





'''simple docstring'''




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
  from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=2						,     _UpperCAmelCase=32						,     _UpperCAmelCase=16						,     _UpperCAmelCase=3						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=32						,     _UpperCAmelCase=4						,     _UpperCAmelCase=[0, 1, 2, 3]						,     _UpperCAmelCase=4						,     _UpperCAmelCase=37						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=3						,     _UpperCAmelCase=[1, 384, 24, 24]						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     ):

          '''simple docstring'''






          __A						:			str								=	parent
          __A						:			str								=	batch_size
          __A						:			Tuple								=	image_size
          __A						:			Dict								=	patch_size
          __A						:			int								=	num_channels
          __A						:			Tuple								=	is_training
          __A						:			Union[str, Any]								=	use_labels
          __A						:			Dict								=	hidden_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			Union[str, Any]								=	backbone_out_indices
          __A						:			List[Any]								=	num_attention_heads
          __A						:			Union[str, Any]								=	intermediate_size
          __A						:			List[str]								=	hidden_act
          __A						:			Optional[Any]								=	hidden_dropout_prob
          __A						:			List[Any]								=	attention_probs_dropout_prob
          __A						:			Tuple								=	initializer_range
          __A						:			Optional[Any]								=	num_labels
          __A						:			List[str]								=	backbone_featmap_shape
          __A						:			Tuple								=	scope
          __A						:			List[Any]								=	is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          __A						:			int								=	(image_size // patch_size) ** 2
          __A						:			Dict								=	num_patches + 1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          __A						:			Any								=	None
          if self.use_labels:
               __A						:			Union[str, Any]								=	ids_tensor([self.batch_size, self.image_size, self.image_size]						,     self.num_labels)

          __A						:			Optional[Any]								=	self.get_config()

          return config, pixel_values, labels


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	{
              'global_padding': 'same',
              'layer_type': 'bottleneck',
              'depths': [3, 4, 9],
              'out_features': ['stage1', 'stage2', 'stage3'],
              'embedding_dynamic_padding': True,
              'hidden_sizes': [96, 192, 384, 768],
              'num_groups': 2,
          }

          return DPTConfig(
              image_size=self.image_size						,     patch_size=self.patch_size						,     num_channels=self.num_channels						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     backbone_out_indices=self.backbone_out_indices						,     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						,     is_decoder=_UpperCAmelCase						,     initializer_range=self.initializer_range						,     is_hybrid=self.is_hybrid						,     backbone_config=_UpperCAmelCase						,     backbone_featmap_shape=self.backbone_featmap_shape						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	DPTModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			str								=	model(_UpperCAmelCase)
          self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.num_labels
          __A						:			str								=	DPTForDepthEstimation(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			List[Any]								=	model(_UpperCAmelCase)
          self.parent.assertEqual(result.predicted_depth.shape						,     (self.batch_size, self.image_size, self.image_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[Any]								=	self.num_labels
          __A						:			int								=	DPTForSemanticSegmentation(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			Optional[Any]								=	model(_UpperCAmelCase						,     labels=_UpperCAmelCase)
          self.parent.assertEqual(
              result.logits.shape						,     (self.batch_size, self.num_labels, self.image_size, self.image_size))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.prepare_config_and_inputs()
          __A			,__A			,__A						:			int								=	config_and_inputs
          __A						:			int								=	{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     lowerCAmelCase		      =	(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	DPTModelTester(self)
          __A						:			List[str]								=	ConfigTester(self						,     config_class=_UpperCAmelCase						,     has_text_modality=_UpperCAmelCase						,     hidden_size=37)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     @unittest.skip(reason='DPT does not use inputs_embeds')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			List[Any]								=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A						:			Any								=	model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()						,     (nn.Module))
               __A						:			str								=	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase						,     nn.Linear))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			int								=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A						:			Optional[int]								=	model_class(_UpperCAmelCase)
               __A						:			str								=	inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A						:			List[str]								=	[*signature.parameters.keys()]

               __A						:			Tuple								=	['pixel_values']
               self.assertListEqual(arg_names[:1]						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                    continue

