





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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tempfile.mkdtemp()

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							[
                "[UNK]",
                "[CLS]",
                "[SEP]",
                "[PAD]",
                "[MASK]",
                "的",
                "价",
                "格",
                "是",
                "15",
                "便",
                "alex",
                "##andra",
                "，",
                "。",
                "-",
                "t",
                "shirt",
            ]
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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]							)							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							{
                "do_resize": True,
                "size": {"height": 2_2_4, "width": 2_2_4},
                "do_center_crop": True,
                "crop_size": {"height": 1_8, "width": 1_8},
                "do_normalize": True,
                "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
                "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
                "do_convert_rgb": True,
            }
            _SCREAMING_SNAKE_CASE       :	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     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return BertTokenizer.from_pretrained(self.tmpdirname    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     Tuple:
            return BertTokenizerFast.from_pretrained(self.tmpdirname    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     Any:
            return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,						**__lowerCamelCase							)




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




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Any						    =							[np.random.randint(2_5_5    ,						size=(3, 3_0, 4_0_0)    ,						dtype=np.uinta							)]

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[Image.fromarray(np.moveaxis(__lowerCamelCase    ,						0    ,						-1							)							) for x in image_inputs]

            return image_inputs




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.get_rust_tokenizer()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_image_processor()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)
            processor_slow.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,						use_fast=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)
            processor_fast.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							ChineseCLIPProcessor.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     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()							)
            processor.save_pretrained(self.tmpdirname							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_tokenizer(cls_token="(CLS)"    ,						sep_token="(SEP)"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.get_image_processor(do_normalize=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							ChineseCLIPProcessor.from_pretrained(
                self.tmpdirname    ,						cls_token="(CLS)"    ,						sep_token="(SEP)"    ,						do_normalize=__lowerCamelCase							)

            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     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_image_processor()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE       :	int						    =							image_processor(__lowerCamelCase    ,						return_tensors="np"							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_image_processor()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	str						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Any						    =							"Alexandra，T-shirt的价格是15便士。"

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(__lowerCamelCase							)

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




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_image_processor()
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	int						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							"Alexandra，T-shirt的价格是15便士。"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE       :	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     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_image_processor()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _SCREAMING_SNAKE_CASE       :	Dict						    =							processor.batch_decode(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer.batch_decode(__lowerCamelCase							)

            self.assertListEqual(__lowerCamelCase    ,						__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_image_processor()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Any						    =							ChineseCLIPProcessor(tokenizer=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							"Alexandra，T-shirt的价格是15便士。"
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							processor(text=__lowerCamelCase    ,						images=__lowerCamelCase							)

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






import cmath
import math






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							math.radians(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							math.radians(__lowerCamelCase	)

     # Convert voltage and current to rectangular form
     _SCREAMING_SNAKE_CASE       :	str						    =							cmath.rect(__lowerCamelCase,       __lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	str						    =							cmath.rect(__lowerCamelCase,       __lowerCamelCase	)

     # Calculate apparent power
     return voltage_rect * current_rect


if __name__ == "__main__":
       import doctest

       doctest.testmod()