


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 		_snake_case  (				unittest.TestCase							):




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	tempfile.mkdtemp()

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       return BertTokenizer.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       return ViTImageProcessor.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       shutil.rmtree(self.tmpdirname   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	[np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta   )]

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

       return image_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	self.get_tokenizer()
       a						:List[str]										=	self.get_image_processor()

       a						:str										=	VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase ,  image_processor=_lowerCamelCase   )

       processor.save_pretrained(self.tmpdirname   )
       a						: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 ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()   )
       processor.save_pretrained(self.tmpdirname   )

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.get_image_processor()
       a						:int										=	self.get_tokenizer()

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

       a						:List[str]										=	self.prepare_image_inputs()

       a						:List[Any]										=	image_processor(_lowerCamelCase ,  return_tensors='''np'''   )
       a						: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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_image_processor()
       a						:Optional[int]										=	self.get_tokenizer()

       a						:Dict										=	VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase ,  image_processor=_lowerCamelCase   )

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

       a						:List[str]										=	processor(text=_lowerCamelCase   )

       a						:Any										=	tokenizer(_lowerCamelCase   )

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	self.get_image_processor()
       a						:List[str]										=	self.get_tokenizer()

       a						:Dict										=	VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase ,  image_processor=_lowerCamelCase   )

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

       a						:Union[str, Any]										=	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 self.assertRaises(_lowerCamelCase   ):
             processor()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.get_image_processor()
       a						:Optional[int]										=	self.get_tokenizer()

       a						:Optional[Any]										=	VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase ,  image_processor=_lowerCamelCase   )

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

       a						:Tuple										=	processor.batch_decode(_lowerCamelCase   )
       a						:Optional[Any]										=	tokenizer.batch_decode(_lowerCamelCase   )

       self.assertListEqual(_lowerCamelCase ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_image_processor()
       a						:List[str]										=	self.get_tokenizer()

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

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

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

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









def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_			,  UpperCAmelCase_						):
            raise TypeError('''Input value must be an \'int\' type'''						)

      a						:Optional[int]										=	0
      while number:
            position += 1
            number >>= 1

      return position


if __name__ == "__main__":
     import doctest

     doctest.testmod()





