

'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class lowerCamelCase      (						__UpperCAmelCase							,							unittest.TestCase							):
				snake_case_           =   MPNetTokenizer
				snake_case_           =   MPNetTokenizerFast
				snake_case_           =   True
				snake_case_           =   True

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					super().setUp()

					snake_case							 =				[
					    '[UNK]',
					    '[CLS]',
					    '[SEP]',
					    '[PAD]',
					    '[MASK]',
					    'want',
					    '##want',
					    '##ed',
					    'wa',
					    'un',
					    'runn',
					    '##ing',
					    ',',
					    'low',
					    'lowest',
					]
					snake_case							 =				os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['vocab_file']   )
					with open(self.vocab_file,	'w',	encoding='utf-8'   ) as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       int:
					snake_case							 =				'UNwant\u00E9d,running'
					snake_case							 =				'unwanted, running'
					return input_text, output_text

				def 				_lowerCamelCase						(					self   )						->       Any:
					snake_case							 =				self.tokenizer_class(self.vocab_file   )

					snake_case							 =				tokenizer.tokenize('UNwant\u00E9d,running'   )
					self.assertListEqual(lowerCamelCase__,	['un', '##want', '##ed', ',', 'runn', '##ing']   )
					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__   ),	[9, 6, 7, 12, 10, 11]   )






				@slow
				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					snake_case							 =				self.tokenizer_class.from_pretrained('microsoft/mpnet-base'   )

					snake_case							 =				tokenizer.encode('sequence builders',	add_special_tokens=lowerCamelCase__   )
					snake_case							 =				tokenizer.encode('multi-sequence build',	add_special_tokens=lowerCamelCase__   )

					snake_case							 =				tokenizer.build_inputs_with_special_tokens(lowerCamelCase__   )
					snake_case							 =				tokenizer.build_inputs_with_special_tokens(lowerCamelCase__,	lowerCamelCase__   )

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						lowerCAmelCase_						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





