





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       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase__							:				Optional[Any]          =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       UpperCamelCase__							:				Union[str, 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__							:				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],
       }
       UpperCamelCase__							:				Union[str, Any]          =      os.path.join(self.tmpdirname,		__magic_name__							)
       with open(self.image_processor_file,		'''w''',		encoding='''utf-8'''							) as fp:
           json.dump(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       return BertTokenizer.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      List[Any]:






       """simple docstring"""
       return ViTImageProcessor.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       shutil.rmtree(self.tmpdirname							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta							)]

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

       return image_inputs




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.get_tokenizer()
       UpperCamelCase__							:				str          =      self.get_image_processor()

       UpperCamelCase__							:				Dict          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       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,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      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__							:				Dict          =      self.get_image_processor(do_normalize=__magic_name__,		padding_value=1.0							)

       UpperCamelCase__							:				Optional[Any]          =      VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname,		bos_token='''(BOS)''',		eos_token='''(EOS)''',		do_normalize=__magic_name__,		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,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.get_image_processor()
       UpperCamelCase__							:				Optional[int]          =      self.get_tokenizer()

       UpperCamelCase__							:				Any          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      self.prepare_image_inputs()

       UpperCamelCase__							:				List[str]          =      image_processor(__magic_name__,		return_tensors='''np'''							)
       UpperCamelCase__							:				Any          =      processor(images=__magic_name__,		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:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.get_image_processor()
       UpperCamelCase__							:				Union[str, Any]          =      self.get_tokenizer()

       UpperCamelCase__							:				Tuple          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      '''lower newer'''

       UpperCamelCase__							:				int          =      processor(text=__magic_name__							)

       UpperCamelCase__							:				List[str]          =      tokenizer(__magic_name__							)

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




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.get_image_processor()
       UpperCamelCase__							:				Optional[int]          =      self.get_tokenizer()

       UpperCamelCase__							:				Optional[int]          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

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

       UpperCamelCase__							:				Tuple          =      processor(text=__magic_name__,		images=__magic_name__							)

       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(__magic_name__							):
           processor()




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.get_image_processor()
       UpperCamelCase__							:				Union[str, Any]          =      self.get_tokenizer()

       UpperCamelCase__							:				Optional[int]          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

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

       UpperCamelCase__							:				Optional[Any]          =      processor.batch_decode(__magic_name__							)
       UpperCamelCase__							:				Dict          =      tokenizer.batch_decode(__magic_name__							)

       self.assertListEqual(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.get_image_processor()
       UpperCamelCase__							:				str          =      self.get_tokenizer()

       UpperCamelCase__							:				int          =      VisionTextDualEncoderProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

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

       UpperCamelCase__							:				Any          =      processor(text=__magic_name__,		images=__magic_name__							)

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








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''      )

    UpperCamelCase__							:				Optional[Any]          =      0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()

