

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_    :						Optional[Any]    		=      get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
@require_tokenizers
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =XLMRobertaTokenizer
    __UpperCamelCase         =XLMRobertaTokenizerFast
    __UpperCamelCase         =True
    __UpperCamelCase         =True

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE								=			XLMRobertaTokenizer(snake_case__							,    keep_accents=snake_case__    )
     tokenizer.save_pretrained(self.tmpdirname    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'<pad>'
     SCREAMING_SNAKE_CASE								=			1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    )							,    snake_case__    )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    )							,    snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]							,    '<s>'    )
     self.assertEqual(vocab_keys[1]							,    '<pad>'    )
     self.assertEqual(vocab_keys[-1]							,    '<mask>'    )
     self.assertEqual(len(snake_case__    )							,    1_0_0_2    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size							,    1_0_0_2    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			XLMRobertaTokenizer(snake_case__							,    keep_accents=snake_case__    )

     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize('This is a test'    )
     self.assertListEqual(snake_case__							,    ['▁This', '▁is', '▁a', '▁t', 'est']    )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(snake_case__    )							,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,    )

     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
     self.assertListEqual(
         snake_case__							,    [
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '9',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             'é',
             '.',
         ]							,    )
     SCREAMING_SNAKE_CASE								=			tokenizer.convert_tokens_to_ids(snake_case__    )
     self.assertListEqual(
         snake_case__							,    [
             value + tokenizer.fairseq_offset
             for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]							,    )

     SCREAMING_SNAKE_CASE								=			tokenizer.convert_ids_to_tokens(snake_case__    )
     self.assertListEqual(
         snake_case__							,    [
             SPIECE_UNDERLINE + 'I',
             SPIECE_UNDERLINE + 'was',
             SPIECE_UNDERLINE + 'b',
             'or',
             'n',
             SPIECE_UNDERLINE + 'in',
             SPIECE_UNDERLINE + '',
             '<unk>',
             '2',
             '0',
             '0',
             '0',
             ',',
             SPIECE_UNDERLINE + 'and',
             SPIECE_UNDERLINE + 'this',
             SPIECE_UNDERLINE + 'is',
             SPIECE_UNDERLINE + 'f',
             'al',
             's',
             '<unk>',
             '.',
         ]							,    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     if not self.test_slow_tokenizer:
      # as we don't have a slow version, we can't compare the outputs between slow and fast versions
      return

     SCREAMING_SNAKE_CASE								=			(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
       SCREAMING_SNAKE_CASE								=			self.rust_tokenizer_class.from_pretrained(snake_case__							,    **snake_case__    )
       SCREAMING_SNAKE_CASE								=			self.tokenizer_class.from_pretrained(snake_case__							,    **snake_case__    )

       SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE								=			tokenizer_r.save_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.save_pretrained(snake_case__    )

       # Checks it save with the same files + the tokenizer.json file for the fast one
       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )
       SCREAMING_SNAKE_CASE								=			tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f    )
       self.assertSequenceEqual(snake_case__							,    snake_case__    )

       # Checks everything loads correctly in the same way
       SCREAMING_SNAKE_CASE								=			tokenizer_r.from_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.from_pretrained(snake_case__    )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(snake_case__							,    snake_case__    )    )
        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

       shutil.rmtree(snake_case__    )

       # Save tokenizer rust, legacy_format=True
       SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE								=			tokenizer_r.save_pretrained(snake_case__							,    legacy_format=snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.save_pretrained(snake_case__    )

       # Checks it save with the same files
       self.assertSequenceEqual(snake_case__							,    snake_case__    )

       # Checks everything loads correctly in the same way
       SCREAMING_SNAKE_CASE								=			tokenizer_r.from_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.from_pretrained(snake_case__    )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(snake_case__							,    snake_case__    )    )

       shutil.rmtree(snake_case__    )

       # Save tokenizer rust, legacy_format=False
       SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE								=			tokenizer_r.save_pretrained(snake_case__							,    legacy_format=snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.save_pretrained(snake_case__    )

