





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCamelCase__     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BioGptTokenizer
							snake_case_							 =						False

							def      A_  (      self				:     Any    )	->List[Any]:
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													SCREAMING_SNAKE_CASE__     :				int   =					[
													    "l",
													    "o",
													    "w",
													    "e",
													    "r",
													    "s",
													    "t",
													    "i",
													    "d",
													    "n",
													    "w</w>",
													    "r</w>",
													    "t</w>",
													    "lo",
													    "low",
													    "er</w>",
													    "low</w>",
													    "lowest</w>",
													    "newer</w>",
													    "wider</w>",
													    "<unk>",
													]
													SCREAMING_SNAKE_CASE__     :				int   =					dict(zip(UpperCAmelCase__							,	range(len(UpperCAmelCase__    )    )    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					["l o 123", "lo w 1456", "e r</w> 1789", ""]

													SCREAMING_SNAKE_CASE__     :				int   =					os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["vocab_file"]    )
													SCREAMING_SNAKE_CASE__     :				str   =					os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["merges_file"]    )
													with open(self.vocab_file							,	"w"    ) as fp:
																			fp.write(json.dumps(UpperCAmelCase__    )    )
													with open(self.merges_file							,	"w"    ) as fp:
																			fp.write("\n".join(UpperCAmelCase__    )    )

							def      A_  (      self				:     List[str]							,	a				:     Optional[Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"lower newer"
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"lower newer"
													return input_text, output_text

							def      A_  (      self				:     Tuple    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					BioGptTokenizer(self.vocab_file							,	self.merges_file    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					"lower"
													SCREAMING_SNAKE_CASE__     :				str   =					["low", "er</w>"]
													SCREAMING_SNAKE_CASE__     :				str   =					tokenizer.tokenize(UpperCAmelCase__    )
													self.assertListEqual(UpperCAmelCase__							,	UpperCAmelCase__    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					tokens + ["<unk>"]
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__    )							,	UpperCAmelCase__    )






							@slow
							def      A_  (      self				:     Any    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BioGptTokenizer.from_pretrained("microsoft/biogpt"    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.encode("sequence builders"							,	add_special_tokens=UpperCAmelCase__    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.encode("multi-sequence build"							,	add_special_tokens=UpperCAmelCase__    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__    )
													SCREAMING_SNAKE_CASE__     :				str   =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__							,	UpperCAmelCase__    )

													self.assertTrue(encoded_sentence == [2] + text    )
													self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )






from __future__ import annotations

from fractions import Fraction







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						return (
						    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
						)







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					[]
						SCREAMING_SNAKE_CASE__     :				str   =					11
						SCREAMING_SNAKE_CASE__     :				Any   =					int("1" + "0" * digit_len     )
						for num in range(_lowerCamelCase			,   _lowerCamelCase     ):
												while den <= 99:
																		if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																								if is_digit_cancelling(_lowerCamelCase			,   _lowerCamelCase     ):
																														solutions.append(f"""{num}/{den}"""     )
																		den += 1
												num += 1
												SCREAMING_SNAKE_CASE__     :				str   =					10
						return solutions







def       UpperCAmelCase							(     _lowerCamelCase      :  int = 2     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1.0
						for fraction in fraction_list(_lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					Fraction(_lowerCamelCase     )
												result *= frac.denominator / frac.numerator
						return int(_lowerCamelCase     )


if __name__ == "__main__":
				print(solution())