


from math import pi, sqrt, tan
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if side_length < 0:
              raise ValueError("surface_area_cube() only accepts non-negative values"						)
       return 6 * side_length**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if length < 0 or breadth < 0 or height < 0:
              raise ValueError("surface_area_cuboid() only accepts non-negative values"						)
       return 2 * ((length * breadth) + (breadth * height) + (length * height))
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if radius < 0:
              raise ValueError("surface_area_sphere() only accepts non-negative values"						)
       return 4 * pi * radius**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if radius < 0:
              raise ValueError("surface_area_hemisphere() only accepts non-negative values"						)
       return 3 * pi * radius**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if radius < 0 or height < 0:
              raise ValueError("surface_area_cone() only accepts non-negative values"						)
       return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if radius_a < 0 or radius_a < 0 or height < 0:
              raise ValueError(
                  "surface_area_conical_frustum() only accepts non-negative values"						)
       _A      :       Optional[int]				    = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
       return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if radius < 0 or height < 0:
              raise ValueError("surface_area_cylinder() only accepts non-negative values"						)
       return 2 * pi * radius * (height + radius)
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if torus_radius < 0 or tube_radius < 0:
              raise ValueError("surface_area_torus() only accepts non-negative values"						)
       if torus_radius < tube_radius:
              raise ValueError(
                  "surface_area_torus() does not support spindle or self intersecting tori"						)
       return 4 * pow(UpperCamelCase__				,							2						) * torus_radius * tube_radius
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if length < 0 or width < 0:
              raise ValueError("area_rectangle() only accepts non-negative values"						)
       return length * width
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if side_length < 0:
              raise ValueError("area_square() only accepts non-negative values"						)
       return side_length**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if base < 0 or height < 0:
              raise ValueError("area_triangle() only accepts non-negative values"						)
       return (base * height) / 2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if sidea < 0 or sidea < 0 or sidea < 0:
              raise ValueError("area_triangle_three_sides() only accepts non-negative values"						)
       elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
              raise ValueError("Given three sides do not form a triangle"						)
       _A      :       int				    = (sidea + sidea + sidea) / 2
       _A      :       Any				    = sqrt(
           semi_perimeter
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)						)
       return area
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if base < 0 or height < 0:
              raise ValueError("area_parallelogram() only accepts non-negative values"						)
       return base * height
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if basea < 0 or basea < 0 or height < 0:
              raise ValueError("area_trapezium() only accepts non-negative values"						)
       return 1 / 2 * (basea + basea) * height
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if radius < 0:
              raise ValueError("area_circle() only accepts non-negative values"						)
       return pi * radius**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if radius_x < 0 or radius_y < 0:
              raise ValueError("area_ellipse() only accepts non-negative values"						)
       return pi * radius_x * radius_y
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       if diagonal_a < 0 or diagonal_a < 0:
              raise ValueError("area_rhombus() only accepts non-negative values"						)
       return 1 / 2 * diagonal_a * diagonal_a
def 							_UpperCAmelCase				(UpperCamelCase__     :  int				,							UpperCamelCase__     :  float						):
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						) or sides < 3:
              raise ValueError(
                  "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"						)
       elif length < 0:
              raise ValueError(
                  "area_reg_polygon() only accepts non-negative values as \
length of a side"						)
       return (sides * length**2) / (4 * tan(pi / sides						))
       return (sides * length**2) / (4 * tan(pi / sides						))


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

    print('[DEMO] Areas of various geometric shapes: \n')
    print(f"Rectangle: {area_rectangle(10, 20) = }")
    print(f"Square: {area_square(10) = }")
    print(f"Triangle: {area_triangle(10, 10) = }")
    print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
    print(f"Parallelogram: {area_parallelogram(10, 20) = }")
    print(f"Rhombus: {area_rhombus(10, 20) = }")
    print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
    print(f"Circle: {area_circle(20) = }")
    print(f"Ellipse: {area_ellipse(10, 20) = }")
    print('\nSurface Areas of various geometric shapes: \n')
    print(f"Cube: {surface_area_cube(20) = }")
    print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
    print(f"Sphere: {surface_area_sphere(20) = }")
    print(f"Hemisphere: {surface_area_hemisphere(20) = }")
    print(f"Cone: {surface_area_cone(10, 20) = }")
    print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
    print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
    print(f"Torus: {surface_area_torus(20, 10) = }")
    print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
    print(f"Square: {area_reg_polygon(4, 10) = }")
    print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertModel,
      )



