

import unittest

from transformers.image_processing_utils import get_size_dict





class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	={'wrong_key': 224}
        with self.assertRaises(lowerCAmelCase ):
              get_size_dict(lowerCAmelCase )

        _lowercase     	={'height': 224}
        with self.assertRaises(lowerCAmelCase ):
              get_size_dict(lowerCAmelCase )

        _lowercase     	={'width': 224, 'shortest_edge': 224}
        with self.assertRaises(lowerCAmelCase ):
              get_size_dict(lowerCAmelCase )

        # Test a dict with the correct keys is returned as is
        _lowercase     	={'height': 224, 'width': 224}
        _lowercase     	=get_size_dict(lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       lowerCAmelCase )

        _lowercase     	={'shortest_edge': 224}
        _lowercase     	=get_size_dict(lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       {'shortest_edge': 224} )

        _lowercase     	={'longest_edge': 224, 'shortest_edge': 224}
        _lowercase     	=get_size_dict(lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       {'longest_edge': 224, 'shortest_edge': 224} )

        # Test a single int value which  represents (size, size)
        _lowercase     	=get_size_dict(224 )
        self.assertEqual(lowerCAmelCase							,       {'height': 224, 'width': 224} )

        # Test a single int value which represents the shortest edge
        _lowercase     	=get_size_dict(224							,       default_to_square=lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       {'shortest_edge': 224} )

        # Test a tuple of ints which represents (height, width)
        _lowercase     	=get_size_dict((150, 200) )
        self.assertEqual(lowerCAmelCase							,       {'height': 150, 'width': 200} )

        # Test a tuple of ints which represents (width, height)
        _lowercase     	=get_size_dict((150, 200)							,       height_width_order=lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       {'height': 200, 'width': 150} )

        # Test an int representing the shortest edge and max_size which represents the longest edge
        _lowercase     	=get_size_dict(224							,       max_size=256							,       default_to_square=lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       {'shortest_edge': 224, 'longest_edge': 256} )

        # Test int with default_to_square=True and max_size fails
        with self.assertRaises(lowerCAmelCase ):
              get_size_dict(224							,       max_size=256							,       default_to_square=lowerCAmelCase )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_     =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       ['OneFormerImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       [
      'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OneFormerForUniversalSegmentation',
      'OneFormerModel',
      'OneFormerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
  from .processing_oneformer import OneFormerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_oneformer import OneFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_oneformer import (
        ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        OneFormerForUniversalSegmentation,
        OneFormerModel,
        OneFormerPreTrainedModel,
    )


else:
  import sys

  lowercase_     =       _LazyModule(__name__, globals()['__file__'], _import_structure)


