import unittest

from transformers.image_processing_utils import get_size_dict



class 					snake_case__       (				unittest.TestCase							):



			def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     Dict:




									'''simple docstring'''




									snake_case_     :			List[Any]       =       {'''wrong_key''': 2_24}
									with self.assertRaises(_snake_case   ):
															get_size_dict(_snake_case   )

									snake_case_     :			Dict       =       {'''height''': 2_24}
									with self.assertRaises(_snake_case   ):
															get_size_dict(_snake_case   )

									snake_case_     :			str       =       {'''width''': 2_24, '''shortest_edge''': 2_24}
									with self.assertRaises(_snake_case   ):
															get_size_dict(_snake_case   )

									# Test a dict with the correct keys is returned as is
									snake_case_     :			Any       =       {'''height''': 2_24, '''width''': 2_24}
									snake_case_     :			Optional[int]       =       get_size_dict(_snake_case   )
									self.assertEqual(_snake_case			,	_snake_case   )

									snake_case_     :			Optional[Any]       =       {'''shortest_edge''': 2_24}
									snake_case_     :			int       =       get_size_dict(_snake_case   )
									self.assertEqual(_snake_case			,	{"shortest_edge": 2_24}   )

									snake_case_     :			int       =       {'''longest_edge''': 2_24, '''shortest_edge''': 2_24}
									snake_case_     :			Tuple       =       get_size_dict(_snake_case   )
									self.assertEqual(_snake_case			,	{"longest_edge": 2_24, "shortest_edge": 2_24}   )

									# Test a single int value which  represents (size, size)
									snake_case_     :			Any       =       get_size_dict(2_24   )
									self.assertEqual(_snake_case			,	{"height": 2_24, "width": 2_24}   )

									# Test a single int value which represents the shortest edge
									snake_case_     :			List[Any]       =       get_size_dict(2_24			,	default_to_square=_snake_case   )
									self.assertEqual(_snake_case			,	{"shortest_edge": 2_24}   )

									# Test a tuple of ints which represents (height, width)
									snake_case_     :			Any       =       get_size_dict((1_50, 2_00)   )
									self.assertEqual(_snake_case			,	{"height": 1_50, "width": 2_00}   )

									# Test a tuple of ints which represents (width, height)
									snake_case_     :			List[str]       =       get_size_dict((1_50, 2_00)			,	height_width_order=_snake_case   )
									self.assertEqual(_snake_case			,	{"height": 2_00, "width": 1_50}   )

									# Test an int representing the shortest edge and max_size which represents the longest edge
									snake_case_     :			Any       =       get_size_dict(2_24			,	max_size=2_56			,	default_to_square=_snake_case   )
									self.assertEqual(_snake_case			,	{"shortest_edge": 2_24, "longest_edge": 2_56}   )

									# Test int with default_to_square=True and max_size fails
									with self.assertRaises(_snake_case   ):
															get_size_dict(2_24			,	max_size=2_56			,	default_to_square=_snake_case   )




from typing import TYPE_CHECKING

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


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



