token_ids_a
   _snake_case							:		Any								=  [self.sep_token_id]
   return sep + token_ids_a + sep + sep + token_ids_a


 def 			UpperCamelCase_       (    self:		Any,				a_:		List[int],				a_:		Optional[List[int]] = None,				a_:		bool = False		):




   '''simple docstring'''



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

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


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		List[int],				a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  [self.sep_token_id]

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


 @property
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  {self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
   vocab.update(self.added_tokens_encoder		)
   return vocab


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		str		):




   '''simple docstring'''



   return self.sp_model.encode(a_,				out_type=a_		)


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   _snake_case							:		int								=  self.sp_model.PieceToId(a_		)

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Dict		):




   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset		)


 def 			UpperCamelCase_       (    self:		List[str],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  """""".join(a_		).replace(a_,				""" """		).strip()
   return out_string


 def 			UpperCamelCase_       (    self:		Dict,				a_:		str,				a_:		Optional[str] = None		):




   '''simple docstring'''



   if not os.path.isdir(a_		):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
     return
   _snake_case							:		Tuple								=  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:
       _snake_case							:		int								=  self.sp_model.serialized_model_proto()
       fi.write(a_		)

   return (out_vocab_file,)





"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = AlbertTokenizer
 lowercase__			 = AlbertTokenizerFast
 lowercase__			 = True
 lowercase__			 = True
 lowercase__			 = True


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Optional[int]								=  AlbertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		int		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """this is a test"""
   _snake_case							:		Optional[int]								=  """this is a test"""
   return input_text, output_text


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  """<pad>"""
   _snake_case							:		List[Any]								=  0

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_		),				a_		)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_		),				a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  list(self.get_tokenizer().get_vocab().keys()		)

   self.assertEqual(vocab_keys[0],				"""<pad>"""		)
   self.assertEqual(vocab_keys[1],				"""<unk>"""		)
   self.assertEqual(vocab_keys[-1],				"""▁eloquent"""		)
   self.assertEqual(len(a_		),				30_000		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,				30_000		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		List[str]								=  self.get_rust_tokenizer()

   _snake_case							:		Optional[int]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		Optional[Any]								=  tokenizer.tokenize(a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		Any								=  tokenizer.encode(a_,				add_special_tokens=a_		)
   _snake_case							:		Any								=  rust_tokenizer.encode(a_,				add_special_tokens=a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		int								=  self.get_rust_tokenizer()
   _snake_case							:		Dict								=  tokenizer.encode(a_		)
   _snake_case							:		Optional[int]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,				a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  AlbertTokenizer(a_,				keep_accents=a_		)

   _snake_case							:		Any								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(a_,				["""▁this""", """▁is""", """▁a""", """▁test"""]		)

   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),				[48, 25, 21, 1_289]		)

   _snake_case							:		Dict								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]		)
   _snake_case							:		str								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(a_,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

   _snake_case							:		Tuple								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""],				)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  AlbertTokenizer(a_		)

   _snake_case							:		int								=  tokenizer.encode("""sequence builders"""		)
   _snake_case							:		Optional[int]								=  tokenizer.encode("""multi-sequence build"""		)

   _snake_case							:		Any								=  tokenizer.build_inputs_with_special_tokens(a_		)
   _snake_case							:		Union[str, Any]								=  tokenizer.build_inputs_with_special_tokens(a_,				a_		)

   assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
   assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
       tokenizer.sep_token_id
   ]


 @slow
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Any								=  {"""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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=a_,				model_name="""albert-base-v2""",				revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""",				)


