
"""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   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Any 							=						tempfile.mkdtemp()

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

          UpperCAmelCase__							: List[Any] 							=						{
              '''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__							: Dict 							=						os.path.join(self.tmpdirname ,			__UpperCamelCase  )
          with open(self.image_processor_file ,			"w" ,			encoding="utf-8"  ) as fp:
               json.dump(__UpperCamelCase ,			__UpperCamelCase  )



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



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Tuple:
          return ViTImageProcessor.from_pretrained(self.tmpdirname ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          shutil.rmtree(self.tmpdirname  )



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Optional[int] 							=						[np.random.randint(2_55 ,			size=(3, 30, 4_00) ,			dtype=np.uinta  )]

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

          return image_inputs



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

          UpperCAmelCase__							: int 							=						VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase ,			image_processor=__UpperCamelCase  )

          processor.save_pretrained(self.tmpdirname  )
          UpperCAmelCase__							: Optional[int] 							=						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 ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Optional[Any] 							=						VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer() ,			image_processor=self.get_image_processor()  )
          processor.save_pretrained(self.tmpdirname  )

          UpperCAmelCase__							: Optional[int] 							=						self.get_tokenizer(bos_token="(BOS)" ,			eos_token="(EOS)"  )
          UpperCAmelCase__							: str 							=						self.get_image_processor(do_normalize=__UpperCamelCase ,			padding_value=1.0  )

          UpperCAmelCase__							: str 							=						VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname ,			bos_token="(BOS)" ,			eos_token="(EOS)" ,			do_normalize=__UpperCamelCase ,			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 ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Any 							=						self.get_image_processor()
          UpperCAmelCase__							: Optional[int] 							=						self.get_tokenizer()

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

          UpperCAmelCase__							: str 							=						self.prepare_image_inputs()

          UpperCAmelCase__							: Any 							=						image_processor(__UpperCamelCase ,			return_tensors="np"  )
          UpperCAmelCase__							: Union[str, Any] 							=						processor(images=__UpperCamelCase ,			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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						self.get_image_processor()
          UpperCAmelCase__							: Optional[Any] 							=						self.get_tokenizer()

          UpperCAmelCase__							: Dict 							=						VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase ,			image_processor=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						'''lower newer'''

          UpperCAmelCase__							: List[str] 							=						processor(text=__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						tokenizer(__UpperCamelCase  )

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



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Optional[int] 							=						self.get_image_processor()
          UpperCAmelCase__							: Dict 							=						self.get_tokenizer()

          UpperCAmelCase__							: Dict 							=						VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase ,			image_processor=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						'''lower newer'''
          UpperCAmelCase__							: List[Any] 							=						self.prepare_image_inputs()

          UpperCAmelCase__							: Dict 							=						processor(text=__UpperCamelCase ,			images=__UpperCamelCase  )

          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(__UpperCamelCase  ):
               processor()



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

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

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

          UpperCAmelCase__							: Tuple 							=						processor.batch_decode(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						tokenizer.batch_decode(__UpperCamelCase  )

          self.assertListEqual(__UpperCamelCase ,			__UpperCamelCase  )



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

          UpperCAmelCase__							: Tuple 							=						VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase ,			image_processor=__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						'''lower newer'''
          UpperCAmelCase__							: Dict 							=						self.prepare_image_inputs()

          UpperCAmelCase__							: Any 							=						processor(text=__UpperCamelCase ,			images=__UpperCamelCase  )

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





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if a < 0:
          raise ValueError("Input value must be a positive integer"		)
     elif isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          raise TypeError("Input value must be a 'int' type"		)
     return bin(lowerCAmelCase		).count("1"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



