





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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		tempfile.mkdtemp()

             # fmt: off
             _lowerCamelCase							:     List[str]	=		['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
             # fmt: on
             _lowerCamelCase							:     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]))

             _lowerCamelCase							:     Optional[int]	=		{
                 '''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],
             }
             _lowerCamelCase							:     List[str]	=		os.path.join(self.tmpdirname      ,							a__)
             with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8''') as fp:
                    json.dump(a__      ,							a__)


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             return image_inputs


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.get_tokenizer()
             _lowerCamelCase							:     List[Any]	=		self.get_image_processor()

             _lowerCamelCase							:     int	=		VisionTextDualEncoderProcessor(tokenizer=a__      ,							image_processor=a__)

             processor.save_pretrained(self.tmpdirname)
             _lowerCamelCase							:     str	=		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      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		VisionTextDualEncoderProcessor(
                 tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor())
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase							:     Optional[Any]	=		self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)''')
             _lowerCamelCase							:     List[Any]	=		self.get_image_processor(do_normalize=a__      ,							padding_value=1.0)

             _lowerCamelCase							:     str	=		VisionTextDualEncoderProcessor.from_pretrained(
                 self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=a__      ,							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      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     str	=		self.prepare_image_inputs()

             _lowerCamelCase							:     List[str]	=		image_processor(a__      ,							return_tensors='''np''')
             _lowerCamelCase							:     Optional[Any]	=		processor(images=a__      ,							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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.get_image_processor()
             _lowerCamelCase							:     int	=		self.get_tokenizer()

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

             _lowerCamelCase							:     Dict	=		'''lower newer'''

             _lowerCamelCase							:     Optional[Any]	=		processor(text=a__)

             _lowerCamelCase							:     Any	=		tokenizer(a__)

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.get_image_processor()
             _lowerCamelCase							:     Optional[int]	=		self.get_tokenizer()

             _lowerCamelCase							:     Any	=		VisionTextDualEncoderProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Optional[Any]	=		'''lower newer'''
             _lowerCamelCase							:     List[str]	=		self.prepare_image_inputs()

             _lowerCamelCase							:     Tuple	=		processor(text=a__      ,							images=a__)

             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(a__):
                    processor()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.get_image_processor()
             _lowerCamelCase							:     Dict	=		self.get_tokenizer()

             _lowerCamelCase							:     List[Any]	=		VisionTextDualEncoderProcessor(tokenizer=a__      ,							image_processor=a__)

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

             _lowerCamelCase							:     Dict	=		processor.batch_decode(a__)
             _lowerCamelCase							:     Tuple	=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__      ,							a__)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.get_image_processor()
             _lowerCamelCase							:     int	=		self.get_tokenizer()

             _lowerCamelCase							:     Tuple	=		VisionTextDualEncoderProcessor(tokenizer=a__      ,							image_processor=a__)

             _lowerCamelCase							:     Tuple	=		'''lower newer'''
             _lowerCamelCase							:     Optional[Any]	=		self.prepare_image_inputs()

             _lowerCamelCase							:     List[Any]	=		processor(text=a__      ,							images=a__)

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








def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float(moles / volume      ) * nfactor      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (volume)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((pressure * volume) / (0.0_8_2_1 * moles)      )      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

