

"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    tempfile.mkdtemp()

        # fmt: off
        UpperCAmelCase__			:							Tuple							=    ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
        # fmt: on
        UpperCAmelCase__			:							str							=    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__			:							str							=    {
            """do_resize""": True,
            """size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.5, 0.5, 0.5],
            """image_std""": [0.5, 0.5, 0.5],
        }
        UpperCAmelCase__			:							Optional[int]							=    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	):


        """simple docstring"""

        return BertTokenizer.from_pretrained(self.tmpdirname							,       **_lowerCamelCase	)



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        return ViTImageProcessor.from_pretrained(self.tmpdirname							,       **_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        shutil.rmtree(self.tmpdirname	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    [np.random.randint(255							,       size=(3, 30, 400)							,       dtype=np.uinta	)]

        UpperCAmelCase__			:							Any							=    [Image.fromarray(np.moveaxis(_lowerCamelCase							,       0							,       -1	)	) for x in image_inputs]

        return image_inputs



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    self.get_tokenizer()
        UpperCAmelCase__			:							Any							=    self.get_image_processor()

        UpperCAmelCase__			:							str							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        processor.save_pretrained(self.tmpdirname	)
        UpperCAmelCase__			:							Any							=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer.get_vocab()	)
        self.assertIsInstance(processor.tokenizer							,       (BertTokenizer, BertTokenizerFast)	)

        self.assertEqual(processor.image_processor.to_json_string()							,       image_processor.to_json_string()	)
        self.assertIsInstance(processor.image_processor							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    VisionTextDualEncoderProcessor(
            tokenizer=self.get_tokenizer()							,       image_processor=self.get_image_processor()	)
        processor.save_pretrained(self.tmpdirname	)

        UpperCAmelCase__			:							Any							=    self.get_tokenizer(bos_token="""(BOS)"""							,       eos_token="""(EOS)"""	)
        UpperCAmelCase__			:							int							=    self.get_image_processor(do_normalize=_lowerCamelCase							,       padding_value=1.0	)

        UpperCAmelCase__			:							str							=    VisionTextDualEncoderProcessor.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							,       (BertTokenizer, BertTokenizerFast)	)

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.get_image_processor()
        UpperCAmelCase__			:							Tuple							=    self.get_tokenizer()

        UpperCAmelCase__			:							Optional[int]							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    self.prepare_image_inputs()

        UpperCAmelCase__			:							Optional[int]							=    image_processor(_lowerCamelCase							,       return_tensors="""np"""	)
        UpperCAmelCase__			:							Tuple							=    processor(images=_lowerCamelCase							,       return_tensors="""np"""	)

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.get_image_processor()
        UpperCAmelCase__			:							str							=    self.get_tokenizer()

        UpperCAmelCase__			:							List[str]							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    """lower newer"""

        UpperCAmelCase__			:							str							=    processor(text=_lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    tokenizer(_lowerCamelCase	)

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



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.get_image_processor()
        UpperCAmelCase__			:							Optional[Any]							=    self.get_tokenizer()

        UpperCAmelCase__			:							Union[str, Any]							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    """lower newer"""
        UpperCAmelCase__			:							Dict							=    self.prepare_image_inputs()

        UpperCAmelCase__			:							List[Any]							=    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 self.assertRaises(_lowerCamelCase	):
              processor()



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    self.get_image_processor()
        UpperCAmelCase__			:							Dict							=    self.get_tokenizer()

        UpperCAmelCase__			:							Optional[Any]							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__			:							str							=    processor.batch_decode(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    tokenizer.batch_decode(_lowerCamelCase	)

        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.get_image_processor()
        UpperCAmelCase__			:							Tuple							=    self.get_tokenizer()

        UpperCAmelCase__			:							Union[str, Any]							=    VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase							,       image_processor=_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    """lower newer"""
        UpperCAmelCase__			:							Any							=    self.prepare_image_inputs()

        UpperCAmelCase__			:							Union[str, Any]							=    processor(text=_lowerCamelCase							,       images=_lowerCamelCase	)

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




"""simple docstring"""
import cmath
import math




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> complex:
      UpperCAmelCase__			:							str							=    math.radians(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    math.radians(lowerCAmelCase    )

      # Convert voltage and current to rectangular form
      UpperCAmelCase__			:							Union[str, Any]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[str]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )

      # Calculate apparent power
      return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()

