






"""simple docstring"""

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__          = """▁"""

lowercase__          = get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class 		__lowerCamelCase				( A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				str            =							BertGenerationTokenizer
		a_	:				Any            =							False
		a_	:				Union[str, Any]            =							True





		def    lowerCamelCase       (   self							:	str	):
				super().setUp()

				lowerCAmelCase_   :							str									=			BertGenerationTokenizer(a_						,			keep_accents=a_	)
				tokenizer.save_pretrained(self.tmpdirname	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							List[Any]									=			"<s>"
				lowerCAmelCase_   :							str									=			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]	):
				lowerCAmelCase_   :							List[str]									=			list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]						,			"<unk>"	)
				self.assertEqual(vocab_keys[1]						,			"<s>"	)
				self.assertEqual(vocab_keys[-1]						,			"<pad>"	)
				self.assertEqual(len(a_	)						,			10_02	)





		def    lowerCamelCase       (   self							:	int	):
				self.assertEqual(self.get_tokenizer().vocab_size						,			10_00	)





		def    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							List[str]									=			BertGenerationTokenizer(a_						,			keep_accents=a_	)

				lowerCAmelCase_   :							int									=			tokenizer.tokenize("This is a test"	)
				self.assertListEqual(a_						,			["▁This", "▁is", "▁a", "▁t", "est"]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(a_	)						,			[2_85, 46, 10, 1_70, 3_82]						,			)

				lowerCAmelCase_   :							int									=			tokenizer.tokenize("I was born in 92000, and this is falsé."	)
				self.assertListEqual(
				    a_						,			[
				        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",
				        ".",
				    ]						,			)
				lowerCAmelCase_   :							str									=			tokenizer.convert_tokens_to_ids(a_	)
				self.assertListEqual(
				    a_						,			[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]						,			)

				lowerCAmelCase_   :							Any									=			tokenizer.convert_ids_to_tokens(a_	)
				self.assertListEqual(
				    a_						,			[
				        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    lowerCamelCase       (   self							:	str	):
				return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)





		@slow
		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							str									=			"Hello World!"
				lowerCAmelCase_   :							Optional[Any]									=			[1_85_36, 22_60, 1_01]

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





		@slow
		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Any									=			(
				    "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"
				)
				lowerCAmelCase_   :							Optional[int]									=			[
				    8_71,
				    4_19,
				    3_58,
				    9_46,
				    9_91,
				    25_21,
				    4_52,
				    3_58,
				    13_57,
				    3_87,
				    77_51,
				    35_36,
				    1_12,
				    9_85,
				    4_56,
				    1_26,
				    8_65,
				    9_38,
				    54_00,
				    57_34,
				    4_58,
				    13_68,
				    4_67,
				    7_86,
				    24_62,
				    52_46,
				    11_59,
				    6_33,
				    8_65,
				    45_19,
				    4_57,
				    5_82,
				    8_52,
				    25_57,
				    4_27,
				    9_16,
				    5_08,
				    4_05,
				    3_43_24,
				    4_97,
				    3_91,
				    4_08,
				    1_13_42,
				    12_44,
				    3_85,
				    1_00,
				    9_38,
				    9_85,
				    4_56,
				    5_74,
				    3_62,
				    1_25_97,
				    32_00,
				    31_29,
				    11_72,
				]

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





		@require_torch
		@slow
		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				import torch

				from transformers import BertGenerationConfig, BertGenerationEncoder

				# Build sequence
				lowerCAmelCase_   :							Optional[Any]									=			list(self.big_tokenizer.get_vocab().keys()	)[:10]
				lowerCAmelCase_   :							int									=			" ".join(a_	)
				lowerCAmelCase_   :							int									=			self.big_tokenizer.encode_plus(a_						,			return_tensors="pt"						,			return_token_type_ids=a_	)
				lowerCAmelCase_   :							str									=			self.big_tokenizer.batch_encode_plus(
				    [sequence + " " + sequence]						,			return_tensors="pt"						,			return_token_type_ids=a_	)

				lowerCAmelCase_   :							Dict									=			BertGenerationConfig()
				lowerCAmelCase_   :							Tuple									=			BertGenerationEncoder(a_	)

				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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





		@slow
		def    lowerCamelCase       (   self							:	Tuple	):
				# fmt: off
				lowerCAmelCase_   :							Optional[int]									=			{"input_ids": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 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], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 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]], "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, 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, 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=a_						,			model_name="google/bert_for_seq_generation_L-24_bbc_encoder"						,			revision="c817d1fd1be2ffa69431227a1fe320544943d4db"						,			)








"""simple docstring"""

from __future__ import annotations

from math import pow, sqrt

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    dict[str, float]:

		"""simple docstring"""







		if (resistance, reactance, impedance).count(0       ) != 1:
				raise ValueError("One and only one argument must be 0"       )
		if resistance == 0:
				return {"resistance": sqrt(pow(__UpperCamelCase      ,       2       ) - pow(__UpperCamelCase      ,       2       )       )}
		elif reactance == 0:
				return {"reactance": sqrt(pow(__UpperCamelCase      ,       2       ) - pow(__UpperCamelCase      ,       2       )       )}
		elif impedance == 0:
				return {"impedance": sqrt(pow(__UpperCamelCase      ,       2       ) + pow(__UpperCamelCase      ,       2       )       )}
		else:
				raise ValueError("Exactly one argument must be 0"       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
