



'''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     SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase      ):







	"""simple docstring"""






	lowerCamelCase__      			=	BartTokenizer
	lowerCamelCase__      			=	BartTokenizerFast
	lowerCamelCase__      			=	True
	lowerCamelCase__      			=	filter_roberta_detectors







	def 					A     (						self      :					int		)      ->		Union[str, 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			:	Dict										=		dict(zip(__snake_case		,      range(len(__snake_case		)		)		)		)
								UpperCAmelCase			:	int										=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								UpperCAmelCase			:	List[str]										=		{'''unk_token''': '''<unk>'''}

								UpperCAmelCase			:	str										=		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(__snake_case		) + '''\n'''		)
								with open(self.merges_file		,      '''w'''		,      encoding='''utf-8'''		) as fp:
															fp.write('''\n'''.join(__snake_case		)		)







	def 					A     (						self      :					str		,      **__snake_case      :					Optional[Any]		)      ->		Union[str, Any]:
								kwargs.update(self.special_tokens_map		)
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,      **__snake_case		)







	def 					A     (						self      :					Tuple		,      **__snake_case      :					List[str]		)      ->		List[Any]:
								kwargs.update(self.special_tokens_map		)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,      **__snake_case		)







	def 					A     (						self      :					Any		,      __snake_case      :					int		)      ->		Dict:
								return "lower newer", "lower newer"







	@cached_property
	def 					A     (						self      :					str		)      ->		Union[str, Any]:
								return BartTokenizer.from_pretrained('''facebook/bart-large'''		)







	@cached_property
	def 					A     (						self      :					List[Any]		)      ->		Tuple:
								return BartTokenizerFast.from_pretrained('''facebook/bart-large'''		)







	@require_torch
	def 					A     (						self      :					Tuple		)      ->		List[Any]:
								UpperCAmelCase			:	List[Any]										=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								UpperCAmelCase			:	Union[str, Any]										=		[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															UpperCAmelCase			:	Tuple										=		tokenizer(__snake_case		,      max_length=len(__snake_case		)		,      padding=__snake_case		,      return_tensors='''pt'''		)
															self.assertIsInstance(__snake_case		,      __snake_case		)

															self.assertEqual((2, 9)		,      batch.input_ids.shape		)
															self.assertEqual((2, 9)		,      batch.attention_mask.shape		)
															UpperCAmelCase			:	Tuple										=		batch.input_ids.tolist()[0]
															self.assertListEqual(__snake_case		,      __snake_case		)
															# Test that special tokens are reset







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







	@require_torch
	def 					A     (						self      :					Optional[Any]		)      ->		Tuple:
								UpperCAmelCase			:	Tuple										=		[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															UpperCAmelCase			:	Union[str, Any]										=		tokenizer(text_target=__snake_case		,      max_length=32		,      padding='''max_length'''		,      return_tensors='''pt'''		)
															self.assertEqual(32		,      targets['''input_ids'''].shape[1]		)







	@require_torch
	def 					A     (						self      :					Dict		)      ->		List[Any]:
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															UpperCAmelCase			:	Any										=		tokenizer(
															    ['''I am a small frog''' * 1024, '''I am a small frog''']		,      padding=__snake_case		,      truncation=__snake_case		,      return_tensors='''pt'''		)
															self.assertIsInstance(__snake_case		,      __snake_case		)
															self.assertEqual(batch.input_ids.shape		,      (2, 1024)		)







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







	def 					A     (						self      :					Any		)      ->		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(__snake_case		,      **__snake_case		)
																						UpperCAmelCase			:	Dict										=		self.tokenizer_class.from_pretrained(__snake_case		,      **__snake_case		)
																						UpperCAmelCase			:	Dict										=		'''A, <mask> AllenNLP sentence.'''
																						UpperCAmelCase			:	str										=		tokenizer_r.encode_plus(__snake_case		,      add_special_tokens=__snake_case		,      return_token_type_ids=__snake_case		)
																						UpperCAmelCase			:	Any										=		tokenizer_p.encode_plus(__snake_case		,      add_special_tokens=__snake_case		,      return_token_type_ids=__snake_case		)

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










'''simple docstring'''






UpperCamelCase__:       Any							     =      [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[str]		,      __snake_case      :					int		)      ->		int:
								UpperCAmelCase			:	Any										=		name
								UpperCAmelCase			:	Optional[Any]										=		[]
								UpperCAmelCase			:	Any										=		[]







	def 					A     (						self      :					Dict		,      __snake_case      :					str		)      ->		Tuple:
								self.inbound.append(__snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					str		)      ->		Optional[int]:
								self.outbound.append(__snake_case		)







	def __repr__(						self      :					Optional[int]		)      ->		Optional[int]:
								return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Dict=3       ,				_lowerCAmelCase  :						Tuple=0.8_5  )				->       Optional[Any]:
							UpperCAmelCase			:	Tuple										=		{}
							for node in nodes:
														UpperCAmelCase			:	List[str]										=		1

							UpperCAmelCase			:	List[Any]										=		{}
							for node in nodes:
														UpperCAmelCase			:	str										=		len(node.outbound  )

							for i in range(_lowerCAmelCase  ):
														print(f"""======= Iteration {i + 1} ======="""  )
														for _, node in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase			:	str										=		(1 - d) + d * sum(
																					    ranks[ib] / outbounds[ib] for ib in node.inbound  )
														print(_lowerCAmelCase  )


def 			snake_case_					( )				->       List[str]:
							UpperCAmelCase			:	str										=		list(input('''Enter Names of the Nodes: '''  ).split()  )

							UpperCAmelCase			:	Dict										=		[Node(_lowerCAmelCase  ) for name in names]

							for ri, row in enumerate(_lowerCAmelCase  ):
														for ci, col in enumerate(_lowerCAmelCase  ):
																					if col == 1:
																												nodes[ci].add_inbound(names[ri]  )
																												nodes[ri].add_outbound(names[ci]  )

							print('''======= Nodes ======='''  )
							for node in nodes:
														print(_lowerCAmelCase  )

							page_rank(_lowerCAmelCase  )


if __name__ == "__main__":
						main()





