




'''simple docstring'''





import unittest

from transformers.image_processing_utils import get_size_dict




class     UpperCamelCase					(  unittest.TestCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			{"""wrong_key""": 224}
      with self.assertRaises(__lowerCAmelCase   ):
          get_size_dict(__lowerCAmelCase   )

      _a							:       List[Any]       					=			{"""height""": 224}
      with self.assertRaises(__lowerCAmelCase   ):
          get_size_dict(__lowerCAmelCase   )

      _a							:       Tuple       					=			{"""width""": 224, """shortest_edge""": 224}
      with self.assertRaises(__lowerCAmelCase   ):
          get_size_dict(__lowerCAmelCase   )

      # Test a dict with the correct keys is returned as is
      _a							:       Optional[int]       					=			{"""height""": 224, """width""": 224}
      _a							:       Dict       					=			get_size_dict(__lowerCAmelCase   )
      self.assertEqual(__lowerCAmelCase		,    __lowerCAmelCase   )

      _a							:       str       					=			{"""shortest_edge""": 224}
      _a							:       List[str]       					=			get_size_dict(__lowerCAmelCase   )
      self.assertEqual(__lowerCAmelCase		,    {"""shortest_edge""": 224}   )

      _a							:       int       					=			{"""longest_edge""": 224, """shortest_edge""": 224}
      _a							:       Union[str, Any]       					=			get_size_dict(__lowerCAmelCase   )
      self.assertEqual(__lowerCAmelCase		,    {"""longest_edge""": 224, """shortest_edge""": 224}   )

      # Test a single int value which  represents (size, size)
      _a							:       List[str]       					=			get_size_dict(224   )
      self.assertEqual(__lowerCAmelCase		,    {"""height""": 224, """width""": 224}   )

      # Test a single int value which represents the shortest edge
      _a							:       Dict       					=			get_size_dict(224		,    default_to_square=__lowerCAmelCase   )
      self.assertEqual(__lowerCAmelCase		,    {"""shortest_edge""": 224}   )

      # Test a tuple of ints which represents (height, width)
      _a							:       Optional[int]       					=			get_size_dict((150, 200)   )
      self.assertEqual(__lowerCAmelCase		,    {"""height""": 150, """width""": 200}   )

      # Test a tuple of ints which represents (width, height)
      _a							:       Dict       					=			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
      _a							:       Tuple       					=			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   )








'''simple docstring'''





from typing import TYPE_CHECKING

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


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


