





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     : Union[str, Any]	     =       tempfile.mkdtemp()

       UpperCamelCase     : Tuple	     =       BlipImageProcessor()
       UpperCamelCase     : str	     =       BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"       )

       UpperCamelCase     : Union[str, Any]	     =       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     : List[str]	     =       [np.random.randint(255	,							size=(3, 30, 400)	,							dtype=np.uinta       )]

       UpperCamelCase     : Tuple	     =       [Image.fromarray(np.moveaxis(A_	,							0	,							-1       )       ) for x in image_inputs]

       return image_inputs





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       BlipProcessor(tokenizer=self.get_tokenizer()	,							image_processor=self.get_image_processor()       )
       processor.save_pretrained(self.tmpdirname       )

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

       UpperCamelCase     : Union[str, Any]	     =       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     : Union[str, Any]	     =       self.get_image_processor()
       UpperCamelCase     : str	     =       self.get_tokenizer()

       UpperCamelCase     : int	     =       BlipProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : int	     =       self.prepare_image_inputs()

       UpperCamelCase     : int	     =       image_processor(A_	,							return_tensors="np"       )
       UpperCamelCase     : Dict	     =       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     : int	     =       self.get_image_processor()
       UpperCamelCase     : List[Any]	     =       self.get_tokenizer()

       UpperCamelCase     : int	     =       BlipProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Optional[int]	     =       "lower newer"

       UpperCamelCase     : List[Any]	     =       processor(text=A_       )

       UpperCamelCase     : Any	     =       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     : Union[str, Any]	     =       self.get_image_processor()
       UpperCamelCase     : Optional[Any]	     =       self.get_tokenizer()

       UpperCamelCase     : str	     =       BlipProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Any	     =       "lower newer"
       UpperCamelCase     : Dict	     =       self.prepare_image_inputs()

       UpperCamelCase     : List[Any]	     =       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     : List[Any]	     =       self.get_image_processor()
       UpperCamelCase     : int	     =       self.get_tokenizer()

       UpperCamelCase     : Dict	     =       BlipProcessor(tokenizer=A_	,							image_processor=A_       )

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

       UpperCamelCase     : Optional[int]	     =       processor.batch_decode(A_       )
       UpperCamelCase     : Optional[Any]	     =       tokenizer.batch_decode(A_       )

       self.assertListEqual(A_	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       self.get_image_processor()
       UpperCamelCase     : Optional[Any]	     =       self.get_tokenizer()

       UpperCamelCase     : Tuple	     =       BlipProcessor(tokenizer=A_	,							image_processor=A_       )

       UpperCamelCase     : Optional[Any]	     =       "lower newer"
       UpperCamelCase     : Optional[int]	     =       self.prepare_image_inputs()

       UpperCamelCase     : Optional[Any]	     =       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"]       )







from pathlib import Path

import numpy as np
from PIL import Image



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 return (gray > 127) & (gray <= 255)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     : Dict	     =       np.zeros_like(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

 # Copy image to padded image
 UpperCamelCase     : Tuple	     =       image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]					):
  for y in range(image.shape[0]					):
   UpperCamelCase     : List[str]	     =       (
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   UpperCamelCase     : int	     =       int(summation > 0					)
 return output


if __name__ == "__main__":
    # read original image
    __lowerCamelCase	:					int			  =					Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
    __lowerCamelCase	:					Tuple			  =					np.array(Image.open(lena_path))
    # kernel to be applied
    __lowerCamelCase	:					Optional[Any]			  =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    __lowerCamelCase	:					Union[str, Any]			  =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    __lowerCamelCase	:					Any			  =					Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_dilation.png""")