class 		UpperCamelCase__  :
   def __init__(self :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					List[str]=1_3					,				snake_case_ :					Tuple=7					,				snake_case_ :					List[Any]=True					,				snake_case_ :					List[Any]=True					,				snake_case_ :					Dict=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					str=9_9					,				snake_case_ :					Dict=6_4					,				snake_case_ :					Any=3_2					,				snake_case_ :					str=5					,				snake_case_ :					int=4					,				snake_case_ :					List[Any]=3_7					,				snake_case_ :					Any="gelu"					,				snake_case_ :					Dict=0.1					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					str=5_1_2					,				snake_case_ :					Any=1_6					,				snake_case_ :					str=2					,				snake_case_ :					int=0.02					,				snake_case_ :					Union[str, Any]=3					,				snake_case_ :					Optional[Any]=4					,				snake_case_ :					List[Any]=None					,				):
    __a :  Any						=  parent
    __a :  Optional[int]						=  batch_size
    __a :  Any						=  seq_length
    __a :  int						=  is_training
    __a :  Optional[int]						=  use_input_mask
    __a :  List[Any]						=  use_token_type_ids
    __a :  Dict						=  use_labels
    __a :  Tuple						=  vocab_size
    __a :  str						=  hidden_size
    __a :  List[Any]						=  embedding_size
    __a :  List[Any]						=  num_hidden_layers
    __a :  str						=  num_attention_heads
    __a :  str						=  intermediate_size
    __a :  Union[str, Any]						=  hidden_act
    __a :  Optional[Any]						=  hidden_dropout_prob
    __a :  Tuple						=  attention_probs_dropout_prob
    __a :  Union[str, Any]						=  max_position_embeddings
    __a :  Any						=  type_vocab_size
    __a :  int						=  type_sequence_label_size
    __a :  int						=  initializer_range
    __a :  int						=  num_labels
    __a :  Union[str, Any]						=  num_choices
    __a :  Dict						=  scope
   def       lowerCAmelCase			(self :					str   ):
    __a :  int						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  List[Any]						=  None
    if self.use_input_mask:
     __a :  Dict						=  random_attention_mask([self.batch_size, self.seq_length]   )

    __a :  Optional[Any]						=  None
    if self.use_token_type_ids:
     __a :  List[Any]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size   )

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

    __a :  Optional[int]						=  self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       lowerCAmelCase			(self :					int   ):
    return MobileBertConfig(
        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					,				embedding_size=self.embedding_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					,				is_decoder=snake_case_					,				initializer_range=self.initializer_range					,				)
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[str]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Any   ):
    __a :  Any						=  MobileBertModel(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[str]						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_   )

    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       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					str					,				snake_case_ :					List[Any]   ):
    __a :  str						=  MobileBertForMaskedLM(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict   ):
    __a :  Optional[Any]						=  MobileBertForNextSentencePrediction(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  int						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]   ):
    __a :  str						=  MobileBertForPreTraining(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Union[str, Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				next_sentence_label=snake_case_					,				)
    self.parent.assertEqual(result.prediction_logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
    self.parent.assertEqual(result.seq_relationship_logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					str					,				snake_case_ :					str   ):
    __a :  str						=  MobileBertForQuestionAnswering(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Optional[Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				start_positions=snake_case_					,				end_positions=snake_case_					,				)
    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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					List[Any]					,				snake_case_ :					str					,				snake_case_ :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[int]   ):
    __a :  Any						=  self.num_labels
    __a :  Union[str, Any]						=  MobileBertForSequenceClassification(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]   ):
    __a :  Union[str, Any]						=  self.num_labels
    __a :  str						=  MobileBertForTokenClassification(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Any						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					List[str]					,				snake_case_ :					Any					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Union[str, Any]   ):
    __a :  Union[str, Any]						=  self.num_choices
    __a :  List[str]						=  MobileBertForMultipleChoice(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[Any]						=  input_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  int						=  token_type_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  str						=  input_mask.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  Any						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Optional[Any]						=  self.prepare_config_and_inputs()
    (
        (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       
    ) :  int						=  config_and_inputs
    __a :  Union[str, Any]						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
    return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      (
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE      :							Any	       =      (
       {
           "feature-extraction": MobileBertModel,
           "fill-mask": MobileBertForMaskedLM,
           "question-answering": MobileBertForQuestionAnswering,
           "text-classification": MobileBertForSequenceClassification,
           "token-classification": MobileBertForTokenClassification,
           "zero-shot": MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      True
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[Any]					,				snake_case_ :					Union[str, Any]=False   ):
    __a :  List[str]						=  super()._prepare_for_class(snake_case_					,				snake_case_					,				return_labels=snake_case_   )

    if return_labels:
     if model_class in get_values(snake_case_   ):
      __a :  Optional[int]						=  torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)					,				dtype=torch.long					,				device=snake_case_   )
      __a :  int						=  torch.zeros(
          self.model_tester.batch_size					,				dtype=torch.long					,				device=snake_case_   )
    return inputs_dict
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  List[Any]						=  MobileBertModelTester(self   )
    __a :  int						=  ConfigTester(self					,				config_class=snake_case_					,				hidden_size=3_7   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_model(*snake_case_   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  Any						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  str						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_   )







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_   )





def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 return torch.tensor(
     lowerCAmelCase__  ,   dtype=torch.long  ,   device=lowerCAmelCase__  ,   )


lowercase__       =1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @slow
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Dict						=  MobileBertModel.from_pretrained('''google/mobilebert-uncased'''   ).to(snake_case_   )
    __a :  Tuple						=  _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
    with torch.no_grad():
     __a :  str						=  model(snake_case_   )[0]
    __a :  List[Any]						=  torch.Size((1, 9, 5_1_2)   )
    self.assertEqual(output.shape					,				snake_case_   )
    __a :  Union[str, Any]						=  torch.tensor(
        [
            [
                [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
                [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
                [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
            ]
        ]					,				device=snake_case_					,				)

    # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
    # ~1 difference, it's therefore not a good idea to measure using addition.
    # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
    # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
    __a :  List[str]						=  torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
    __a :  Any						=  torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

    self.assertTrue(lower_bound and upper_bound   )
