




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 		_A   (    __snake_case     ,  unittest.TestCase ):
  _UpperCamelCase  :     Any					=     MvpTokenizer
  _UpperCamelCase  :     Union[str, Any]					=     MvpTokenizerFast
  _UpperCamelCase  :     Union[str, Any]					=     True
  _UpperCamelCase  :     Any					=     filter_roberta_detectors






  def    __a     (    self						:      Union[str, Any] )						->       int:






       """simple docstring"""







       super().setUp()
       lowercase   :  int        =					[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       lowercase   :  List[Any]        =					dict(zip(_A ,		range(len(_A ) ) ) )
       lowercase   :  int        =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       lowercase   :  Tuple        =					{"unk_token": "<unk>"}

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






  def    __a     (    self						:      List[str] ,		**_A						:      List[str] )						->       List[str]:






       """simple docstring"""







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






  def    __a     (    self						:      Tuple ,		**_A						:      str )						->       Any:






       """simple docstring"""







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






  def    __a     (    self						:      str ,		_A						:      int )						->       Optional[Any]:






       """simple docstring"""







       return "lower newer", "lower newer"






  @cached_property
  def    __a     (    self						:      Optional[Any] )						->       Dict:






       """simple docstring"""







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






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






       """simple docstring"""







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






  @require_torch
  def    __a     (    self						:      str )						->       Tuple:






       """simple docstring"""







       lowercase   :  Optional[int]        =					["A long paragraph for summarization.", "Another paragraph for summarization."]
       lowercase   :  str        =					[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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






  @require_torch
  def    __a     (    self						:      Tuple )						->       Any:






       """simple docstring"""







       lowercase   :  List[str]        =					["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            lowercase   :  str        =					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    __a     (    self						:      Optional[Any] )						->       int:






       """simple docstring"""







       lowercase   :  List[str]        =					[
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            lowercase   :  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    __a     (    self						:      Any )						->       str:






       """simple docstring"""







       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            lowercase   :  List[str]        =					tokenizer(
                ['''I am a small frog''' * 1_024, '''I am a small frog'''] ,		padding=_A ,		truncation=_A ,		return_tensors='''pt''' )
            self.assertIsInstance(_A ,		_A )
            self.assertEqual(batch.input_ids.shape ,		(2, 1_024) )






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






       """simple docstring"""







       lowercase   :  Optional[int]        =					["A long paragraph for summarization."]
       lowercase   :  Any        =					[
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            lowercase   :  Dict        =					tokenizer(_A ,		text_target=_A ,		return_tensors='''pt''' )
            lowercase   :  Dict        =					inputs["input_ids"]
            lowercase   :  int        =					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    __a     (    self						:      Optional[int] )						->       Dict:






       """simple docstring"""







       pass






  def    __a     (    self						:      Dict )						->       Tuple:






       """simple docstring"""







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

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

def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			bool:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [int(_UpperCAmelCase  ) for i in ip_va_address.split("."  ) if i.isdigit()]
						return len(_UpperCAmelCase  ) == 4 and all(0 <= int(_UpperCAmelCase  ) <= 254 for octet in octets  )


if __name__ == "__main__":
							UpperCamelCase_						:     Dict	       =							input().strip()
							UpperCamelCase_						:     List[Any]	       =							"""valid""" if is_ip_va_address_valid(ip) else """invalid"""
							print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')




