



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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      DebertaTokenizer
    __lowerCamelCase			: Any					=      True
    __lowerCamelCase			: Tuple					=      DebertaTokenizerFast






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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        A :       int          =   [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """[UNK]""",
        ]
        A :       Optional[Any]          =   dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A :       Tuple          =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        A :       List[str]          =   {"""unk_token""": """[UNK]"""}

        A :       Optional[Any]          =   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(lowerCamelCase__		) + """\n"""		)
        with open(self.merges_file,		"""w""",		encoding="""utf-8"""		) as fp:
            fp.write("""\n""".join(lowerCamelCase__		)		)






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Any          =   """lower newer"""
        A :       Dict          =   """lower newer"""
        return input_text, output_text






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.get_tokenizer()
        A :       Dict          =   """lower newer"""
        A :       List[Any]          =   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        A :       Optional[Any]          =   tokenizer.tokenize(lowerCamelCase__		)
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self.get_tokenizer()
        A :       Any          =   tokenizer("""Hello""",		"""World"""		)
        A :       Optional[int]          =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["""token_type_ids"""],		lowerCamelCase__		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""		)

        A :       Tuple          =   tokenizer.encode("""sequence builders""",		add_special_tokens=lowerCamelCase__		)
        A :       Optional[Any]          =   tokenizer.encode("""multi-sequence build""",		add_special_tokens=lowerCamelCase__		)

        A :       str          =   tokenizer.encode(
            """sequence builders""",		add_special_tokens=lowerCamelCase__,		add_prefix_space=lowerCamelCase__		)
        A :       Any          =   tokenizer.encode(
            """sequence builders""",		"""multi-sequence build""",		add_special_tokens=lowerCamelCase__,		add_prefix_space=lowerCamelCase__		)

        A :       Any          =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__		)
        A :       str          =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__,		lowerCamelCase__		)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   [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 :       int          =   [
                """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 :       Tuple          =   tokenizer(lowerCamelCase__,		padding=lowerCamelCase__		)
            A :       Dict          =   [tokenizer.decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		) for seq in encoding["""input_ids"""]]

            # fmt: off
            A :       Optional[int]          =   {
                """input_ids""": [
                    [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 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, 1_1126, 565, 2_4536, 80, 4_3797, 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, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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 :       List[str]          =   [
                """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,		lowerCamelCase__		)

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







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


SCREAMING_SNAKE_CASE_:List[Any]             =						"""▁"""

SCREAMING_SNAKE_CASE_:int             =						{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_:Any             =						{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

SCREAMING_SNAKE_CASE_:Optional[Any]             =						{
    """google/pegasus-xsum""": 512,
}


SCREAMING_SNAKE_CASE_:Tuple             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      VOCAB_FILES_NAMES

    __lowerCamelCase			: List[Any]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Optional[Any]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: str					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: str					=      ["input_ids", "attention_mask"]






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__="<pad>",		lowerCamelCase__="</s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<mask_2>",		lowerCamelCase__="<mask_1>",		lowerCamelCase__=None,		lowerCamelCase__=103,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       int          =   offset
        if additional_special_tokens is not None:
            if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                raise TypeError(
                    f'''additional_special_tokens should be of type {type(lowerCamelCase__		)}, but is'''
                    f''' {type(lowerCamelCase__		)}'''		)

            A :       Dict          =   (
                ([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(lowerCamelCase__		),		self.offset - 1		)
            ]

            if len(set(lowerCamelCase__		)		) != len(lowerCamelCase__		):
                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 :       int          =   additional_special_tokens_extended
        else:
            A :       Optional[int]          =   [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 :       Any          =   {} if sp_model_kwargs is None else sp_model_kwargs

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

        # 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 			_lowerCAmelCase					(       self		):
        return len(self.sp_model		) + self.offset






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def __getstate__(       self		):
        A :       List[Any]          =   self.__dict__.copy()
        A :       Union[str, Any]          =   None
        return state






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

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

        A :       Tuple          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if token in self.decoder:
            return self.decoder[token]
        elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
        A :       List[str]          =   self.sp_model.piece_to_id(lowerCamelCase__		)
        return sp_id + self.offset






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if index in self.encoder:
            return self.encoder[index]
        elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
        else:
            A :       Dict          =   self.sp_model.IdToPiece(index - self.offset		)
        return token






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        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(lowerCamelCase__		) + token
                A :       int          =   []
            else:
                current_sub_tokens.append(lowerCamelCase__		)
        out_string += self.sp_model.decode(lowerCamelCase__		)
        return out_string.strip()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=False		):
        return 1






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):
        if already_has_special_tokens:
            return self._special_token_mask(lowerCamelCase__		)
        elif token_ids_a is None:
            return self._special_token_mask(lowerCamelCase__		) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A :       Any          =   os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

        return (out_vocab_file,)


