





'''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  ):







	'''simple docstring'''

	a      :			str     =    MvpTokenizer
	a      :			Tuple     =    MvpTokenizerFast
	a      :			Dict     =    True
	a      :			Dict     =    filter_roberta_detectors


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				super().setUp()
				__lowercase			  =							[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				__lowercase			  =							dict(zip(_lowerCamelCase       ,range(len(_lowerCamelCase	)	)	)	)
				__lowercase			  =							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				__lowercase			  =							{'''unk_token''': '''<unk>'''}

				__lowercase			  =							os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file''']	)
				__lowercase			  =							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       ,**_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				kwargs.update(self.special_tokens_map	)
				return self.tokenizer_class.from_pretrained(self.tmpdirname       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,**_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				kwargs.update(self.special_tokens_map	)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				return "lower newer", "lower newer"


	@cached_property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''	)


	@cached_property
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				__lowercase			  =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowercase			  =							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	)
							__lowercase			  =							batch.input_ids.tolist()[0]
							self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)
							# Test that special tokens are reset


	@require_torch
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowercase			  =							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	)							->							int:







				'''simple docstring'''





				__lowercase			  =							[
				    '''Summary of the text.''',
				    '''Another summary.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowercase			  =							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	)							->							str:







				'''simple docstring'''





				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowercase			  =							tokenizer(
							    ['''I am a small frog''' * 1024, '''I am a small frog''']       ,padding=_lowerCamelCase       ,truncation=_lowerCamelCase       ,return_tensors='''pt'''	)
							self.assertIsInstance(_lowerCamelCase       ,_lowerCamelCase	)
							self.assertEqual(batch.input_ids.shape       ,(2, 1024)	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							['''A long paragraph for summarization.''']
				__lowercase			  =							[
				    '''Summary of the text.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowercase			  =							tokenizer(_lowerCamelCase       ,text_target=_lowerCamelCase       ,return_tensors='''pt'''	)
							__lowercase			  =							inputs['''input_ids''']
							__lowercase			  =							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	)							->							List[str]:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
										__lowercase			  =							self.rust_tokenizer_class.from_pretrained(_lowerCamelCase       ,**_lowerCamelCase	)
										__lowercase			  =							self.tokenizer_class.from_pretrained(_lowerCamelCase       ,**_lowerCamelCase	)
										__lowercase			  =							'''A, <mask> AllenNLP sentence.'''
										__lowercase			  =							tokenizer_r.encode_plus(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase       ,return_token_type_ids=_lowerCamelCase	)
										__lowercase			  =							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''']	)       ,)

										__lowercase			  =							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']	)
										__lowercase			  =							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(
										    _lowerCamelCase       ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']	)
										self.assertSequenceEqual(
										    _lowerCamelCase       ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']	)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 6_0_0_8_5_1_4_7_5_1_4_3			):

			try:
						__lowercase			  =							int(lowerCamelCase_			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						while n % i == 0:
									__lowercase			  =							i
									n //= i
						i += 1
			if n > 1:
						__lowercase			  =							n
			return int(lowerCamelCase_			)


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





