






import decimal





def    _lowerCamelCase	(	)   -> int:

     '''simple docstring'''
     UpperCAmelCase__			:     List[str]										= 0
     UpperCAmelCase__			:     Tuple										= decimal.Context(prec=105					)
     for i in range(2   ,       100					):
          UpperCAmelCase__			:     Any										= decimal.Decimal(a_					)
          UpperCAmelCase__			:     Optional[Any]										= number.sqrt(a_					)
          if len(str(a_					)					) > 1:
               answer += int(str(a_					)[0]					)
               UpperCAmelCase__			:     Optional[int]										= str(a_					)[2:101]
               answer += sum(int(a_					) for x in sqrt_number_str					)
     return answer


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(f'''{solution() = }''')














import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 							UpperCAmelCase_     (		__lowerCamelCase       ,			unittest.TestCase		):
     __lowerCamelCase										=     PreTrainedTokenizerFast
     __lowerCamelCase										=     False
     __lowerCamelCase										=     True
     __lowerCamelCase										=     'tokenizer_file'




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Union[str, Any]										= False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          UpperCAmelCase__			:     Dict										= True

          UpperCAmelCase__			:     int										= ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
          UpperCAmelCase__			:     List[Any]										= """SaulLu/dummy-tokenizer-bytelevel-bpe"""

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          UpperCAmelCase__			:     int										= [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          UpperCAmelCase__			:     Union[str, Any]										= PreTrainedTokenizerFast.from_pretrained(model_paths[0]		)
          tokenizer.save_pretrained(self.tmpdirname		)




     def      __UpperCAmelCase     (							self		):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass




     def      __UpperCAmelCase     (							self		):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass




     def      __UpperCAmelCase     (							self		):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass




     def      __UpperCAmelCase     (							self		):
          # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
          pass




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[int]										= self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    try:
                         UpperCAmelCase__			:     str										= tempfile.mkdtemp()
                         UpperCAmelCase__			:     Any										= self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase  ,	**_lowerCAmelCase		)

                         tokenizer.save_pretrained(self.tmpdirname		)
                         super().test_training_new_tokenizer()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname		)
                         UpperCAmelCase__			:     str										= tmpdirname_orig




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Tuple										= self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    try:
                         UpperCAmelCase__			:     Optional[Any]										= tempfile.mkdtemp()
                         UpperCAmelCase__			:     Any										= self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase  ,	**_lowerCAmelCase		)

                         tokenizer.save_pretrained(self.tmpdirname		)
                         super().test_training_new_tokenizer_with_special_tokens_change()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname		)
                         UpperCAmelCase__			:     str										= tmpdirname_orig




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Tuple										= self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name		)

          UpperCAmelCase__			:     Dict										= ("""a""" for _ in range(1000		))
          UpperCAmelCase__			:     int										= tokenizer.train_new_from_iterator(text_iterator=_lowerCAmelCase  ,	length=1000  ,	vocab_size=50		)

          UpperCAmelCase__			:     Optional[Any]										= new_tokenizer.encode("""a🤗"""		)
          self.assertEqual(_lowerCAmelCase  ,	[64, 172, 253, 97, 245]		)







@require_tokenizers
class 							UpperCAmelCase_     (		unittest.TestCase		):




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     List[str]										= AutoTokenizer.from_pretrained("""bert-base-cased"""		)
          UpperCAmelCase__			:     List[Any]										= json.loads(tokenizer._tokenizer.to_str()		)
          UpperCAmelCase__			:     Optional[Any]										= len(_lowerCAmelCase		)

          with tempfile.TemporaryDirectory() as tmp_dir:
               # Hack to save this in the tokenizer_config.json
               UpperCAmelCase__			:     Tuple										= ["""tokenizer.4.0.0.json"""]
               tokenizer.save_pretrained(_lowerCAmelCase		)
               json.dump(_lowerCAmelCase  ,	open(os.path.join(_lowerCAmelCase  ,	"""tokenizer.4.0.0.json"""		)  ,	"""w"""		)		)

               # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
               UpperCAmelCase__			:     Optional[int]										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
               self.assertEqual(len(_lowerCAmelCase		)  ,	len(_lowerCAmelCase		) + 1		)
               UpperCAmelCase__			:     Any										= json.loads(new_tokenizer._tokenizer.to_str()		)
               self.assertIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)

               # Will need to be adjusted if we reach v42 and this test is still here.
               # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
               shutil.move(os.path.join(_lowerCAmelCase  ,	"""tokenizer.4.0.0.json"""		)  ,	os.path.join(_lowerCAmelCase  ,	"""tokenizer.42.0.0.json"""		)		)
               UpperCAmelCase__			:     Union[str, Any]										= ["""tokenizer.42.0.0.json"""]
               tokenizer.save_pretrained(_lowerCAmelCase		)
               UpperCAmelCase__			:     Optional[Any]										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
               self.assertEqual(len(_lowerCAmelCase		)  ,	len(_lowerCAmelCase		)		)
               UpperCAmelCase__			:     List[Any]										= json.loads(new_tokenizer._tokenizer.to_str()		)
               self.assertNotIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)




     def      __UpperCAmelCase     (							self		):
          # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
          UpperCAmelCase__			:     List[str]										= """hf-internal-testing/test-two-tokenizers"""

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          UpperCAmelCase__			:     int										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
          self.assertEqual(len(_lowerCAmelCase		)  ,	28997		)
          UpperCAmelCase__			:     List[Any]										= json.loads(tokenizer._tokenizer.to_str()		)
          self.assertIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          UpperCAmelCase__			:     Any										= """3.0.0"""
          UpperCAmelCase__			:     Optional[int]										= old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCAmelCase		)
          self.assertEqual(len(_lowerCAmelCase		)  ,	28996		)
          UpperCAmelCase__			:     Optional[int]										= json.loads(old_tokenizer._tokenizer.to_str()		)
          self.assertNotIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)







@require_tokenizers
class 							UpperCAmelCase_     (		unittest.TestCase		):




     def      __UpperCAmelCase     (							self		):
          # See https://github.com/huggingface/transformers/pull/12550
          # and https://github.com/huggingface/tokenizers/issues/537
          UpperCAmelCase__			:     int										= PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""		)
          UpperCAmelCase__			:     List[str]										= """The Matrix is a 1999 science fiction action film."""

          with concurrent.futures.ThreadPoolExecutor() as executor:
               UpperCAmelCase__			:     List[str]										= [executor.submit(self.fetch  ,	_lowerCAmelCase  ,	_lowerCAmelCase		) for i in range(10		)]
               UpperCAmelCase__			:     Dict										= [future.result() for future in futures]
               self.assertEqual(_lowerCAmelCase  ,	[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10		)]		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          return tokenizer.encode(_lowerCAmelCase  ,	truncation="""longest_first"""  ,	padding="""longest"""		)






