


'''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 _lowerCamelCase			(		_lowercase						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     Tuple        =							MvpTokenizer
		A_      :     List[Any]        =							MvpTokenizerFast
		A_      :     str        =							True
		A_      :     List[str]        =							filter_roberta_detectors







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

			__magic_name__			:       Any	      =	os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES['vocab_file']     )
			__magic_name__			:       Dict	      =	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(_A     ) + '\n'     )
			with open(self.merges_file    ,	'w'    ,	encoding='utf-8'     ) as fp:
				fp.write('\n'.join(_A     )     )







		def   __lowerCAmelCase						(   self						:		Optional[int]    ,	**_A						:		Optional[Any]     )    ->					Optional[int]:
			kwargs.update(self.special_tokens_map     )
			return self.tokenizer_class.from_pretrained(self.tmpdirname    ,	**_A     )







		def   __lowerCAmelCase						(   self						:		Optional[Any]    ,	**_A						:		List[Any]     )    ->					Tuple:
			kwargs.update(self.special_tokens_map     )
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,	**_A     )







		def   __lowerCAmelCase						(   self						:		Optional[Any]    ,	_A						:		Any     )    ->					List[str]:
			return "lower newer", "lower newer"







		@cached_property
		def   __lowerCAmelCase						(   self						:		int     )    ->					Union[str, Any]:
			return MvpTokenizer.from_pretrained('RUCAIBox/mvp'     )







		@cached_property
		def   __lowerCAmelCase						(   self						:		Optional[Any]     )    ->					Optional[Any]:
			return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'     )







		@require_torch
		def   __lowerCAmelCase						(   self						:		str     )    ->					Dict:
			__magic_name__			:       str	      =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
			__magic_name__			:       str	      =	[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__magic_name__			:       Any	      =	tokenizer(_A    ,	max_length=len(_A     )    ,	padding=_A    ,	return_tensors='pt'     )
				self.assertIsInstance(_A    ,	_A     )

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







		@require_torch
		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					Optional[int]:
			__magic_name__			:       str	      =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__magic_name__			:       Dict	      =	tokenizer(_A    ,	padding=_A    ,	return_tensors='pt'     )
				# check if input_ids are returned and no labels
				self.assertIn('input_ids'    ,	_A     )
				self.assertIn('attention_mask'    ,	_A     )
				self.assertNotIn('labels'    ,	_A     )
				self.assertNotIn('decoder_attention_mask'    ,	_A     )







		@require_torch
		def   __lowerCAmelCase						(   self						:		int     )    ->					int:
			__magic_name__			:       str	      =	[
			    'Summary of the text.',
			    'Another summary.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__magic_name__			:       Optional[int]	      =	tokenizer(text_target=_A    ,	max_length=32    ,	padding='max_length'    ,	return_tensors='pt'     )
				self.assertEqual(32    ,	targets['input_ids'].shape[1]     )







		@require_torch
		def   __lowerCAmelCase						(   self						:		str     )    ->					Union[str, Any]:
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__magic_name__			:       str	      =	tokenizer(
				    ['I am a small frog' * 1024, 'I am a small frog']    ,	padding=_A    ,	truncation=_A    ,	return_tensors='pt'     )
				self.assertIsInstance(_A    ,	_A     )
				self.assertEqual(batch.input_ids.shape    ,	(2, 1024)     )







		@require_torch
		def   __lowerCAmelCase						(   self						:		int     )    ->					int:
			__magic_name__			:       Optional[Any]	      =	['A long paragraph for summarization.']
			__magic_name__			:       str	      =	[
			    'Summary of the text.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				__magic_name__			:       int	      =	tokenizer(_A    ,	text_target=_A    ,	return_tensors='pt'     )
				__magic_name__			:       Optional[Any]	      =	inputs['input_ids']
				__magic_name__			:       Tuple	      =	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   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			pass







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

					# 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']     )    ,	)

					__magic_name__			:       Union[str, Any]	      =	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
					__magic_name__			:       List[Any]	      =	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, 50264, 3823, 487, 21992, 3645, 4, 2]     )
					self.assertSequenceEqual(tokens_r['input_ids']    ,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )

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




import math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,  int(math.sqrt(__UpperCAmelCase       ) + 1       )							,  6       ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


if __name__ == "__main__":
     print(f'{solution() = }')





