





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						tempfile.mkdtemp()

            _UpperCamelCase              =						BlipImageProcessor()
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''      )

            _UpperCamelCase              =						BlipProcessor(A,       A      )

            processor.save_pretrained(self.tmpdirname      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,       **A      ).tokenizer

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,       **A      ).image_processor

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						[Image.fromarray(np.moveaxis(A,       0,       -1      )      ) for x in image_inputs]

            return image_inputs

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						BlipProcessor(tokenizer=self.get_tokenizer(),       image_processor=self.get_image_processor()      )
            processor.save_pretrained(self.tmpdirname      )

            _UpperCamelCase              =						self.get_tokenizer(bos_token='''(BOS)''',       eos_token='''(EOS)'''      )
            _UpperCamelCase              =						self.get_image_processor(do_normalize=A,       padding_value=1.0      )

            _UpperCamelCase              =						BlipProcessor.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,       A      )

            self.assertEqual(processor.image_processor.to_json_string(),       image_processor_add_kwargs.to_json_string()      )
            self.assertIsInstance(processor.image_processor,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						image_processor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A,       return_token_type_ids=A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(text=A,       images=A      )

            self.assertListEqual(list(inputs.keys()      ),       ['''pixel_values''', '''input_ids''', '''attention_mask''']      )

            # test if it raises when no input is passed
            with pytest.raises(A      ):
                   processor()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _UpperCamelCase              =						processor.batch_decode(A      )
            _UpperCamelCase              =						tokenizer.batch_decode(A      )

            self.assertListEqual(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(text=A,       images=A      )

            # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
            self.assertListEqual(list(inputs.keys()      ),       ['''pixel_values''', '''input_ids''', '''attention_mask''']      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						0
       for ch in input_str:
              _UpperCamelCase              =						ord(lowerCAmelCase					)
              _UpperCamelCase              =						pow(2    ,       lowerCAmelCase					)

              # If we already turned on bit for current character's unicode
              if bitmap >> ch_unicode & 1 == 1:
                     return False
              bitmap |= ch_bit_index_on
       return True


if __name__ == "__main__":
       import doctest

       doctest.testmod()






