




from __future__ import annotations




def 			__magic_name__				( A		:					list[int],  A		:					int   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return False
			a    =			len(A   ) // 2
			if a_list[midpoint] == item:
						return True
			if item < a_list[midpoint]:
						return binary_search(a_list[:midpoint],  A   )
			else:
						return binary_search(a_list[midpoint + 1 :],  A   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Optional[int]       			=		input('Enter numbers separated by comma:\n').strip()
		__lowerCAmelCase		:				Union[str, Any]       			=		[int(item.strip()) for item in user_input.split(',')]
		__lowerCAmelCase		:				List[str]       			=		int(input('Enter the number to be found in the list:\n').strip())
		__lowerCAmelCase		:				Optional[int]       			=		'' if binary_search(sequence, target) else 'not '
		print(F'''{target} was {not_str}found in {sequence}''')












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


__lowerCAmelCase		:				str       			=		get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					XLMRobertaTokenizer
				SCREAMING_SNAKE_CASE_       :							int							 =					XLMRobertaTokenizerFast
				SCREAMING_SNAKE_CASE_       :							str							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					True





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							super().setUp()

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





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			"<pad>"
							a    =			1

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





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							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(__lowerCamelCase							)					,      10_02							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Any:
							self.assertEqual(self.get_tokenizer().vocab_size					,      10_02							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							a    =			XLMRobertaTokenizer(__lowerCamelCase					,      keep_accents=__lowerCamelCase							)

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,      )

							a    =			tokenizer.tokenize("I was born in 92000, and this is falsé."							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        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(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]					,      )

							a    =			tokenizer.convert_ids_to_tokens(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [
							        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				:     Optional[Any]							)					->     List[str]:
							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(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)

													a    =			tempfile.mkdtemp()

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

													# 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(__lowerCamelCase					,      __lowerCamelCase							)

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

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

													shutil.rmtree(__lowerCamelCase							)

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

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase					,      legacy_format=__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

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

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

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

													shutil.rmtree(__lowerCamelCase							)

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

													a    =			tokenizer_r.save_pretrained(__lowerCamelCase					,      legacy_format=__lowerCamelCase							)
													a    =			tokenizer_p.save_pretrained(__lowerCamelCase							)

													# 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(__lowerCamelCase							)
													a    =			tokenizer_p.from_pretrained(__lowerCamelCase							)

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

													shutil.rmtree(__lowerCamelCase							)





				@cached_property
				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[Any]:
							with tempfile.NamedTemporaryFile() as f:
										shutil.copyfile(__lowerCamelCase					,      f.name							)
										a    =			XLMRobertaTokenizer(f.name					,      keep_accents=__lowerCamelCase							)
										a    =			pickle.dumps(__lowerCamelCase							)
							pickle.loads(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     str:
							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(__lowerCamelCase							)
							a    =			rust_tokenizer.tokenize(__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
							a    =			rust_tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			self.get_rust_tokenizer()
							a    =			tokenizer.encode(__lowerCamelCase							)
							a    =			rust_tokenizer.encode(__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Any:
							a    =			"Hello World!"
							a    =			[0, 3_53_78, 66_61, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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





				@slow
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     int:
							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,
							    32_93,
							    83,
							    10,
							    45_52,
							    49_89,
							    79_86,
							    6_78,
							    10,
							    59_15,
							    1_11,
							    17_94_59,
							    12_48_50,
							    4,
							    60_44,
							    2_37,
							    12,
							    6,
							    5,
							    6,
							    4,
							    67_80,
							    7_05,
							    15,
							    13_88,
							    44,
							    3_78,
							    1_01_14,
							    7_11,
							    1_52,
							    20,
							    6,
							    5,
							    2_23_76,
							    6_42,
							    12_21,
							    1_51_90,
							    3_41_53,
							    4_50,
							    56_08,
							    9_59,
							    11_19,
							    5_77_02,
							    1_36,
							    1_86,
							    47,
							    10_98,
							    2_93_67,
							    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,
							    60_44,
							    2_37,
							    62_84,
							    5_09_01,
							    5_28,
							    31,
							    90,
							    34,
							    9_27,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[Any]:
							# fmt: off
							a    =			{"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=__lowerCamelCase					,      model_name="xlm-roberta-base"					,      revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"					,      )






