




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 						UpperCamelCase__      (unittest.TestCase	):

    '''simple docstring'''



    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : Tuple     					=   tempfile.mkdtemp()

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

           lowerCamelCase  : 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],
           }
           lowerCamelCase  : int     					=   os.path.join(self.tmpdirname	,    _UpperCAmelCase	)
           with open(self.image_processor_file	,    "w"	,    encoding="utf-8"	) as fp:
                  json.dump(_UpperCAmelCase	,    _UpperCAmelCase	)







    def        _lowercase	(       self	,    **UpperCamelCase__	)	-> Tuple:
           return BertTokenizer.from_pretrained(self.tmpdirname	,    **_UpperCAmelCase	)







    def        _lowercase	(       self	,    **UpperCamelCase__	)	-> Any:
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **_UpperCAmelCase	)







    def        _lowercase	(       self	)	-> Optional[Any]:
           shutil.rmtree(self.tmpdirname	)







    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : Union[str, Any]     					=   [np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta	)]

           lowerCamelCase  : List[str]     					=   [Image.fromarray(np.moveaxis(_UpperCAmelCase	,    0	,    -1	)	) for x in image_inputs]

           return image_inputs







    def        _lowercase	(       self	)	-> Dict:
           lowerCamelCase  : Optional[Any]     					=   self.get_tokenizer()
           lowerCamelCase  : List[Any]     					=   self.get_image_processor()

           lowerCamelCase  : Dict     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

           processor.save_pretrained(self.tmpdirname	)
           lowerCamelCase  : Union[str, 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	,    _UpperCAmelCase	)







    def        _lowercase	(       self	)	-> Optional[int]:
           lowerCamelCase  : Optional[int]     					=   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  : Optional[Any]     					=   self.get_image_processor(do_normalize=_UpperCAmelCase	,    padding_value=1.0	)

           lowerCamelCase  : Tuple     					=   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        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : Any     					=   self.get_image_processor()
           lowerCamelCase  : Any     					=   self.get_tokenizer()

           lowerCamelCase  : Optional[int]     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

           lowerCamelCase  : List[Any]     					=   self.prepare_image_inputs()

           lowerCamelCase  : str     					=   image_processor(_UpperCAmelCase	,    return_tensors="np"	)
           lowerCamelCase  : Tuple     					=   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        _lowercase	(       self	)	-> Any:
           lowerCamelCase  : Optional[int]     					=   self.get_image_processor()
           lowerCamelCase  : Optional[int]     					=   self.get_tokenizer()

           lowerCamelCase  : Optional[int]     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

           lowerCamelCase  : Union[str, Any]     					=   'lower newer'

           lowerCamelCase  : Optional[Any]     					=   processor(text=_UpperCAmelCase	)

           lowerCamelCase  : List[Any]     					=   tokenizer(_UpperCAmelCase	)

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







    def        _lowercase	(       self	)	-> int:
           lowerCamelCase  : List[str]     					=   self.get_image_processor()
           lowerCamelCase  : Any     					=   self.get_tokenizer()

           lowerCamelCase  : Any     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

           lowerCamelCase  : Union[str, Any]     					=   'lower newer'
           lowerCamelCase  : Dict     					=   self.prepare_image_inputs()

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

           lowerCamelCase  : List[str]     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

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

           lowerCamelCase  : List[str]     					=   processor.batch_decode(_UpperCAmelCase	)
           lowerCamelCase  : Union[str, Any]     					=   tokenizer.batch_decode(_UpperCAmelCase	)

           self.assertListEqual(_UpperCAmelCase	,    _UpperCAmelCase	)







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

           lowerCamelCase  : str     					=   VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,    image_processor=_UpperCAmelCase	)

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

           lowerCamelCase  : str     					=   processor(text=_UpperCAmelCase	,    images=_UpperCAmelCase	)

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













'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	int:
     if len(__snake_case     ) != len(__snake_case     ):
          raise ValueError('String lengths must match!'     )

     __A		:					Optional[Any]						=			0

     for chara, chara in zip(__snake_case					,				__snake_case     ):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()