


"""simple docstring"""
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        __A       (SCREAMING_SNAKE_CASE__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[str]							 =      BarthezTokenizer
							__lowercase:						Optional[int]							 =      BarthezTokenizerFast
							__lowercase:						Any							 =      True
							__lowercase:						Any							 =      True



							def 						lowerCAmelCase    (			self							: Any    )   ->str:



														"""simple docstring"""



														super().setUp()

														snake_case_          =						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""    )
														tokenizer.save_pretrained(self.tmpdirname    )
														tokenizer.save_pretrained(self.tmpdirname						,    legacy_format=a_    )
														snake_case_          =						tokenizer



							def 						lowerCAmelCase    (			self							: str    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						"""<pad>"""
														snake_case_          =						1

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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Optional[int]:



														"""simple docstring"""



														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(a_    )						,    101_122    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Optional[int]:



														"""simple docstring"""



														self.assertEqual(self.get_tokenizer().vocab_size						,    101_122    )



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



														"""simple docstring"""



														snake_case_          =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														snake_case_          =						[0, 57, 3_018, 70_307, 91, 2]

														snake_case_          =						self.tokenizer(
														    a_						,    max_length=len(a_    )						,    padding=a_						,    truncation=a_						,    return_tensors="""pt"""    )
														self.assertIsInstance(a_						,    a_    )

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



							def 						lowerCAmelCase    (			self							: int    )   ->Tuple:



														"""simple docstring"""



														if not self.test_rust_tokenizer:
																					return

														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_rust_tokenizer()

														snake_case_          =						"""I was born in 92000, and this is falsé."""

														snake_case_          =						tokenizer.tokenize(a_    )
														snake_case_          =						rust_tokenizer.tokenize(a_    )
														self.assertListEqual(a_						,    a_    )

														snake_case_          =						tokenizer.encode(a_						,    add_special_tokens=a_    )
														snake_case_          =						rust_tokenizer.encode(a_						,    add_special_tokens=a_    )
														self.assertListEqual(a_						,    a_    )

														snake_case_          =						self.get_rust_tokenizer()
														snake_case_          =						tokenizer.encode(a_    )
														snake_case_          =						rust_tokenizer.encode(a_    )
														self.assertListEqual(a_						,    a_    )



							@slow
							def 						lowerCAmelCase    (			self							: Dict    )   ->str:



														"""simple docstring"""



														snake_case_          =						{"""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.
														snake_case_          =						[
														    """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=a_						,    model_name="""moussaKam/mbarthez"""						,    revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,    sequences=a_						,    )










"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterator





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Any						,    UpperCAmelCase_							: int    )   ->None:



														"""simple docstring"""



														snake_case_          =						value
														snake_case_          =						None
														snake_case_          =						None





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[Any]						,    UpperCAmelCase_							: Node    )   ->None:



														"""simple docstring"""



														snake_case_          =						tree



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Node | None    )   ->int:



														"""simple docstring"""



														if node is None:
																					return 0
														return node.value + (
														    self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
														)



							def __iter__(			self							: str    )   ->Iterator[int]:



														"""simple docstring"""



														yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



