code: 
  
  """simple docstring"""
  from __future__ import annotations
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[Any]						=						{1}
        UpperCAmelCase__ :       Optional[int]						=						[1]
        for _ in range(2 ,  depth + 1						):
              UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
              UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
              UpperCAmelCase__ :       Dict						=						[]
              for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                    coefficients.add(x + y						)
                    previous_coefficients.append(x + y						)
        return coefficients
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[str]						=						set()
        for number in unique_coefficients:
              UpperCAmelCase__ :       int						=						2
              UpperCAmelCase__ :       List[Any]						=						number
              while divisor**2 <= copy_number:
                    UpperCAmelCase__ :       Tuple						=						0
                    while copy_number % divisor == 0:
                          copy_number //= divisor
                          multiplicity += 1
                    if multiplicity >= 2:
                          non_squarefrees.add(lowerCAmelCase						)
                          break
                    divisor += 1
  
        return unique_coefficients.difference(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase = 51						)  ->   int:
        UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
        return sum(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
      import numpy as np
      import tensorflow as tf
  
      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import MobileViTImageProcessor
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.config_class(**self.inputs_dict  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""hidden_sizes"""  )  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""neck_hidden_sizes"""  )  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""num_attention_heads"""  )  )
  
  
  
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=13       ,					_lowerCamelCase=32       ,					_lowerCamelCase=2       ,					_lowerCamelCase=3       ,					_lowerCamelCase=640       ,					_lowerCamelCase=4       ,					_lowerCamelCase="silu"       ,					_lowerCamelCase=3       ,					_lowerCamelCase=32       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase=10       ,					_lowerCamelCase=None       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						parent
          UpperCAmelCase__ :       str						=						batch_size
          UpperCAmelCase__ :       Dict						=						image_size
          UpperCAmelCase__ :       Dict						=						patch_size
          UpperCAmelCase__ :       str						=						num_channels
          UpperCAmelCase__ :       Optional[int]						=						last_hidden_size
          UpperCAmelCase__ :       Dict						=						num_attention_heads
          UpperCAmelCase__ :       List[str]						=						hidden_act
          UpperCAmelCase__ :       Union[str, Any]						=						conv_kernel_size
          UpperCAmelCase__ :       str						=						output_stride
          UpperCAmelCase__ :       List[Any]						=						hidden_dropout_prob
          UpperCAmelCase__ :       Dict						=						attention_probs_dropout_prob
          UpperCAmelCase__ :       str						=						classifier_dropout_prob
          UpperCAmelCase__ :       Dict						=						use_labels
          UpperCAmelCase__ :       Dict						=						is_training
          UpperCAmelCase__ :       List[str]						=						num_labels
          UpperCAmelCase__ :       Tuple						=						initializer_range
          UpperCAmelCase__ :       List[str]						=						scope
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
          UpperCAmelCase__ :       Any						=						None
          UpperCAmelCase__ :       List[Any]						=						None
          if self.use_labels:
                UpperCAmelCase__ :       Tuple						=						ids_tensor([self.batch_size]       ,					self.num_labels  )
                UpperCAmelCase__ :       Optional[int]						=						ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels  )
  
          UpperCAmelCase__ :       List[Any]						=						self.get_config()
  
          return config, pixel_values, labels, pixel_labels
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return MobileViTConfig(
              image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					num_attention_heads=self.num_attention_heads       ,					hidden_act=self.hidden_act       ,					conv_kernel_size=self.conv_kernel_size       ,					output_stride=self.output_stride       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					classifier_dropout_prob=self.classifier_dropout_prob       ,					initializer_range=self.initializer_range       ,					)
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						TFMobileViTModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       List[str]						=						model(_lowerCamelCase       ,					training=_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						self.image_size // self.output_stride
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,					(self.batch_size, self.last_hidden_size, expected_height, expected_width)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.num_labels
          UpperCAmelCase__ :       Union[str, Any]						=						TFMobileViTForImageClassification(_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					labels=_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.num_labels
          UpperCAmelCase__ :       Dict						=						TFMobileViTForSemanticSegmentation(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						self.image_size // self.output_stride
  
