



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     List[Any]								=     BarthezTokenizer
				A_   :     List[str]								=     BarthezTokenizerFast
				A_   :     Any								=     True
				A_   :     Any								=     True


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											super().setUp()

											A      						=    BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'       )
											tokenizer.save_pretrained(self.tmpdirname       )
											tokenizer.save_pretrained(self.tmpdirname						, legacy_format=__UpperCamelCase       )
											A      						=    tokenizer


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    '<pad>'
											A      						=    1

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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Dict:
											A      						=    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(__UpperCamelCase       )						, 101_122       )


				def __UpperCamelCase						(							self      :  Any       )		->     List[str]:
											self.assertEqual(self.get_tokenizer().vocab_size						, 101_122       )


				@require_torch
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     str:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											A      						=    [0, 57, 3_018, 70_307, 91, 2]

											A      						=    self.tokenizer(
											    __UpperCamelCase						, max_length=len(__UpperCamelCase       )						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, return_tensors='pt'       )
											self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )

											self.assertEqual((2, 6)						, batch.input_ids.shape       )
											self.assertEqual((2, 6)						, batch.attention_mask.shape       )
											A      						=    batch.input_ids.tolist()[0]
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Dict:
											if not self.test_rust_tokenizer:
																		return

											A      						=    self.get_tokenizer()
											A      						=    self.get_rust_tokenizer()

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

											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											A      						=    rust_tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											A      						=    rust_tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    self.get_rust_tokenizer()
											A      						=    tokenizer.encode(__UpperCamelCase       )
											A      						=    rust_tokenizer.encode(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[int]:
											# fmt: off
											A      						=    {'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 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], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], '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, 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, 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]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											A      						=    [
											    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
											    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
											    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
											    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
											    'telles que la traduction et la synthèse de texte.',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=__UpperCamelCase						, model_name='moussaKam/mbarthez'						, revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'						, sequences=__UpperCamelCase						, )






from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    _A     =							int(number**0.5				)
    return number == sq * sq





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> tuple[int, int]:




    """simple docstring"""




    _A     =							x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _A     =							x_den * y_den * z_den
    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    top //= hcf
    bottom //= hcf
    return top, bottom





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 35				)							-> int:




    """simple docstring"""




    _A     =							set()
    _A     =							42
    _A     =							Fraction(0				)
    _A     =							42

    for x_num in range(1							,     order + 1				):
        for x_den in range(x_num + 1							,     order + 1				):
            for y_num in range(1							,     order + 1				):
                for y_den in range(y_num + 1							,     order + 1				):
                    # n=1
                    _A     =							x_num * y_den + x_den * y_num
                    _A     =							x_den * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _A     =							x_den * x_den * y_den * y_den
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=-1
                    _A     =							x_num * y_num
                    _A     =							x_den * y_num + x_num * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							x_num * x_num * y_num * y_num
                    _A     =							(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

    for num, den in unique_s:
        total += Fraction(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    return total.denominator + total.numerator


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





