
"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           LayoutLMForMaskedLM,
           LayoutLMForQuestionAnswering,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMModel,
       )



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Tuple     ,   a_       : Union[str, Any]     ,   a_       : Union[str, Any]=13     ,   a_       : Union[str, Any]=7     ,   a_       : Optional[int]=True     ,   a_       : List[str]=True     ,   a_       : Tuple=True     ,   a_       : List[str]=True     ,   a_       : int=99     ,   a_       : Optional[Any]=32     ,   a_       : Any=5     ,   a_       : Dict=4     ,   a_       : Dict=37     ,   a_       : Dict="gelu"     ,   a_       : Union[str, Any]=0.1     ,   a_       : Dict=0.1     ,   a_       : int=5_12     ,   a_       : int=16     ,   a_       : Dict=2     ,   a_       : Optional[int]=0.02     ,   a_       : int=3     ,   a_       : Tuple=4     ,   a_       : Dict=None     ,   a_       : Optional[Any]=10_00     ,   )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  parent
      UpperCAmelCase_						:    str    					=  batch_size
      UpperCAmelCase_						:    Dict    					=  seq_length
      UpperCAmelCase_						:    Any    					=  is_training
      UpperCAmelCase_						:    Union[str, Any]    					=  use_input_mask
      UpperCAmelCase_						:    str    					=  use_token_type_ids
      UpperCAmelCase_						:    List[Any]    					=  use_labels
      UpperCAmelCase_						:    List[str]    					=  vocab_size
      UpperCAmelCase_						:    Dict    					=  hidden_size
      UpperCAmelCase_						:    Dict    					=  num_hidden_layers
      UpperCAmelCase_						:    Union[str, Any]    					=  num_attention_heads
      UpperCAmelCase_						:    str    					=  intermediate_size
      UpperCAmelCase_						:    List[str]    					=  hidden_act
      UpperCAmelCase_						:    int    					=  hidden_dropout_prob
      UpperCAmelCase_						:    List[str]    					=  attention_probs_dropout_prob
      UpperCAmelCase_						:    int    					=  max_position_embeddings
      UpperCAmelCase_						:    Tuple    					=  type_vocab_size
      UpperCAmelCase_						:    Tuple    					=  type_sequence_label_size
      UpperCAmelCase_						:    Optional[int]    					=  initializer_range
      UpperCAmelCase_						:    List[Any]    					=  num_labels
      UpperCAmelCase_						:    int    					=  num_choices
      UpperCAmelCase_						:    Dict    					=  scope
      UpperCAmelCase_						:    List[Any]    					=  range_bbox




 def   a      ( self       : Dict )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size )

      UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size, self.seq_length, 4]     ,   self.range_bbox )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0] ):
           for j in range(bbox.shape[1] ):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                     UpperCAmelCase_						:    List[Any]    					=  bbox[i, j, 3]
                     UpperCAmelCase_						:    Tuple    					=  bbox[i, j, 1]
                     UpperCAmelCase_						:    Dict    					=  t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                     UpperCAmelCase_						:    Tuple    					=  bbox[i, j, 2]
                     UpperCAmelCase_						:    Any    					=  bbox[i, j, 0]
                     UpperCAmelCase_						:    Dict    					=  t

      UpperCAmelCase_						:    List[Any]    					=  None
      if self.use_input_mask:
           UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size, self.seq_length]     ,   vocab_size=2 )

      UpperCAmelCase_						:    str    					=  None
      if self.use_token_type_ids:
           UpperCAmelCase_						:    Union[str, Any]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.type_vocab_size )

      UpperCAmelCase_						:    Dict    					=  None
      UpperCAmelCase_						:    Optional[Any]    					=  None
      UpperCAmelCase_						:    str    					=  None
      if self.use_labels:
           UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size]     ,   self.type_sequence_label_size )
           UpperCAmelCase_						:    List[Any]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.num_labels )
           UpperCAmelCase_						:    Optional[int]    					=  ids_tensor([self.batch_size]     ,   self.num_choices )

      UpperCAmelCase_						:    int    					=  self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def   a      ( self       : Any )->					List[Any]:



      """simple docstring"""

      return LayoutLMConfig(
          vocab_size=self.vocab_size     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   max_position_embeddings=self.max_position_embeddings     ,   type_vocab_size=self.type_vocab_size     ,   initializer_range=self.initializer_range     ,   )




 def   a      ( self       : Union[str, Any]     ,   a_       : str     ,   a_       : Tuple     ,   a_       : int     ,   a_       : Dict     ,   a_       : int     ,   a_       : Any     ,   a_       : Optional[int]     ,   a_       : int )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMModel(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_ )
      UpperCAmelCase_						:    Optional[int]    					=  model(a_     ,   a_     ,   token_type_ids=a_ )
      UpperCAmelCase_						:    str    					=  model(a_     ,   a_ )
      self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size) )
      self.parent.assertEqual(result.pooler_output.shape     ,   (self.batch_size, self.hidden_size) )




 def   a      ( self       : Union[str, Any]     ,   a_       : Optional[Any]     ,   a_       : Union[str, Any]     ,   a_       : List[str]     ,   a_       : str     ,   a_       : List[str]     ,   a_       : Optional[int]     ,   a_       : int     ,   a_       : List[Any] )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMForMaskedLM(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size) )