          UpperCAmelCase__ :       List[Any]						=						model(_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_labels, expected_height, expected_width)  )
  
          UpperCAmelCase__ :       Any						=						model(_lowerCamelCase       ,					labels=_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_labels, expected_height, expected_width)  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.prepare_config_and_inputs()
          UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[int]						=						config_and_inputs
          UpperCAmelCase__ :       List[str]						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (
        (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
        if is_tf_available()
        else ()
    )
    SCREAMING_SNAKE_CASE							=     (
        {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
        if is_tf_available()
        else {}
    )
  
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						TFMobileViTModelTester(self  )
          UpperCAmelCase__ :       Dict						=						TFMobileViTConfigTester(self       ,					config_class=_lowerCamelCase       ,					has_text_modality=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          self.config_tester.run_common_tests()
  
  
  
    @unittest.skip(reason="""MobileViT does not use inputs_embeds"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    @unittest.skip(reason="""MobileViT does not support input and output embeddings"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    @unittest.skip(reason="""MobileViT does not output attentions"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       Tuple						=						model_class(_lowerCamelCase  )
                UpperCAmelCase__ :       Optional[int]						=						inspect.signature(model.call  )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCAmelCase__ :       Optional[int]						=						[*signature.parameters.keys()]
  
                UpperCAmelCase__ :       Any						=						["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       str						=						model_class(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Any						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
  
                UpperCAmelCase__ :       Tuple						=						outputs.hidden_states
  
                UpperCAmelCase__ :       Union[str, Any]						=						5
                self.assertEqual(len(_lowerCamelCase  )       ,					_lowerCamelCase  )
  
                # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                # with the width and height being successively divided by 2.
                UpperCAmelCase__ :       str						=						2
                for i in range(len(_lowerCamelCase  )  ):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]  )       ,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,					)
                      divisor *= 2
  
                self.assertEqual(self.model_tester.output_stride       ,					divisor // 2  )
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       Optional[Any]						=						True
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__ :       str						=						True
  
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase  )
  
  
  
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0       ,					reason="""TF does not support backprop for grouped convolutions on CPU."""       ,					)
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          super().test_dataset_conversion()
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=2e-1       ,					_lowerCamelCase=2e-1  ):
  
  
          """simple docstring"""
  
          self.assertTrue(np.allclose(_lowerCamelCase       ,					_lowerCamelCase       ,					atol=_lowerCamelCase       ,					rtol=_lowerCamelCase  )  )
  
  
  
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0       ,					reason="""TF does not support backprop for grouped convolutions on CPU."""       ,					)
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
                if model_class.__name__ != "TFMobileViTModel":
                      UpperCAmelCase__ :       Optional[int]						=						model_class(_lowerCamelCase  )
                      if getattr(_lowerCamelCase       ,					"""hf_compute_loss"""       ,					_lowerCamelCase  ):
                            super().test_keras_fit()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       str						=						self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                # set an ignore index to correctly test the masked loss used in
                # `TFMobileViTForSemanticSegmentation`.
                if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                      UpperCAmelCase__ :       List[Any]						=						5
  
                UpperCAmelCase__ :       Any						=						model_class(_lowerCamelCase  )
                if getattr(_lowerCamelCase       ,					"""hf_compute_loss"""       ,					_lowerCamelCase  ):
                      # The number of elements in the loss should be the same as the number of elements in the label
                      UpperCAmelCase__ :       Optional[int]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Optional[Any]						=						prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()       ,					reverse=_lowerCamelCase  )[0]
                      ]
                      UpperCAmelCase__ :       Union[str, Any]						=						added_label.shape.as_list()[:1]
  
                      # Test that model correctly compute the loss with kwargs
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Any						=						{"""input_ids""", """pixel_values""", """input_features"""}
                      UpperCAmelCase__ :       Optional[Any]						=						possible_input_names.intersection(set(_lowerCamelCase  )  ).pop()
                      UpperCAmelCase__ :       Optional[int]						=						prepared_for_class.pop(_lowerCamelCase  )
  
