






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  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[str]   )				-> str:
        __UpperCAmelCase				:      List[str]	 =    tempfile.mkdtemp()

        __UpperCAmelCase				:      Optional[int]	 =    BlipImageProcessor()
        __UpperCAmelCase				:      Any	 =    BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""   )

        __UpperCAmelCase				:      Optional[Any]	 =    BlipProcessor(__lowercase						, __lowercase   )

        processor.save_pretrained(self.tmpdirname   )
      def       UpperCAmelCase ( self   :     int						, **__lowercase   :     Optional[Any]   )				-> Any:
        return AutoProcessor.from_pretrained(self.tmpdirname						, **__lowercase   ).tokenizer
      def       UpperCAmelCase ( self   :     Optional[Any]						, **__lowercase   :     int   )				-> str:
        return AutoProcessor.from_pretrained(self.tmpdirname						, **__lowercase   ).image_processor
      def       UpperCAmelCase ( self   :     int   )				-> int:
        shutil.rmtree(self.tmpdirname   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Dict:
        __UpperCAmelCase				:      List[str]	 =    [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta   )]

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

        return image_inputs
      def       UpperCAmelCase ( self   :     int   )				-> Any:
        __UpperCAmelCase				:      int	 =    BlipProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        __UpperCAmelCase				:      str	 =    self.get_tokenizer(bos_token="""(BOS)"""						, eos_token="""(EOS)"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor(do_normalize=__lowercase						, padding_value=1.0   )

        __UpperCAmelCase				:      List[str]	 =    BlipProcessor.from_pretrained(
            self.tmpdirname						, bos_token="""(BOS)"""						, eos_token="""(EOS)"""						, do_normalize=__lowercase						, padding_value=1.0   )

        self.assertEqual(processor.tokenizer.get_vocab()						, tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer						, __lowercase   )

        self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.image_processor						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> int:
        __UpperCAmelCase				:      List[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      Dict	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Optional[Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      Dict	 =    self.prepare_image_inputs()

        __UpperCAmelCase				:      str	 =    image_processor(__lowercase						, return_tensors="""np"""   )
        __UpperCAmelCase				:      Dict	 =    processor(images=__lowercase						, 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   :     Union[str, Any]   )				-> Dict:
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      Dict	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Union[str, Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      str	 =    """lower newer"""

        __UpperCAmelCase				:      int	 =    processor(text=__lowercase   )

        __UpperCAmelCase				:      Any	 =    tokenizer(__lowercase						, return_token_type_ids=__lowercase   )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]						, encoded_processor[key]   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Any:
        __UpperCAmelCase				:      Tuple	 =    self.get_image_processor()
        __UpperCAmelCase				:      Any	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Dict	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      Union[str, Any]	 =    """lower newer"""
        __UpperCAmelCase				:      Tuple	 =    self.prepare_image_inputs()

        __UpperCAmelCase				:      int	 =    processor(text=__lowercase						, images=__lowercase   )

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

        # test if it raises when no input is passed
        with pytest.raises(__lowercase   ):
          processor()
      def       UpperCAmelCase ( self   :     str   )				-> List[Any]:
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      List[str]	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Union[str, Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      str	 =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase				:      str	 =    processor.batch_decode(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    tokenizer.batch_decode(__lowercase   )

        self.assertListEqual(__lowercase						, __lowercase   )




      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[Any]:
        __UpperCAmelCase				:      int	 =    self.get_image_processor()
        __UpperCAmelCase				:      List[str]	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Dict	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      List[Any]	 =    """lower newer"""
        __UpperCAmelCase				:      List[str]	 =    self.prepare_image_inputs()

        __UpperCAmelCase				:      Optional[Any]	 =    processor(text=__lowercase						, images=__lowercase   )

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








class 							UpperCamelCase_       :  # Public class to implement a graph

    '''simple docstring'''






    def __init__(     self :      str   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       row
           A__ 					=       col
           A__ 					=       graph


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->bool:





           '''simple docstring'''

           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           A__ 					=       [-1, 0, 1, -1, 1, -1, 0, 1]
           A__ 					=       True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__):
                         self.diffs(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:  # And finally, count all islands.





           '''simple docstring'''

           A__ 					=       [[False for j in range(self.COL)] for i in range(self.ROW)]
           A__ 					=       0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
                                count += 1
           return count


