

"""simple docstring"""
class 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						data
        UpperCAmelCase__ :       int
        UpperCAmelCase__ :       Node




def 				a__	(	lowerCAmelCase						)  ->   None:
      UpperCAmelCase__ :       Any						=						0
      UpperCAmelCase__ :       Union[str, Any]						=						x




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   None:
      UpperCAmelCase__				,		UpperCAmelCase__ :       int						=						find_set(lowerCAmelCase						), find_set(lowerCAmelCase						)
      if x == y:
            return

      elif x.rank > y.rank:
            UpperCAmelCase__ :       int						=						x
      else:
            UpperCAmelCase__ :       int						=						y
            if x.rank == y.rank:
                  y.rank += 1




def 				a__	(	lowerCAmelCase						)  ->   Node:
      if x != x.parent:
            UpperCAmelCase__ :       str						=						find_set(x.parent						)
      return x.parent




def 				a__	(	lowerCAmelCase						)  ->   set:
      UpperCAmelCase__ :       List[Any]						=						({0, 1, 2}, {3, 4, 5})
      for s in sets:
            if node.data in s:
                  return s
      UpperCAmelCase__ :       Any						=						F"""{node.data} is not in {sets}"""
      raise ValueError(lowerCAmelCase						)




def 				a__	(	)  ->   None:
      UpperCAmelCase__ :       List[Any]						=						[Node(lowerCAmelCase						) for i in range(6						)]
      for v in vertex:
            make_set(lowerCAmelCase						)

      union_set(vertex[0] ,  vertex[1]						)
      union_set(vertex[1] ,  vertex[2]						)
      union_set(vertex[3] ,  vertex[4]						)
      union_set(vertex[3] ,  vertex[5]						)

      for nodea in vertex:
            for nodea in vertex:
                  if find_python_set(lowerCAmelCase						).isdisjoint(find_python_set(lowerCAmelCase						)						):
                        assert find_set(lowerCAmelCase						) != find_set(lowerCAmelCase						)
                  else:
                        assert find_set(lowerCAmelCase						) == find_set(lowerCAmelCase						)


if __name__ == "__main__":
    test_disjoint_set()




"""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  )

