

'''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


__lowerCamelCase						    =      '''▁'''

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

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

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


__lowerCamelCase						    =      logging.get_logger(__name__)


class    A__ ( _snake_case     ):
    lowercase		   =						VOCAB_FILES_NAMES

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



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="</s>"						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__="<mask_2>"						,			UpperCamelCase__="<mask_1>"						,			UpperCamelCase__=None						,			UpperCamelCase__=103						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> None:





           '''simple docstring'''
           A_		   = offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                         raise TypeError(
                             f'''additional_special_tokens should be of type {type(UpperCamelCase__    )}, but is'''
                             f''' {type(UpperCamelCase__    )}'''    )

                  A_		   = (
                      ([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(UpperCamelCase__    )						,			self.offset - 1    )
                  ]

                  if len(set(UpperCamelCase__    )    ) != len(UpperCamelCase__    ):
                         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_		   = additional_special_tokens_extended
           else:
                  A_		   = [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_		   = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCamelCase__						,			unk_token=UpperCamelCase__						,			mask_token=UpperCamelCase__						,			pad_token=UpperCamelCase__						,			mask_token_sent=UpperCamelCase__						,			offset=UpperCamelCase__						,			additional_special_tokens=UpperCamelCase__						,			sp_model_kwargs=self.sp_model_kwargs						,			**UpperCamelCase__						,			)
           A_		   = mask_token_sent
           A_		   = vocab_file
           A_		   = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(UpperCamelCase__    )

           # add special tokens to encoder dict
           A_		   = {
               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_		   = {v: k for k, v in self.encoder.items()}



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return len(self.sp_model    ) + self.offset



    def    snake_case_     (   self    )  -> Dict[str, int]:





           '''simple docstring'''
           A_		   = {self.convert_ids_to_tokens(UpperCamelCase__    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab



    def __getstate__(   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.__dict__.copy()
           A_		   = None
           return state



    def __setstate__(   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__    )  -> List[str]:





           '''simple docstring'''
           return self.sp_model.encode(UpperCamelCase__						,			out_type=UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A_		   = self.sp_model.piece_to_id(UpperCamelCase__    )
           return sp_id + self.offset



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A_		   = self.sp_model.IdToPiece(index - self.offset    )
           return token



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           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(UpperCamelCase__    ) + token
                         A_		   = []
                  else:
                         current_sub_tokens.append(UpperCamelCase__    )
           out_string += self.sp_model.decode(UpperCamelCase__    )
           return out_string.strip()



    def    snake_case_     (   self						,			UpperCamelCase__=False    )  -> Union[str, Any]:





           '''simple docstring'''
           return 1



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = 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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = False    )  -> List[int]:





           '''simple docstring'''
           if already_has_special_tokens:
                  return self._special_token_mask(UpperCamelCase__    )
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCamelCase__    ) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=None    )  -> List[int]:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Tuple[str]:





           '''simple docstring'''
           if not os.path.isdir(UpperCamelCase__    ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
                  return
           A_		   = os.path.join(
               UpperCamelCase__						,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

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

           return (out_vocab_file,)



'''simple docstring'''







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase						    =      get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						ReformerTokenizer
    lowercase		   =						ReformerTokenizerFast
    lowercase		   =						True
    lowercase		   =						False
    lowercase		   =						True



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           super().setUp()

           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )
           tokenizer.save_pretrained(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = """<s>"""
           A_		   = 1

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



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = list(self.get_tokenizer().get_vocab().keys()    )

           self.assertEqual(vocab_keys[0]						,			"""<unk>"""    )
           self.assertEqual(vocab_keys[1]						,			"""<s>"""    )
           self.assertEqual(vocab_keys[-1]						,			"""j"""    )
           self.assertEqual(len(UpperCamelCase__    )						,			1000    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size						,			1000    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           if not self.test_rust_tokenizer:
                  return

           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           A_		   = """I was born in 92000, and this is falsé."""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=15    )  -> int:





           '''simple docstring'''
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         # Simple input
                         A_		   = """This is a simple input"""
                         A_		   = ["""This is a simple input 1""", """This is a simple input 2"""]
                         A_		   = ("""This is a simple input""", """This is a pair""")
                         A_		   = [
                             ("""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(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )

           A_		   = tokenizer.tokenize("""This is a test"""    )
           self.assertListEqual(UpperCamelCase__						,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__    )						,			[285, 46, 10, 170, 382]						,			)

           A_		   = tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   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(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,			)

           A_		   = tokenizer.convert_ids_to_tokens(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   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    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""    )



    @slow
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = """Hello World!"""
           A_		   = [126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @slow
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = (
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           A_		   = [
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @require_torch
    @slow
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           A_		   = list(self.big_tokenizer.get_vocab().keys()    )[:10]
           A_		   = """ """.join(UpperCamelCase__    )
           A_		   = self.big_tokenizer.encode_plus(UpperCamelCase__						,			return_tensors="""pt"""    )
           A_		   = self.big_tokenizer.batch_encode_plus([sequence, sequence]						,			return_tensors="""pt"""    )

           A_		   = ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           A_		   = encoded_sequence["""input_ids"""].shape
           A_		   = ReformerModel(UpperCamelCase__    )

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**UpperCamelCase__    )
                  model(**UpperCamelCase__    )







    @slow
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # fmt: off
           A_		   = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           A_		   = [
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__						,			model_name="""google/reformer-crime-and-punishment"""						,			revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""						,			padding=UpperCamelCase__						,			sequences=UpperCamelCase__						,			)
