






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      :       Any				  =   get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class       __lowercase      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				XLMRobertaTokenizer
     SCREAMING_SNAKE_CASE   						=				XLMRobertaTokenizerFast
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				True




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         super().setUp()

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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __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 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __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_				) ,	1_002				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size ,	1_002				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	XLMRobertaTokenizer(UpperCamelCase_ ,	keep_accents=UpperCamelCase_				)

         __A         =	tokenizer.tokenize("""This is a test"""				)
         self.assertListEqual(UpperCamelCase_ ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(UpperCamelCase_				) ,	[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,	)

         __A         =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
         self.assertListEqual(
             UpperCamelCase_ ,	[
                 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""",
                 """.""",
             ] ,	)
         __A         =	tokenizer.convert_tokens_to_ids(UpperCamelCase_				)
         self.assertListEqual(
             UpperCamelCase_ ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,	)

         __A         =	tokenizer.convert_ids_to_tokens(UpperCamelCase_				)
         self.assertListEqual(
             UpperCamelCase_ ,	[
                 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 		lowerCAmelCase_     (				self       :			List[str]				):


         """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

         __A         =	(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})"				):
                 __A         =	self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ ,	**UpperCamelCase_				)
                 __A         =	self.tokenizer_class.from_pretrained(UpperCamelCase_ ,	**UpperCamelCase_				)

                 __A         =	tempfile.mkdtemp()

                 __A         =	tokenizer_r.save_pretrained(UpperCamelCase_				)
                 __A         =	tokenizer_p.save_pretrained(UpperCamelCase_				)

                 # 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				)				)
                 __A         =	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
                 self.assertSequenceEqual(UpperCamelCase_ ,	UpperCamelCase_				)

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

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

                 shutil.rmtree(UpperCamelCase_				)

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

                 __A         =	tokenizer_r.save_pretrained(UpperCamelCase_ ,	legacy_format=UpperCamelCase_				)
                 __A         =	tokenizer_p.save_pretrained(UpperCamelCase_				)

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

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

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

                 shutil.rmtree(UpperCamelCase_				)

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

                 __A         =	tokenizer_r.save_pretrained(UpperCamelCase_ ,	legacy_format=UpperCamelCase_				)
                 __A         =	tokenizer_p.save_pretrained(UpperCamelCase_				)

                 # 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
                 __A         =	tokenizer_r.from_pretrained(UpperCamelCase_				)
                 __A         =	tokenizer_p.from_pretrained(UpperCamelCase_				)

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

                 shutil.rmtree(UpperCamelCase_				)




     @cached_property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         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 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	"""Hello World!"""
         __A         =	[0, 35_378, 6_661, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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




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


         """simple docstring"""



         __A         =	(
             """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"""
         )
         __A         =	[
             0,
             3_293,
             83,
             10,
             4_552,
             4_989,
             7_986,
             678,
             10,
             5_915,
             111,
             179_459,
             124_850,
             4,
             6_044,
             237,
             12,
             6,
             5,
             6,
             4,
             6_780,
             705,
             15,
             1_388,
             44,
             378,
             10_114,
             711,
             152,
             20,
             6,
             5,
             22_376,
             642,
             1_221,
             15_190,
             34_153,
             450,
             5_608,
             959,
             1_119,
             57_702,
             136,
             186,
             47,
             1_098,
             29_367,
             47,
             # 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_044,
             237,
             6_284,
             50_901,
             528,
             31,
             90,
             34,
             927,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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





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


         """simple docstring"""



         __A         =	{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=UpperCamelCase_ ,	model_name="""xlm-roberta-base""" ,	revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" ,	)












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