 def   a      ( self       : Optional[Any]     ,   a_       : Optional[int]     ,   a_       : Union[str, Any]     ,   a_       : Dict     ,   a_       : List[str]     ,   a_       : Union[str, Any]     ,   a_       : Tuple     ,   a_       : int     ,   a_       : str )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.num_labels
      UpperCAmelCase_						:    List[Any]    					=  LayoutLMForSequenceClassification(a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    str    					=  model(
          a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels) )




 def   a      ( self       : str     ,   a_       : Any     ,   a_       : Any     ,   a_       : List[Any]     ,   a_       : Any     ,   a_       : str     ,   a_       : Union[str, Any]     ,   a_       : List[Any]     ,   a_       : Optional[int] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.num_labels
      UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMForTokenClassification(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels) )




 def   a      ( self       : List[str]     ,   a_       : Any     ,   a_       : List[str]     ,   a_       : List[Any]     ,   a_       : Dict     ,   a_       : int     ,   a_       : Union[str, Any]     ,   a_       : Dict     ,   a_       : Optional[int] )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMForQuestionAnswering(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    List[Any]    					=  model(
          a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   start_positions=a_     ,   end_positions=a_     ,   )
      self.parent.assertEqual(result.start_logits.shape     ,   (self.batch_size, self.seq_length) )
      self.parent.assertEqual(result.end_logits.shape     ,   (self.batch_size, self.seq_length) )






 def   a      ( self       : int )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,
      )						:    Tuple    					=  config_and_inputs
      UpperCAmelCase_						:    List[str]    					=  {
          """input_ids""": input_ids,
          """bbox""": bbox,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict



@require_torch
class 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_  , unittest.TestCase						):
 """simple docstring"""




 UpperCamelCase_						:     List[Any]				   =    (
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 UpperCamelCase_						:     Optional[int]				   =    (
     {
         """document-question-answering""": LayoutLMForQuestionAnswering,
         """feature-extraction""": LayoutLMModel,
         """fill-mask""": LayoutLMForMaskedLM,
         """text-classification""": LayoutLMForSequenceClassification,
         """token-classification""": LayoutLMForTokenClassification,
         """zero-shot""": LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCamelCase_						:     List[str]				   =    True




 def   a      ( self       : int )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMModelTester(self )
      UpperCAmelCase_						:    Tuple    					=  ConfigTester(self     ,   config_class=a_     ,   hidden_size=37 )




 def   a      ( self       : Optional[Any] )->					Optional[int]:



      """simple docstring"""

      self.config_tester.run_common_tests()




 def   a      ( self       : Optional[Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a_ )




 def   a      ( self       : Optional[int] )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCAmelCase_						:    Union[str, Any]    					=  type
           self.model_tester.create_and_check_model(*a_ )




 def   a      ( self       : List[Any] )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*a_ )




 def   a      ( self       : List[str] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*a_ )




 def   a      ( self       : Union[str, Any] )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*a_ )




 def   a      ( self       : Any )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*a_ )






 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
 def   a      ( self       : List[str] )->					List[str]:



      """simple docstring"""

      pass




def 			A_			(					)     ->							int:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    str    					=  torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						, device=lowercase      )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     UpperCAmelCase_						:    Tuple    					=  torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						, device=lowercase      )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels



@require_torch
class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 @slow
 def   a      ( self       : str )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(a_ )

      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    List[str]    					=  prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCAmelCase_						:    Union[str, Any]    					=  model(input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_ )

      # test the sequence output on [0, :3, :3]
      UpperCAmelCase_						:    str    					=  torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]     ,   device=a_     ,   )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,   a_     ,   atol=1E-3 ) )

      # test the pooled output on [1, :3]
      UpperCAmelCase_						:    str    					=  torch.tensor([-0.6580, -0.0214, 0.8552]     ,   device=a_ )

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]     ,   a_     ,   atol=1E-3 ) )




 @slow
 def   a      ( self       : str )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=2 ).to(
          a_ )

      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    str    					=  prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCAmelCase_						:    Dict    					=  model(
          input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=torch.tensor([1, 1]     ,   device=a_ )     ,   )

      # test whether we get a loss as a scalar
      UpperCAmelCase_						:    Optional[int]    					=  outputs.loss
      UpperCAmelCase_						:    List[Any]    					=  torch.Size([] )
      self.assertEqual(loss.shape     ,   a_ )

      # test the shape of the logits
      UpperCAmelCase_						:    int    					=  outputs.logits
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 2) )
      self.assertEqual(logits.shape     ,   a_ )




 @slow
 def   a      ( self       : List[Any] )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=13 ).to(
          a_ )

      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    Optional[int]    					=  prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCAmelCase_						:    Optional[int]    					=  model(
          input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )

      # test the loss calculation to be around 2.65
      # expected_loss = torch.tensor(2.65, device=torch_device)

      # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
      # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

      # test the shape of the logits
      UpperCAmelCase_						:    Optional[Any]    					=  outputs.logits
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 25, 13) )
      self.assertEqual(logits.shape     ,   a_ )






 @slow
 def   a      ( self       : Tuple )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(a_ )

      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    Optional[Any]    					=  prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCAmelCase_						:    Union[str, Any]    					=  model(input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_ )

      # test the shape of the logits
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 25) )
      self.assertEqual(outputs.start_logits.shape     ,   a_ )
      self.assertEqual(outputs.end_logits.shape     ,   a_ )



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



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




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

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

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

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

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

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

