



"""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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[Any]						=						MvpTokenizer
   __lowerCAmelCase  :   Any						=						MvpTokenizerFast
   __lowerCAmelCase  :   Union[str, Any]						=						True
   __lowerCAmelCase  :   Optional[int]						=						filter_roberta_detectors

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




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

    UpperCAmelCase	:					int	=	os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    UpperCAmelCase	:					str	=	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(_SCREAMING_SNAKE_CASE						) + """\n"""						)
    with open(self.merges_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    return "lower newer", "lower newer"

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    UpperCAmelCase	:					Optional[int]	=	[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					Dict	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     max_length=len(_SCREAMING_SNAKE_CASE						)      ,     padding=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

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

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					Optional[Any]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     # check if input_ids are returned and no labels
     self.assertIn("""input_ids"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertIn("""attention_mask"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertNotIn("""labels"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertNotIn("""decoder_attention_mask"""      ,     _SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	[
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					str	=	tokenizer(text_target=_SCREAMING_SNAKE_CASE      ,     max_length=32      ,     padding="""max_length"""      ,     return_tensors="""pt"""						)
     self.assertEqual(32      ,     targets["""input_ids"""].shape[1]						)

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





    '''simple docstring'''




    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					List[Any]	=	tokenizer(
         ["""I am a small frog""" * 1024, """I am a small frog"""]      ,     padding=_SCREAMING_SNAKE_CASE      ,     truncation=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     self.assertEqual(batch.input_ids.shape      ,     (2, 1024)						)

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	["""A long paragraph for summarization."""]
    UpperCAmelCase	:					Any	=	[
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					List[str]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     text_target=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     UpperCAmelCase	:					Optional[Any]	=	inputs["""input_ids"""]
     UpperCAmelCase	:					Optional[Any]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    pass




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    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(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Union[str, Any]	=	self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Union[str, Any]	=	"""A, <mask> AllenNLP sentence."""
      UpperCAmelCase	:					str	=	tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_token_type_ids=_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					str	=	tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_token_type_ids=_SCREAMING_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	:					Any	=	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
      UpperCAmelCase	:					Dict	=	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(
          _SCREAMING_SNAKE_CASE      ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
      self.assertSequenceEqual(
          _SCREAMING_SNAKE_CASE      ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)








"""simple docstring"""






from math import factorial



def 					_A			(__a = 20							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		n // 2

    return int(factorial(__a							) / (factorial(__a							) * factorial(n - k							))							)


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution(20))
  else:
    try:
      UpperCAmelCase_   : List[str]   			= int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number.""")


