






'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class    __lowercase       (__lowerCamelCase ,					unittest.TestCase     ):
	_lowerCamelCase														=	MvpTokenizer
	_lowerCamelCase														=	MvpTokenizerFast
	_lowerCamelCase														=	True
	_lowerCamelCase														=	filter_roberta_detectors

	def   __UpperCamelCase				(   self :    Any):
			super().setUp()
			UpperCamelCase__					:       Optional[int]       =       [
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    '\u0120',
			    '\u0120l',
			    '\u0120n',
			    '\u0120lo',
			    '\u0120low',
			    'er',
			    '\u0120lowest',
			    '\u0120newer',
			    '\u0120wider',
			    '<unk>',
			]
			UpperCamelCase__					:       Union[str, Any]       =       dict(zip(lowerCamelCase__	,       range(len(lowerCamelCase__))))
			UpperCamelCase__					:       Dict       =       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
			UpperCamelCase__					:       List[str]       =       {'unk_token': '<unk>'}

			UpperCamelCase__					:       Tuple       =       os.path.join(self.tmpdirname	,       VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__					:       int       =       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   __UpperCamelCase				(   self :    List[Any]	,       **UpperCAmelCase_ :    str):
			kwargs.update(self.special_tokens_map)
			return self.tokenizer_class.from_pretrained(self.tmpdirname	,       **lowerCamelCase__)

	def   __UpperCamelCase				(   self :    Dict	,       **UpperCAmelCase_ :    Tuple):
			kwargs.update(self.special_tokens_map)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,       **lowerCamelCase__)

	def   __UpperCamelCase				(   self :    Optional[Any]	,       UpperCAmelCase_ :    Union[str, Any]):
			return "lower newer", "lower newer"

	@cached_property
	def   __UpperCamelCase				(   self :    List[str]):
			return MvpTokenizer.from_pretrained('RUCAIBox/mvp')

	@cached_property
	def   __UpperCamelCase				(   self :    Optional[int]):
			return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')

	@require_torch
	def   __UpperCamelCase				(   self :    List[str]):
			UpperCamelCase__					:       List[Any]       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			UpperCamelCase__					:       List[Any]       =       [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					UpperCamelCase__					:       int       =       tokenizer(lowerCamelCase__	,       max_length=len(lowerCamelCase__)	,       padding=lowerCamelCase__	,       return_tensors='pt')
					self.assertIsInstance(lowerCamelCase__	,       lowerCamelCase__)

					self.assertEqual((2, 9)	,       batch.input_ids.shape)
					self.assertEqual((2, 9)	,       batch.attention_mask.shape)
					UpperCamelCase__					:       List[Any]       =       batch.input_ids.tolist()[0]
					self.assertListEqual(lowerCamelCase__	,       lowerCamelCase__)
					# Test that special tokens are reset

	@require_torch
	def   __UpperCamelCase				(   self :    Union[str, Any]):
			UpperCamelCase__					:       Dict       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					UpperCamelCase__					:       List[Any]       =       tokenizer(lowerCamelCase__	,       padding=lowerCamelCase__	,       return_tensors='pt')
					# check if input_ids are returned and no labels
					self.assertIn('input_ids'	,       lowerCamelCase__)
					self.assertIn('attention_mask'	,       lowerCamelCase__)
					self.assertNotIn('labels'	,       lowerCamelCase__)
					self.assertNotIn('decoder_attention_mask'	,       lowerCamelCase__)

	@require_torch
	def   __UpperCamelCase				(   self :    Union[str, Any]):
			UpperCamelCase__					:       str       =       [
			    'Summary of the text.',
			    'Another summary.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					UpperCamelCase__					:       Any       =       tokenizer(text_target=lowerCamelCase__	,       max_length=32	,       padding='max_length'	,       return_tensors='pt')
					self.assertEqual(32	,       targets['input_ids'].shape[1])

	@require_torch
	def   __UpperCamelCase				(   self :    int):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					UpperCamelCase__					:       Dict       =       tokenizer(
					    ['I am a small frog' * 1_024, 'I am a small frog']	,       padding=lowerCamelCase__	,       truncation=lowerCamelCase__	,       return_tensors='pt')
					self.assertIsInstance(lowerCamelCase__	,       lowerCamelCase__)
					self.assertEqual(batch.input_ids.shape	,       (2, 1_024))

	@require_torch
	def   __UpperCamelCase				(   self :    Tuple):
			UpperCamelCase__					:       Tuple       =       ['A long paragraph for summarization.']
			UpperCamelCase__					:       Dict       =       [
			    'Summary of the text.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					UpperCamelCase__					:       Optional[int]       =       tokenizer(lowerCamelCase__	,       text_target=lowerCamelCase__	,       return_tensors='pt')
					UpperCamelCase__					:       List[Any]       =       inputs['input_ids']
					UpperCamelCase__					:       str       =       inputs['labels']
					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())

	def   __UpperCamelCase				(   self :    Tuple):
			pass





	def   __UpperCamelCase				(   self :    Dict):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
							UpperCamelCase__					:       Any       =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,       **lowerCamelCase__)
							UpperCamelCase__					:       Dict       =       self.tokenizer_class.from_pretrained(lowerCamelCase__	,       **lowerCamelCase__)
							UpperCamelCase__					:       Optional[Any]       =       'A, <mask> AllenNLP sentence.'
							UpperCamelCase__					:       Optional[Any]       =       tokenizer_r.encode_plus(lowerCamelCase__	,       add_special_tokens=lowerCamelCase__	,       return_token_type_ids=lowerCamelCase__)
							UpperCamelCase__					:       Optional[int]       =       tokenizer_p.encode_plus(lowerCamelCase__	,       add_special_tokens=lowerCamelCase__	,       return_token_type_ids=lowerCamelCase__)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])	,       sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])	,       sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])	,       )

							UpperCamelCase__					:       Any       =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							UpperCamelCase__					:       int       =       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']	,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']	,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

							self.assertSequenceEqual(
							    lowerCamelCase__	,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    lowerCamelCase__	,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])










def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for i in range(1					,	1001 ):
								total += i**i
				return str(UpperCamelCase__ )[-10:]


if __name__ == "__main__":
					print(solution())


