





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						:							List[str]   		=		MvpTokenizerFast
   a						:							Dict   		=		True
   a						:							Any   		=		filter_roberta_detectors




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






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

       UpperCamelCase__							:				Optional[int]          =      os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES['''vocab_file''']							)
       UpperCamelCase__							:				Union[str, 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(__magic_name__							) + '''\n'''							)
       with open(self.merges_file,		'''w''',		encoding='''utf-8'''							) as fp:
           fp.write('''\n'''.join(__magic_name__							)							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      List[str]:






       """simple docstring"""
       kwargs.update(self.special_tokens_map							)
       return self.tokenizer_class.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Any:






       """simple docstring"""
       kwargs.update(self.special_tokens_map							)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       return "lower newer", "lower newer"




   @cached_property
   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''							)




   @cached_property
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''							)




   @require_torch
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      ['''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__							:				str          =      tokenizer(__magic_name__,		max_length=len(__magic_name__							),		padding=__magic_name__,		return_tensors='''pt'''							)
           self.assertIsInstance(__magic_name__,		__magic_name__							)

           self.assertEqual((2, 9),		batch.input_ids.shape							)
           self.assertEqual((2, 9),		batch.attention_mask.shape							)
           UpperCamelCase__							:				Optional[int]          =      batch.input_ids.tolist()[0]
           self.assertListEqual(__magic_name__,		__magic_name__							)
           # Test that special tokens are reset




   @require_torch
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           UpperCamelCase__							:				Union[str, Any]          =      tokenizer(__magic_name__,		padding=__magic_name__,		return_tensors='''pt'''							)
           # check if input_ids are returned and no labels
           self.assertIn('''input_ids''',		__magic_name__							)
           self.assertIn('''attention_mask''',		__magic_name__							)
           self.assertNotIn('''labels''',		__magic_name__							)
           self.assertNotIn('''decoder_attention_mask''',		__magic_name__							)




   @require_torch
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           UpperCamelCase__							:				Optional[int]          =      tokenizer(text_target=__magic_name__,		max_length=32,		padding='''max_length''',		return_tensors='''pt'''							)
           self.assertEqual(32,		targets['''input_ids'''].shape[1]							)




   @require_torch
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           UpperCamelCase__							:				Optional[int]          =      tokenizer(
               ['''I am a small frog''' * 1024, '''I am a small frog'''],		padding=__magic_name__,		truncation=__magic_name__,		return_tensors='''pt'''							)
           self.assertIsInstance(__magic_name__,		__magic_name__							)
           self.assertEqual(batch.input_ids.shape,		(2, 1024)							)




   @require_torch
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






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






       """simple docstring"""
       pass




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






       """simple docstring"""
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"							):
               UpperCamelCase__							:				List[str]          =      self.rust_tokenizer_class.from_pretrained(__magic_name__,		**__magic_name__							)
               UpperCamelCase__							:				Any          =      self.tokenizer_class.from_pretrained(__magic_name__,		**__magic_name__							)
               UpperCamelCase__							:				str          =      '''A, <mask> AllenNLP sentence.'''
               UpperCamelCase__							:				Tuple          =      tokenizer_r.encode_plus(__magic_name__,		add_special_tokens=__magic_name__,		return_token_type_ids=__magic_name__							)
               UpperCamelCase__							:				Tuple          =      tokenizer_p.encode_plus(__magic_name__,		add_special_tokens=__magic_name__,		return_token_type_ids=__magic_name__							)

               # 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__							:				Optional[Any]          =      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
               UpperCamelCase__							:				Union[str, 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(
                   __magic_name__,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
               self.assertSequenceEqual(
                   __magic_name__,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


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

