





"""simple docstring"""




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A   : Optional[int]			=				get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class   A      (SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[Any]          =   ReformerTokenizer
			__lowerCamelCase     :	Tuple          =   ReformerTokenizerFast
			__lowerCamelCase     :	List[Any]          =   True
			__lowerCamelCase     :	Union[str, Any]          =   False
			__lowerCamelCase     :	Any          =   True






			def        a_	(      self       :   str       )     ->     List[str]:







					"""simple docstring"""






					super().setUp()

					A__  				=       ReformerTokenizer(__lowerCAmelCase							,     keep_accents=__lowerCAmelCase       )
					tokenizer.save_pretrained(self.tmpdirname       )






			def        a_	(      self       :   Optional[int]       )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       """<s>"""
					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        a_	(      self       :   List[Any]       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       list(self.get_tokenizer().get_vocab().keys()       )

					self.assertEqual(vocab_keys[0]							,     """<unk>"""       )
					self.assertEqual(vocab_keys[1]							,     """<s>"""       )
					self.assertEqual(vocab_keys[-1]							,     """j"""       )
					self.assertEqual(len(__lowerCAmelCase       )							,     10_00       )






			def        a_	(      self       :   List[Any]       )     ->     Tuple:







					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,     10_00       )






			def        a_	(      self       :   Any       )     ->     int:







					"""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(__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       )






			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int]=15       )     ->     Union[str, Any]:







					"""simple docstring"""






					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       )

									# Simple input
									A__  				=       """This is a simple input"""
									A__  				=       ["""This is a simple input 1""", """This is a simple input 2"""]
									A__  				=       ("""This is a simple input""", """This is a pair""")
									A__  				=       [
									    ("""This is a simple input 1""", """This is a simple input 2"""),
									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
									]

									# Simple input tests
									self.assertRaises(__lowerCAmelCase							,     tokenizer_r.encode							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""       )

									# Simple input
									self.assertRaises(__lowerCAmelCase							,     tokenizer_r.encode_plus							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""       )

									# Simple input
									self.assertRaises(
									    __lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""							,     )

									# Pair input
									self.assertRaises(__lowerCAmelCase							,     tokenizer_r.encode							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""       )

									# Pair input
									self.assertRaises(__lowerCAmelCase							,     tokenizer_r.encode_plus							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""       )

									# Pair input
									self.assertRaises(
									    __lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     __lowerCAmelCase							,     max_length=__lowerCAmelCase							,     padding="""max_length"""							,     )






			def        a_	(      self       :   Any       )     ->     Any:







					"""simple docstring"""






					pass






			def        a_	(      self       :   str       )     ->     Any:







					"""simple docstring"""






					A__  				=       ReformerTokenizer(__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       )							,     [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							,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,     )

					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>""",
					        """.""",
					    ]							,     )






			@cached_property
			def        a_	(      self       :   Optional[int]       )     ->     Optional[Any]:







					"""simple docstring"""






					return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""       )






			@slow
			def        a_	(      self       :   List[str]       )     ->     str:







					"""simple docstring"""






					A__  				=       """Hello World!"""
					A__  				=       [1_26, 32, 2_62, 1_52, 38, 72, 2_87]

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






			@slow
			def        a_	(      self       :   Optional[Any]       )     ->     Optional[int]:







					"""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__  				=       [
					    1_08,
					    2_65,
					    24,
					    1_11,
					    4,
					    2_58,
					    1_56,
					    35,
					    28,
					    2_75,
					    3,
					    2_59,
					    2_97,
					    2_60,
					    84,
					    4,
					    35,
					    1_10,
					    44,
					    8,
					    2_59,
					    91,
					    2_68,
					    21,
					    11,
					    2_09,
					    2_74,
					    1_09,
					    2_66,
					    2_77,
					    1_17,
					    86,
					    93,
					    3_15,
					    2_58,
					    2_78,
					    2_58,
					    2_77,
					    2_58,
					    0,
					    2_58,
					    2_88,
					    2_58,
					    3_19,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    0,
					    2_58,
					    2_87,
					    2_58,
					    3_15,
					    2_58,
					    2_89,
					    2_58,
					    2_78,
					    99,
					    2_69,
					    2_66,
					    2_62,
					    8,
					    2_59,
					    2_41,
					    4,
					    2_17,
					    2_30,
					    2_68,
					    2_66,
					    55,
					    1_68,
					    1_06,
					    75,
					    1_93,
					    2_66,
					    2_23,
					    27,
					    49,
					    26,
					    2_82,
					    25,
					    2_64,
					    2_99,
					    19,
					    26,
					    0,
					    2_58,
					    2_77,
					    1_17,
					    86,
					    93,
					    1_76,
					    1_83,
					    2_70,
					    11,
					    2_62,
					    42,
					    61,
					    2_65,
					]

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






			@require_torch
			@slow
			def        a_	(      self       :   Optional[Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					import torch

					from transformers import ReformerConfig, ReformerModel

					# Build sequence
					A__  				=       list(self.big_tokenizer.get_vocab().keys()       )[:10]
					A__  				=       """ """.join(__lowerCAmelCase       )
					A__  				=       self.big_tokenizer.encode_plus(__lowerCAmelCase							,     return_tensors="""pt"""       )
					A__  				=       self.big_tokenizer.batch_encode_plus([sequence, sequence]							,     return_tensors="""pt"""       )

					A__  				=       ReformerConfig()
					# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
					A__  				=       encoded_sequence["""input_ids"""].shape
					A__  				=       ReformerModel(__lowerCAmelCase       )

					# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
							model(**__lowerCAmelCase       )
							model(**__lowerCAmelCase       )







			@slow
			def        a_	(      self       :   Union[str, Any]       )     ->     Dict:







					"""simple docstring"""






					A__  				=       {"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """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]]}  # noqa: E501
					# fmt: on

					# This tokenizer does not know some characters like ")".
					# That is the reason why we use very simple texts here.
					# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
					A__  				=       [
					    """This is a very simple sentence.""",
					    """The quick brown fox jumps over the lazy dog.""",
					]

					self.tokenizer_integration_test_util(
					    expected_encoding=__lowerCAmelCase							,     model_name="""google/reformer-crime-and-punishment"""							,     revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""							,     padding=__lowerCAmelCase							,     sequences=__lowerCAmelCase							,     )










def        __lowerCamelCase (							__a       :int = 3   ,					__a       :int = 7   ,					__a       :int = 1_0_0_0_0_0_0   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       1

		for current_denominator in range(1   ,					limit + 1   ):
				A__  				=       current_denominator * numerator // denominator
				if current_denominator % denominator == 0:
						current_numerator -= 1
				if current_numerator * max_denominator > current_denominator * max_numerator:
						A__  				=       current_numerator
						A__  				=       current_denominator
		return max_numerator


if __name__ == "__main__":
					print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))



