


'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor


class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=7				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=3				,							SCREAMING_SNAKE_CASE      :							Tuple=18				,							SCREAMING_SNAKE_CASE      :							Any=30				,							SCREAMING_SNAKE_CASE      :							Optional[int]=400				,							SCREAMING_SNAKE_CASE      :							int=True				,							SCREAMING_SNAKE_CASE      :							Dict=None				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							):
           _A					:     Dict     = size if size is not None else {'shortest_edge': 20}
           _A					:     int     = crop_size if crop_size is not None else {'height': 18, 'width': 18}
           _A					:     Dict     = parent
           _A					:     List[Any]     = batch_size
           _A					:     int     = num_channels
           _A					:     Dict     = image_size
           _A					:     Optional[int]     = min_resolution
           _A					:     int     = max_resolution
           _A					:     List[Any]     = do_resize
           _A					:     Optional[Any]     = size
           _A					:     Tuple     = do_center_crop
           _A					:     Optional[Any]     = crop_size


    def        A   (  self      :							Any):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }




@require_torch
@require_vision
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   MobileNetVaImageProcessor if is_vision_available() else None


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = MobileNetVaImageProcessingTester(self)


    @property
    def        A   (  self      :							List[str]):
           return self.image_processor_tester.prepare_image_processor_dict()


    def        A   (  self      :							List[str]):
           _A					:     Dict     = self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_resize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'size'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'crop_size'))


    def        A   (  self      :							Any):
           _A					:     List[Any]     = self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 20})
           self.assertEqual(image_processor.crop_size				,							{'height': 18, 'width': 18})

           _A					:     Optional[int]     = self.image_processing_class.from_dict(self.image_processor_dict				,							size=42				,							crop_size=84)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 42})
           self.assertEqual(image_processor.crop_size				,							{'height': 84, 'width': 84})


    def        A   (  self      :							Any):
           pass


    def        A   (  self      :							List[str]):
           # Initialize image_processing
           _A					:     Union[str, Any]     = self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _A					:     List[Any]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							Image.Image)

           # Test not batched input
           _A					:     Optional[int]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)

           # Test batched
           _A					:     Optional[int]     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)


    def        A   (  self      :							Any):
           # Initialize image_processing
           _A					:     Optional[Any]     = self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _A					:     str     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							numpify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							np.ndarray)

           # Test not batched input
           _A					:     Optional[int]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)

           # Test batched
           _A					:     Tuple     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)


    def        A   (  self      :							Optional[Any]):
           # Initialize image_processing
           _A					:     Tuple     = self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _A					:     str     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

           # Test not batched input
           _A					:     str     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)

           # Test batched
           _A					:     Union[str, Any]     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )				,							)










'''simple docstring'''







class 			__lowerCamelCase			:  # Public class to implement a graph
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           _A					:     List[Any]     = row
           _A					:     Union[str, Any]     = col
           _A					:     List[str]     = graph


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           # Checking all 8 elements surrounding nth element
           _A					:     Tuple     = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A					:     Dict     = [-1, 0, 1, -1, 1, -1, 0, 1]
           _A					:     List[Any]     = True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE):
                         self.diffs(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):  # And finally, count all islands.
           _A					:     Dict     = [[False for j in range(self.COL)] for i in range(self.ROW)]
           _A					:     Union[str, Any]     = 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(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                                count += 1
           return count






