"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a  :  List[Any]=				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Optional[int]=				[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""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[int]=				get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[str]					=	XLMProphetNetTokenizer
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   List[Any]					=	True



				def       _lowercase  (self       :   Dict)					->			Dict:
					super().setUp()

					# We have a SentencePiece fixture for testing
					__snake_case    :							Tuple   	=				XLMProphetNetTokenizer(_A			,						keep_accents=_A)
					tokenizer.save_pretrained(self.tmpdirname)



				def       _lowercase  (self       :   str)					->			Optional[int]:
					__snake_case    :							List[str]   	=				'[PAD]'
					__snake_case    :							str   	=				0

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



				def       _lowercase  (self       :   Dict)					->			str:
					__snake_case    :							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(_A)			,						10_12)



				def       _lowercase  (self       :   Dict)					->			List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size			,						10_12)



				def       _lowercase  (self       :   str)					->			Dict:
					__snake_case    :							int   	=				XLMProphetNetTokenizer(_A			,						keep_accents=_A)

					__snake_case    :							Tuple   	=				tokenizer.tokenize('This is a test')
					self.assertListEqual(_A			,						['▁This', '▁is', '▁a', '▁t', 'est'])

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

					__snake_case    :							int   	=				tokenizer.tokenize('I was born in 92000, and this is falsé.')
					self.assertListEqual(
					    _A			,						[
					        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',
					        '.',
					    ]			,						)
					__snake_case    :							Tuple   	=				tokenizer.convert_tokens_to_ids(_A)
					self.assertListEqual(
					    _A			,						[
					        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]
					    ]			,						)

					__snake_case    :							Optional[Any]   	=				tokenizer.convert_ids_to_tokens(_A)
					self.assertListEqual(
					    _A			,						[
					        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       _lowercase  (self       :   List[Any])					->			Tuple:
					return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')



				@slow
				def       _lowercase  (self       :   Tuple)					->			Any:
					__snake_case    :							Dict   	=				'Hello World!'
					__snake_case    :							List[str]   	=				[3_53_89, 66_72, 49, 2]
					self.assertListEqual(_A			,						self.big_tokenizer.encode(_A))






				@slow
				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					# fmt: off
					__snake_case    :							Any   	=				{'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=_A			,						model_name='microsoft/xprophetnet-large-wiki100-cased'			,						revision='1acad1643ddd54a44df6a1b797ada8373685d90e'			,						)