                      UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					**_lowerCamelCase  )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
                      # Test that model correctly compute the loss when we mask some positions
                      UpperCAmelCase__ :       int						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						{"""input_ids""", """pixel_values""", """input_features"""}
                      UpperCAmelCase__ :       Optional[Any]						=						possible_input_names.intersection(set(_lowerCamelCase  )  ).pop()
                      UpperCAmelCase__ :       List[Any]						=						prepared_for_class.pop(_lowerCamelCase  )
                      if "labels" in prepared_for_class:
                            UpperCAmelCase__ :       int						=						prepared_for_class["""labels"""].numpy()
                            if len(labels.shape  ) > 1 and labels.shape[1] != 1:
                                  # labels[0] = -100
                                  UpperCAmelCase__ :       int						=						tf.convert_to_tensor(_lowerCamelCase  )
                                  UpperCAmelCase__ :       Dict						=						model(_lowerCamelCase       ,					**_lowerCamelCase  )[0]
                                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
                                  self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )
  
                  # Test that model correctly compute the loss with a dict
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						model(_lowerCamelCase  )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
                      # Test that model correctly compute the loss with a tuple
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
  
                      # Get keys that were added with the _prepare_for_class function
                      UpperCAmelCase__ :       List[str]						=						prepared_for_class.keys() - inputs_dict.keys()
                      UpperCAmelCase__ :       List[str]						=						inspect.signature(model.call  ).parameters
                      UpperCAmelCase__ :       str						=						list(signature.keys()  )
  
                      # Create a dictionary holding the location of the tensors in the tuple
                      UpperCAmelCase__ :       Any						=						{0: input_name}
                      for label_key in label_keys:
                            UpperCAmelCase__ :       Dict						=						signature_names.index(_lowerCamelCase  )
                            UpperCAmelCase__ :       Any						=						label_key
                      UpperCAmelCase__ :       Tuple						=						sorted(tuple_index_mapping.items()  )
                      # Initialize a list with their default values, update the values and convert to a tuple
                      UpperCAmelCase__ :       List[str]						=						[]
  
                      for name in signature_names:
                            if name != "kwargs":
                                  list_input.append(signature[name].default  )
  
                      for index, value in sorted_tuple_index_mapping:
                            UpperCAmelCase__ :       int						=						prepared_for_class[value]
  
                      UpperCAmelCase__ :       Union[str, Any]						=						tuple(_lowerCamelCase  )
  
                      # Send to model
                      UpperCAmelCase__ :       Union[str, Any]						=						model(tuple_input[:-1]  )[0]
  
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__ :       List[str]						=						TFMobileViTModel.from_pretrained(_lowerCamelCase  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  def 				a__	(	)  ->   int:
        UpperCAmelCase__ :       Dict						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        return image
  
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       Optional[Any]						=						MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""  )
          UpperCAmelCase__ :       Dict						=						prepare_img()
          UpperCAmelCase__ :       List[str]						=						image_processor(images=_lowerCamelCase       ,					return_tensors="""tf"""  )
  
          # forward pass
          UpperCAmelCase__ :       str						=						model(**_lowerCamelCase       ,					training=_lowerCamelCase  )
  
          # verify the logits
          UpperCAmelCase__ :       Union[str, Any]						=						tf.TensorShape((1, 1000)  )
          self.assertEqual(outputs.logits.shape       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						tf.constant([-1.9_364, -1.2_327, -0.4_653]  )
  
          tf.debugging.assert_near(outputs.logits[0, :3]       ,					_lowerCamelCase       ,					atol=1e-4       ,					rtol=1e-04  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       Dict						=						MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       List[str]						=						prepare_img()
          UpperCAmelCase__ :       List[str]						=						image_processor(images=_lowerCamelCase       ,					return_tensors="""tf"""  )
  
          # forward pass
          UpperCAmelCase__ :       Tuple						=						model(inputs.pixel_values       ,					training=_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						outputs.logits
  
          # verify the logits
          UpperCAmelCase__ :       Tuple						=						tf.TensorShape((1, 21, 32, 32)  )
          self.assertEqual(logits.shape       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Tuple						=						tf.constant(
              [
                  [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                  [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                  [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
              ]  )
  
          tf.debugging.assert_near(logits[0, :3, :3, :3]       ,					_lowerCamelCase       ,					rtol=1e-4       ,					atol=1e-4  )
  
  