"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					{'wrong_key': 224}
      with self.assertRaises(_SCREAMING_SNAKE_CASE	):
         get_size_dict(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					{'height': 224}
      with self.assertRaises(_SCREAMING_SNAKE_CASE	):
         get_size_dict(_SCREAMING_SNAKE_CASE	)

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

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

      __lowerCamelCase    		=					{'shortest_edge': 224}
      __lowerCamelCase    		=					get_size_dict(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				{'shortest_edge': 224}	)

      __lowerCamelCase    		=					{'longest_edge': 224, 'shortest_edge': 224}
      __lowerCamelCase    		=					get_size_dict(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				{'longest_edge': 224, 'shortest_edge': 224}	)

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

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

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

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

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

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













from typing import TYPE_CHECKING

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


__lowerCAmelCase			:Optional[int]	 =     {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Dict	 =     ['MobileNetV1FeatureExtractor']
							__lowerCAmelCase			:List[Any]	 =     ['MobileNetV1ImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Optional[int]	 =     [
							    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MobileNetV1ForImageClassification',
							    'MobileNetV1Model',
							    'MobileNetV1PreTrainedModel',
							    'load_tf_weights_in_mobilenet_v1',
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							__lowerCAmelCase			:Optional[int]	 =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)