






"""simple docstring"""







import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase            =	logging.get_logger(__name__)


_lowerCAmelCase            =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'allenai/led-base-16384': 1_63_84,
}


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : int												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : str												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Any												=		LEDTokenizer
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :str			, __magic_name__  :str=None			, __magic_name__  :str=None			, __magic_name__  :List[str]=None			, __magic_name__  :List[Any]="replace"			, __magic_name__  :str="<s>"			, __magic_name__  :str="</s>"			, __magic_name__  :int="</s>"			, __magic_name__  :str="<s>"			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[str]="<pad>"			, __magic_name__  :Optional[Any]="<mask>"			, __magic_name__  :Union[str, Any]=False			, __magic_name__  :Optional[Any]=True			, **__magic_name__  :Optional[int]			, )      ->List[Any]:
										super().__init__(
										    __magic_name__			, __magic_name__			, tokenizer_file=__magic_name__			, errors=__magic_name__			, bos_token=__magic_name__			, eos_token=__magic_name__			, sep_token=__magic_name__			, cls_token=__magic_name__			, unk_token=__magic_name__			, pad_token=__magic_name__			, mask_token=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__			, **__magic_name__			, )

										lowercase    :      int       = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get("""add_prefix_space"""			, __magic_name__				) != add_prefix_space:
														lowercase    :      Union[str, Any]       = getattr(__magic_name__			, pre_tok_state.pop("""type"""				)				)
														lowercase    :      int       = add_prefix_space
														lowercase    :      Optional[int]       = pre_tok_class(**__magic_name__				)

										lowercase    :      Dict       = add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										lowercase    :      Optional[Any]       = """post_processor"""
										lowercase    :      Union[str, Any]       = getattr(self.backend_tokenizer			, __magic_name__			, __magic_name__				)
										if tokenizer_component_instance:
														lowercase    :      str       = json.loads(tokenizer_component_instance.__getstate__()				)

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowercase    :      Optional[int]       = tuple(state["""sep"""]				)
														if "cls" in state:
																		lowercase    :      str       = tuple(state["""cls"""]				)

														lowercase    :      Union[str, Any]       = False

														if state.get("""add_prefix_space"""			, __magic_name__				) != add_prefix_space:
																		lowercase    :      Dict       = add_prefix_space
																		lowercase    :      int       = True

														if state.get("""trim_offsets"""			, __magic_name__				) != trim_offsets:
																		lowercase    :      Optional[Any]       = trim_offsets
																		lowercase    :      Tuple       = True

														if changes_to_apply:
																		lowercase    :      Tuple       = getattr(__magic_name__			, state.pop("""type"""				)				)
																		lowercase    :      Optional[Any]       = component_class(**__magic_name__				)
																		setattr(self.backend_tokenizer			, __magic_name__			, __magic_name__				)






						@property
						# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
						def      __snake_case      (		self  :List[str]				)      ->str:
										if self._mask_token is None:
														if self.verbose:
																		logger.error("""Using mask_token, but it is not set yet."""				)
														return None
										return str(self._mask_token				)






						@mask_token.setter
						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :int				)      ->Dict:
										lowercase    :      Optional[int]       = AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				) if isinstance(__magic_name__			, __magic_name__				) else value
										lowercase    :      int       = value






						def      __snake_case      (		self  :str			, *__magic_name__  :List[Any]			, **__magic_name__  :int				)      ->BatchEncoding:
										lowercase    :      Dict       = kwargs.get("""is_split_into_words"""			, __magic_name__				)

										if is_split_into_words and not self.add_prefix_space:
														raise ValueError(
														    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    """to use it with pretokenized inputs."""				)

										return super()._batch_encode_plus(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :int			, *__magic_name__  :Optional[Any]			, **__magic_name__  :List[str]				)      ->BatchEncoding:
										lowercase    :      int       = kwargs.get("""is_split_into_words"""			, __magic_name__				)

										if is_split_into_words and not self.add_prefix_space:
														raise ValueError(
														    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    """to use it with pretokenized inputs."""				)

										return super()._encode_plus(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Dict			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										lowercase    :      Union[str, Any]       = self._tokenizer.model.save(__magic_name__			, name=__magic_name__				)
										return tuple(__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Tuple			, __magic_name__  :Union[str, Any]=None				)      ->List[str]:
										lowercase    :      int       = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Dict       = [self.sep_token_id]
										lowercase    :      List[str]       = [self.cls_token_id]

