






'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor





class 	SCREAMING_SNAKE_CASE					:






			def __init__( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Tuple=13  ,						__SCREAMING_SNAKE_CASE      :			Dict=30  ,						__SCREAMING_SNAKE_CASE      :			Tuple=2  ,						__SCREAMING_SNAKE_CASE      :			Tuple=3  ,						__SCREAMING_SNAKE_CASE      :			Dict=True  ,						__SCREAMING_SNAKE_CASE      :			Dict=True  ,						__SCREAMING_SNAKE_CASE      :			Dict=32  ,						__SCREAMING_SNAKE_CASE      :			Tuple=5  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=4  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=37  ,						__SCREAMING_SNAKE_CASE      :			str="gelu"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.1  ,						__SCREAMING_SNAKE_CASE      :			Dict=0.1  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=10  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=0.02  ,						__SCREAMING_SNAKE_CASE      :			str=None  ,						)			->			Union[str, Any]:
							a_		:  Tuple											=      parent
							a_		:  List[Any]											=      batch_size
							a_		:  List[Any]											=      image_size
							a_		:  int											=      patch_size
							a_		:  Dict											=      num_channels
							a_		:  List[str]											=      is_training
							a_		:  int											=      use_labels
							a_		:  int											=      hidden_size
							a_		:  List[str]											=      num_hidden_layers
							a_		:  Tuple											=      num_attention_heads
							a_		:  Union[str, Any]											=      intermediate_size
							a_		:  List[Any]											=      hidden_act
							a_		:  str											=      hidden_dropout_prob
							a_		:  List[Any]											=      attention_probs_dropout_prob
							a_		:  Optional[int]											=      type_sequence_label_size
							a_		:  int											=      initializer_range
							a_		:  Union[str, Any]											=      scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							a_		:  List[Any]											=      (image_size // patch_size) ** 2
							a_		:  Any											=      num_patches + 1






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			int:
							a_		:  Tuple											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

							a_		:  Optional[int]											=      None
							if self.use_labels:
											a_		:  Dict											=      ids_tensor([self.batch_size]  ,						self.type_sequence_label_size					)

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

							return config, pixel_values, labels






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[str]:
							return ViTMSNConfig(
							    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  ,						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  ,						initializer_range=self.initializer_range  ,						)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							a_		:  Tuple											=      ViTMSNModel(config=__SCREAMING_SNAKE_CASE					)
							model.to(__SCREAMING_SNAKE_CASE					)
							model.eval()
							a_		:  Union[str, Any]											=      model(__SCREAMING_SNAKE_CASE					)
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Union[str, Any]:
							a_		:  Tuple											=      self.type_sequence_label_size
							a_		:  str											=      ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE					)
							model.to(__SCREAMING_SNAKE_CASE					)
							model.eval()
							a_		:  Optional[Any]											=      model(__SCREAMING_SNAKE_CASE  ,						labels=__SCREAMING_SNAKE_CASE					)
							print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''					)
							print('''Labels: {labels}'''					)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							a_		:  str											=      1
							a_		:  Any											=      ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE					)
							model.to(__SCREAMING_SNAKE_CASE					)
							model.eval()

							a_		:  str											=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
							a_		:  Tuple											=      model(__SCREAMING_SNAKE_CASE					)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Any:
							a_		:  str											=      self.prepare_config_and_inputs()
							a_				,			a_				,			a_		:  Tuple											=      config_and_inputs
							a_		:  int											=      {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_torch
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			snake_case__  =				(
			    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case__  =				False
			snake_case__  =				False
			snake_case__  =				False
			snake_case__  =				False






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Union[str, Any]:
							a_		:  Any											=      ViTMSNModelTester(self					)
							a_		:  List[Any]											=      ConfigTester(self  ,						config_class=__SCREAMING_SNAKE_CASE  ,						has_text_modality=__SCREAMING_SNAKE_CASE  ,						hidden_size=37					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							self.config_tester.run_common_tests()






			@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Union[str, Any]:
							pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Tuple:
							a_				,			a_		:  List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:  Optional[Any]											=      model_class(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)					)
											a_		:  Union[str, Any]											=      model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE  ,						nn.Linear					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_				,			a_		:  List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

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

											a_		:  str											=      ['''pixel_values''']
											self.assertListEqual(arg_names[:1]  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Optional[Any]											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							a_		:  Optional[int]											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE					)







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Tuple:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:  List[Any]											=      ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)





def 		_UpperCAmelCase   (							):
				a_		:  str											=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





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






			@cached_property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Union[str, Any]:
							return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''					) if is_vision_available() else None







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(2					)
							a_		:  List[str]											=      ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      self.default_image_processor
							a_		:  Optional[Any]											=      prepare_img()
							a_		:  Any											=      image_processor(images=__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					).to(__SCREAMING_SNAKE_CASE					)

							# forward pass
							with torch.no_grad():
											a_		:  Union[str, Any]											=      model(**__SCREAMING_SNAKE_CASE					)

							# verify the logits
							a_		:  List[str]											=      torch.Size((1, 1000)					)
							self.assertEqual(outputs.logits.shape  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  int											=      torch.tensor([-0.0803, -0.4454, -0.2375]					).to(__SCREAMING_SNAKE_CASE					)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						__SCREAMING_SNAKE_CASE  ,						atol=1e-4					)					)












'''simple docstring'''







from math import pi, sqrt, tan





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





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





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





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   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   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    '''surface_area_conical_frustum() only accepts non-negative values'''      )
				a_		:  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 		_UpperCAmelCase   (							__A							:   float						,		__A							:   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   (							__A							:   float						,		__A							:   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(__A						,		2      ) * torus_radius * tube_radius





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





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





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





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   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_		:  Optional[Any]											=      sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area





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





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





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   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   (							__A							:   float						,		__A							:   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   (							__A							:   int						,		__A							:   float      ):
				if not isinstance(__A						,		__A      ) 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) = }""")




