





"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> int:
								a      :		Any	=				tempfile.mkdtemp()

								a      :		Union[str, Any]	=				[
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "[PAD]",
								    "[MASK]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								a      :		Optional[Any]	=				os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file      ,					"w"      ,					encoding="utf-8"      ) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )

								a      :		Union[str, Any]	=				{
								    "do_resize": True,
								    "size": 20,
								    "do_center_crop": True,
								    "crop_size": 18,
								    "do_normalize": True,
								    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								a      :		Union[str, Any]	=				os.path.join(self.tmpdirname      ,					lowerCAmelCase__      )
								with open(self.image_processor_file      ,					"w"      ,					encoding="utf-8"      ) as fp:
									json.dump(lowerCAmelCase__      ,					lowerCAmelCase__      )






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> Union[str, Any]:
								return BertTokenizer.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> List[Any]:
								return BertTokenizerFast.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> Tuple:
								return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase__      )






							def     __a   (   self      )	-> str:
								shutil.rmtree(self.tmpdirname      )






							def     __a   (   self      )	-> List[str]:
								a      :		str	=				[np.random.randint(255      ,					size=(3, 30, 400)      ,					dtype=np.uinta      )]
								a      :		Dict	=				[Image.fromarray(np.moveaxis(lowerCAmelCase__      ,					0      ,					-1      )      ) for x in image_inputs]
								return image_inputs






							def     __a   (   self      )	-> Optional[int]:
								a      :		Union[str, Any]	=				self.get_tokenizer()
								a      :		Tuple	=				self.get_rust_tokenizer()
								a      :		Dict	=				self.get_image_processor()

								a      :		int	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )
								processor_slow.save_pretrained(self.tmpdirname      )
								a      :		Tuple	=				AlignProcessor.from_pretrained(self.tmpdirname      ,					use_fast=lowerCAmelCase__      )

								a      :		Optional[Any]	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )
								processor_fast.save_pretrained(self.tmpdirname      )
								a      :		List[str]	=				AlignProcessor.from_pretrained(self.tmpdirname      )

								self.assertEqual(processor_slow.tokenizer.get_vocab()      ,					tokenizer_slow.get_vocab()      )
								self.assertEqual(processor_fast.tokenizer.get_vocab()      ,					tokenizer_fast.get_vocab()      )
								self.assertEqual(tokenizer_slow.get_vocab()      ,					tokenizer_fast.get_vocab()      )
								self.assertIsInstance(processor_slow.tokenizer      ,					lowerCAmelCase__      )
								self.assertIsInstance(processor_fast.tokenizer      ,					lowerCAmelCase__      )

								self.assertEqual(processor_slow.image_processor.to_json_string()      ,					image_processor.to_json_string()      )
								self.assertEqual(processor_fast.image_processor.to_json_string()      ,					image_processor.to_json_string()      )
								self.assertIsInstance(processor_slow.image_processor      ,					lowerCAmelCase__      )
								self.assertIsInstance(processor_fast.image_processor      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> str:
								a      :		int	=				AlignProcessor(tokenizer=self.get_tokenizer()      ,					image_processor=self.get_image_processor()      )
								processor.save_pretrained(self.tmpdirname      )

								a      :		List[str]	=				self.get_tokenizer(bos_token="(BOS)"      ,					eos_token="(EOS)"      )
								a      :		Dict	=				self.get_image_processor(do_normalize=lowerCAmelCase__      ,					padding_value=1.0      )

								a      :		List[Any]	=				AlignProcessor.from_pretrained(
								    self.tmpdirname      ,					bos_token="(BOS)"      ,					eos_token="(EOS)"      ,					do_normalize=lowerCAmelCase__      ,					padding_value=1.0      )

								self.assertEqual(processor.tokenizer.get_vocab()      ,					tokenizer_add_kwargs.get_vocab()      )
								self.assertIsInstance(processor.tokenizer      ,					lowerCAmelCase__      )

								self.assertEqual(processor.image_processor.to_json_string()      ,					image_processor_add_kwargs.to_json_string()      )
								self.assertIsInstance(processor.image_processor      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> Dict:
								a      :		Dict	=				self.get_image_processor()
								a      :		Tuple	=				self.get_tokenizer()

								a      :		Optional[int]	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )

								a      :		str	=				self.prepare_image_inputs()

								a      :		Any	=				image_processor(lowerCAmelCase__      ,					return_tensors="np"      )
								a      :		int	=				processor(images=lowerCAmelCase__      ,					return_tensors="np"      )

								for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()      ,					input_processor[key].sum()      ,					delta=1E-2      )






							def     __a   (   self      )	-> Optional[int]:
								a      :		str	=				self.get_image_processor()
								a      :		str	=				self.get_tokenizer()

								a      :		Any	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )

								a      :		Dict	=				"lower newer"

								a      :		Optional[int]	=				processor(text=lowerCAmelCase__      )

								a      :		Any	=				tokenizer(lowerCAmelCase__      ,					padding="max_length"      ,					max_length=64      )

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,					encoded_processor[key]      )






							def     __a   (   self      )	-> Optional[Any]:
								a      :		List[Any]	=				self.get_image_processor()
								a      :		Dict	=				self.get_tokenizer()

								a      :		List[Any]	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				"lower newer"
								a      :		Optional[Any]	=				self.prepare_image_inputs()

								a      :		Optional[int]	=				processor(text=lowerCAmelCase__      ,					images=lowerCAmelCase__      )

								self.assertListEqual(list(inputs.keys()      )      ,					["input_ids", "token_type_ids", "attention_mask", "pixel_values"]      )

								# test if it raises when no input is passed
								with pytest.raises(lowerCAmelCase__      ):
									processor()






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		int	=				self.get_image_processor()
								a      :		Optional[Any]	=				self.get_tokenizer()

								a      :		int	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )

								a      :		Tuple	=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a      :		Tuple	=				processor.batch_decode(lowerCAmelCase__      )
								a      :		Tuple	=				tokenizer.batch_decode(lowerCAmelCase__      )

								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> Tuple:
								a      :		Optional[int]	=				self.get_image_processor()
								a      :		int	=				self.get_tokenizer()

								a      :		Any	=				AlignProcessor(tokenizer=lowerCAmelCase__      ,					image_processor=lowerCAmelCase__      )

								a      :		List[Any]	=				"lower newer"
								a      :		Union[str, Any]	=				self.prepare_image_inputs()

								a      :		Tuple	=				processor(text=lowerCAmelCase__      ,					images=lowerCAmelCase__      )

								self.assertListEqual(list(inputs.keys()      )      ,					processor.model_input_names      )









"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->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()