               __A			,__A						:			Optional[Any]								=	self.model_tester.prepare_config_and_inputs_for_common()
               __A						:			Dict								=	True

               if model_class in get_values(_UpperCAmelCase):
                    continue

               __A						:			str								=	model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.train()
               __A						:			List[Any]								=	self._prepare_for_class(_UpperCAmelCase						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase)
               __A						:			Dict								=	model(**_UpperCAmelCase).loss
               loss.backward()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                    continue

               __A			,__A						:			int								=	self.model_tester.prepare_config_and_inputs_for_common()
               __A						:			str								=	False
               __A						:			Optional[int]								=	True

               if model_class in get_values(_UpperCAmelCase) or not model_class.supports_gradient_checkpointing:
                    continue
               __A						:			Dict								=	model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.gradient_checkpointing_enable()
               model.train()
               __A						:			List[str]								=	self._prepare_for_class(_UpperCAmelCase						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase)
               __A						:			str								=	model(**_UpperCAmelCase).loss
               loss.backward()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			Any								=	self.model_tester.prepare_config_and_inputs_for_common()

          __A						:			Optional[int]								=	_config_zero_init(_UpperCAmelCase)
          for model_class in self.all_model_classes:
               __A						:			Dict								=	model_class(config=_UpperCAmelCase)
               # Skip the check for the backbone
               __A						:			Dict								=	[]
               for name, module in model.named_modules():
                    if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                         __A						:			List[Any]								=	[F'{name}.{key}' for key in module.state_dict().keys()]
                         break

               for name, param in model.named_parameters():
                    if param.requires_grad:
                         if name in backbone_params:
                              continue
                         self.assertIn(
                             ((param.data.mean() * 1e9).round() / 1e9).item()						,     [0.0, 1.0]						,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,     )


     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
               __A						:			List[str]								=	DPTModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			List[Any]								=	self.model_tester.prepare_config_and_inputs_for_common()
          __A						:			str								=	'add'
          with self.assertRaises(_UpperCAmelCase):
               __A						:			int								=	DPTForDepthEstimation(_UpperCAmelCase)






def 	_lowerCAmelCase      (  )					->					List[str]:
     __A						:			Union[str, Any]								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
@slow
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas')
          __A						:			Optional[int]								=	DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas').to(_UpperCAmelCase)

          __A						:			Dict								=	prepare_img()
          __A						:			Tuple								=	image_processor(images=_UpperCAmelCase						,     return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A						:			int								=	model(**_UpperCAmelCase)
               __A						:			Optional[int]								=	outputs.predicted_depth

          # verify the predicted depth
          __A						:			Tuple								=	torch.Size((1, 384, 384))
          self.assertEqual(predicted_depth.shape						,     _UpperCAmelCase)

          __A						:			List[Any]								=	torch.tensor(
              [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(_UpperCAmelCase)

          self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						,     _UpperCAmelCase						,     atol=1e-4))






'''simple docstring'''




from math import pi, sqrt, tan






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('surface_area_cube() only accepts non-negative values'      )
     return 6 * side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					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 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_sphere() only accepts non-negative values'      )
     return 4 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_hemisphere() only accepts non-negative values'      )
     return 3 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					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 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              'surface_area_conical_frustum() only accepts non-negative values'      )
     __A						:			Union[str, Any]								=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius < 0 or height < 0:
          raise ValueError('surface_area_cylinder() only accepts non-negative values'      )
     return 2 * pi * radius * (height + radius)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					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(__snake_case			,							2      ) * torus_radius * tube_radius






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if length < 0 or width < 0:
          raise ValueError('area_rectangle() only accepts non-negative values'      )
     return length * width






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('area_square() only accepts non-negative values'      )
     return side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_triangle() only accepts non-negative values'      )
     return (base * height) / 2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					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						:			Tuple								=	sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)      )
     return area






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_parallelogram() only accepts non-negative values'      )
     return base * height






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					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 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('area_circle() only accepts non-negative values'      )
     return pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					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 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					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 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			float      )					->					float:
     if not isinstance(__snake_case			,							__snake_case      ) 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) = }""")