import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     XLMTokenizer
						lowercase					 =     False

						def        __a		(	self				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowercase							=		[
									    "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>",
									]
									lowercase							=		dict(zip(__lowerCamelCase							,							range(len(__lowerCamelCase   )   )   )   )
									lowercase							=		["l o 123", "lo w 1456", "e r</w> 1789", ""]

									lowercase							=		os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES['''vocab_file''']   )
									lowercase							=		os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES['''merges_file''']   )
									with open(self.vocab_file							,							'''w'''   ) as fp:
												fp.write(json.dumps(__lowerCamelCase   )   )
									with open(self.merges_file							,							'''w'''   ) as fp:
												fp.write('''\n'''.join(__lowerCamelCase   )   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Tuple   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		"lower newer"
									lowercase							=		"lower newer"
									return input_text, output_text

						def        __a		(	self				:      Dict   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		XLMTokenizer(self.vocab_file							,							self.merges_file   )

									lowercase							=		"lower"
									lowercase							=		["low", "er</w>"]
									lowercase							=		tokenizer.tokenize(__lowerCamelCase   )
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		tokens + ["<unk>"]
									lowercase							=		[14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   )							,							__lowerCamelCase   )



						@slow
						def        __a		(	self				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''   )

									lowercase							=		tokenizer.encode('''sequence builders'''							,							add_special_tokens=__lowerCamelCase   )
									lowercase							=		tokenizer.encode('''multi-sequence build'''							,							add_special_tokens=__lowerCamelCase   )

									lowercase							=		tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
									lowercase							=		tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							,							__lowerCamelCase   )

									assert encoded_sentence == [0] + text + [1]
									assert encoded_pair == [0] + text + [1] + text_a + [1]






from __future__ import annotations

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