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






						def      __snake_case      (		self  :Any			, __magic_name__  :Union[Dict[str, EncodedInput], BatchEncoding]			, __magic_name__  :Optional[int] = None			, __magic_name__  :PaddingStrategy = PaddingStrategy.DO_NOT_PAD			, __magic_name__  :Optional[int] = None			, __magic_name__  :Optional[bool] = None			, )      ->dict:
										lowercase    :      int       = super()._pad(
										    encoded_inputs=__magic_name__			, max_length=__magic_name__			, padding_strategy=__magic_name__			, pad_to_multiple_of=__magic_name__			, return_attention_mask=__magic_name__			, )

										# Load from model defaults
										if return_attention_mask is None:
														lowercase    :      int       = """attention_mask""" in self.model_input_names

										if return_attention_mask and "global_attention_mask" in encoded_inputs:
														lowercase    :      List[Any]       = encoded_inputs[self.model_input_names[0]]
														# `global_attention_mask` need to have the same length as other (sequential) inputs.
														lowercase    :      str       = len(encoded_inputs["""global_attention_mask"""]				) != len(__magic_name__				)

														if needs_to_be_padded:
																		lowercase    :      Dict       = len(__magic_name__				) - len(encoded_inputs["""global_attention_mask"""]				)

																		if self.padding_side == "right":
																						# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																						lowercase    :      List[Any]       = (
																						    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																						)
																		elif self.padding_side == "left":
																						lowercase    :      str       = [-1] * difference + encoded_inputs[
																						    """global_attention_mask"""
																						]
																		else:
																						raise ValueError("""Invalid padding strategy:""" + str(self.padding_side				)				)

										return encoded_inputs








"""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


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


@require_sentencepiece
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		XLMProphetNetTokenizer
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		True






						def      __snake_case      (		self  :Optional[int]				)      ->List[str]:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase    :      List[str]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)
										tokenizer.save_pretrained(self.tmpdirname				)






						def      __snake_case      (		self  :int				)      ->Any:
										lowercase    :      Dict       = """[PAD]"""
										lowercase    :      Tuple       = 0

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






						def      __snake_case      (		self  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = 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(__magic_name__				)			, 1_012				)






						def      __snake_case      (		self  :int				)      ->int:
										self.assertEqual(self.get_tokenizer().vocab_size			, 1_012				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      Optional[Any]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.tokenize("""This is a test"""				)
										self.assertListEqual(__magic_name__			, ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__magic_name__				)			, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			, )

										lowercase    :      Optional[Any]       = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
										self.assertListEqual(
										    __magic_name__			, [
										        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""",
										        """.""",
										    ]			, )
										lowercase    :      Optional[int]       = tokenizer.convert_tokens_to_ids(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
										    ]			, )

										lowercase    :      Union[str, Any]       = tokenizer.convert_ids_to_tokens(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        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  :List[Any]				)      ->str:
										return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""				)






						@slow
						def      __snake_case      (		self  :int				)      ->Optional[Any]:
										lowercase    :      str       = """Hello World!"""
										lowercase    :      Optional[Any]       = [35_389, 6_672, 49, 2]
										self.assertListEqual(__magic_name__			, self.big_tokenizer.encode(__magic_name__				)				)






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										# fmt: off
										lowercase    :      Optional[int]       = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 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], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 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=__magic_name__			, model_name="""microsoft/xprophetnet-large-wiki100-cased"""			, revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""			, )
