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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Dict        =		tempfile.mkdtemp()

        __UpperCamelCase					:Union[str, Any]        =		[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        __UpperCamelCase					:Tuple        =		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]))

        __UpperCamelCase					:Optional[int]        =		{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        __UpperCamelCase					:Union[str, Any]        =		os.path.join(self.tmpdirname			,			__lowercase)
        with open(self.image_processor_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            json.dump(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Any:
        return BertTokenizer.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Union[str, Any]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Tuple:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        shutil.rmtree(self.tmpdirname)







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Optional[int]        =		[np.random.randint(255			,			size=(3, 30, 400)			,			dtype=np.uinta)]
        __UpperCamelCase					:Any        =		[Image.fromarray(np.moveaxis(__lowercase			,			0			,			-1)) for x in image_inputs]
        return image_inputs







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		self.get_tokenizer()
        __UpperCamelCase					:int        =		self.get_rust_tokenizer()
        __UpperCamelCase					:Optional[int]        =		self.get_image_processor()

        __UpperCamelCase					:Optional[int]        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)
        processor_slow.save_pretrained(self.tmpdirname)
        __UpperCamelCase					:Optional[int]        =		AlignProcessor.from_pretrained(self.tmpdirname			,			use_fast=__lowercase)

        __UpperCamelCase					:int        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)
        processor_fast.save_pretrained(self.tmpdirname)
        __UpperCamelCase					:int        =		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			,			__lowercase)
        self.assertIsInstance(processor_fast.tokenizer			,			__lowercase)

        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			,			__lowercase)
        self.assertIsInstance(processor_fast.image_processor			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		AlignProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        __UpperCamelCase					:str        =		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)''')
        __UpperCamelCase					:List[str]        =		self.get_image_processor(do_normalize=__lowercase			,			padding_value=1.0)

        __UpperCamelCase					:Dict        =		AlignProcessor.from_pretrained(
            self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=__lowercase			,			padding_value=1.0)

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

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







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:List[Any]        =		self.get_image_processor()
        __UpperCamelCase					:int        =		self.get_tokenizer()

        __UpperCamelCase					:Any        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

        __UpperCamelCase					:Dict        =		self.prepare_image_inputs()

        __UpperCamelCase					:Dict        =		image_processor(__lowercase			,			return_tensors='''np''')
        __UpperCamelCase					:List[str]        =		processor(images=__lowercase			,			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       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		self.get_image_processor()
        __UpperCamelCase					:Dict        =		self.get_tokenizer()

        __UpperCamelCase					:List[str]        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

        __UpperCamelCase					:int        =		'''lower newer'''

        __UpperCamelCase					:Dict        =		processor(text=__lowercase)

        __UpperCamelCase					:Any        =		tokenizer(__lowercase			,			padding='''max_length'''			,			max_length=64)

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







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Union[str, Any]        =		self.get_image_processor()
        __UpperCamelCase					:List[str]        =		self.get_tokenizer()

        __UpperCamelCase					:Optional[Any]        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

        __UpperCamelCase					:Optional[int]        =		'''lower newer'''
        __UpperCamelCase					:Tuple        =		self.prepare_image_inputs()

        __UpperCamelCase					:Union[str, Any]        =		processor(text=__lowercase			,			images=__lowercase)

        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(__lowercase):
            processor()







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:List[Any]        =		self.get_image_processor()
        __UpperCamelCase					:Tuple        =		self.get_tokenizer()

        __UpperCamelCase					:Union[str, Any]        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

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

        __UpperCamelCase					:Any        =		processor.batch_decode(__lowercase)
        __UpperCamelCase					:Dict        =		tokenizer.batch_decode(__lowercase)

        self.assertListEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		self.get_image_processor()
        __UpperCamelCase					:Optional[int]        =		self.get_tokenizer()

        __UpperCamelCase					:int        =		AlignProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

        __UpperCamelCase					:List[str]        =		'''lower newer'''
        __UpperCamelCase					:List[str]        =		self.prepare_image_inputs()

        __UpperCamelCase					:str        =		processor(text=__lowercase			,			images=__lowercase)

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






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''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()





