




'''simple docstring'''




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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     UpperCamelCase_     (a__,      unittest.TestCase     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					BartTokenizer
						_lowerCAmelCase    						=					BartTokenizerFast
						_lowerCAmelCase    						=					True
						_lowerCAmelCase    						=					filter_roberta_detectors







						def       _a    (     self      :       List[Any]    ):





											"""simple docstring"""







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

											A_ :     Optional[int] =							os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES['''vocab_file''']    )
											A_ :     Optional[Any] =							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       _a    (     self      :       List[str]							, **_lowerCamelCase      :       List[Any]    ):





											"""simple docstring"""







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







						def       _a    (     self      :       Dict							, **_lowerCamelCase      :       List[Any]    ):





											"""simple docstring"""







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







						def       _a    (     self      :       List[str]							, _lowerCamelCase      :       Optional[Any]    ):





											"""simple docstring"""







											return "lower newer", "lower newer"







						@cached_property
						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											return BartTokenizer.from_pretrained('''facebook/bart-large'''    )







						@cached_property
						def       _a    (     self      :       Union[str, Any]    ):





											"""simple docstring"""







											return BartTokenizerFast.from_pretrained('''facebook/bart-large'''    )







						@require_torch
						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											A_ :     int =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											A_ :     List[Any] =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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







						@require_torch
						def       _a    (     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     Any =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																A_ :     int =							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       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							[
											    '''Summary of the text.''',
											    '''Another summary.''',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																A_ :     Union[str, 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       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																A_ :     Optional[int] =							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       _a    (     self      :       Dict    ):





											"""simple docstring"""







											A_ :     Dict =							['''A long paragraph for summarization.''']
											A_ :     List[str] =							[
											    '''Summary of the text.''',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																A_ :     Tuple =							tokenizer(_lowerCamelCase							, return_tensors='''pt'''    )
																A_ :     Dict =							tokenizer(text_target=_lowerCamelCase							, return_tensors='''pt'''    )
																A_ :     Tuple =							inputs['''input_ids''']
																A_ :     Any =							targets['''input_ids''']
																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       _a    (     self      :       Dict    ):





											"""simple docstring"""







											pass







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







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

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









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   int			)   ->   bool:
					if not isinstance(lowerCamelCase__			,		lowerCamelCase__			):
										A_ :     int =							f'Input value of [number={number}] must be an integer'
										raise TypeError(lowerCamelCase__			)
					if number < 0 or number % 2 == 1:
										return False
					A_ :     Dict =							int(number**0.5			)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