       # Checks it saved the tokenizer.json file
       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )

       # Checks everything loads correctly in the same way
       SCREAMING_SNAKE_CASE								=			tokenizer_r.from_pretrained(snake_case__    )
       SCREAMING_SNAKE_CASE								=			tokenizer_p.from_pretrained(snake_case__    )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(snake_case__							,    snake_case__    )    )

       shutil.rmtree(snake_case__    )

    @cached_property
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     with tempfile.NamedTemporaryFile() as f:
      shutil.copyfile(snake_case__							,    f.name    )
      SCREAMING_SNAKE_CASE								=			XLMRobertaTokenizer(f.name							,    keep_accents=snake_case__    )
      SCREAMING_SNAKE_CASE								=			pickle.dumps(snake_case__    )
     pickle.loads(snake_case__    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     if not self.test_rust_tokenizer:
      return

     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE								=			'I was born in 92000, and this is falsé.'

     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize(snake_case__    )
     SCREAMING_SNAKE_CASE								=			rust_tokenizer.tokenize(snake_case__    )
     self.assertListEqual(snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			tokenizer.encode(snake_case__							,    add_special_tokens=snake_case__    )
     SCREAMING_SNAKE_CASE								=			rust_tokenizer.encode(snake_case__							,    add_special_tokens=snake_case__    )
     self.assertListEqual(snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE								=			tokenizer.encode(snake_case__    )
     SCREAMING_SNAKE_CASE								=			rust_tokenizer.encode(snake_case__    )
     self.assertListEqual(snake_case__							,    snake_case__    )

    @slow
    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'Hello World!'
     SCREAMING_SNAKE_CASE								=			[0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(snake_case__							,    self.big_tokenizer.encode(snake_case__    )    )

    @slow
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			(
         'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
         ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
     )
     SCREAMING_SNAKE_CASE								=			[
         0,
         3_2_9_3,
         8_3,
         1_0,
         4_5_5_2,
         4_9_8_9,
         7_9_8_6,
         6_7_8,
         1_0,
         5_9_1_5,
         1_1_1,
         1_7_9_4_5_9,
         1_2_4_8_5_0,
         4,
         6_0_4_4,
         2_3_7,
         1_2,
         6,
         5,
         6,
         4,
         6_7_8_0,
         7_0_5,
         1_5,
         1_3_8_8,
         4_4,
         3_7_8,
         1_0_1_1_4,
         7_1_1,
         1_5_2,
         2_0,
         6,
         5,
         2_2_3_7_6,
         6_4_2,
         1_2_2_1,
         1_5_1_9_0,
         3_4_1_5_3,
         4_5_0,
         5_6_0_8,
         9_5_9,
         1_1_1_9,
         5_7_7_0_2,
         1_3_6,
         1_8_6,
         4_7,
         1_0_9_8,
         2_9_3_6_7,
         4_7,
         # 4426, # What fairseq tokenizes from "<unk>": "_<"
         # 3678, # What fairseq tokenizes from "<unk>": "unk"
         # 2740, # What fairseq tokenizes from "<unk>": ">"
         3,  # What we tokenize from "<unk>": "<unk>"
         6,  # Residue from the tokenization: an extra sentencepiece underline
         4,
         6_0_4_4,
         2_3_7,
         6_2_8_4,
         5_0_9_0_1,
         5_2_8,
         3_1,
         9_0,
         3_4,
         9_2_7,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(snake_case__							,    self.big_tokenizer.encode(snake_case__    )    )




    @slow
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=snake_case__							,    model_name='xlm-roberta-base'							,    revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'							,    )







def        __lowerCAmelCase    (       _UpperCamelCase		:  int = 10_00	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			2**power
 SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0

 for i in list_num:
  sum_of_num += int(_UpperCamelCase	)

 return sum_of_num


if __name__ == "__main__":
      a_    :						List[str]    		=      int(input("Enter the power of 2: ").strip())
      print("2 ^ ", power, " = ", 2**power)
      a_    :						int    		=      solution(power)
      print("Sum of the digits is: ", result)

