





from __future__ import annotations

from math import pi, pow



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						float:
		if side_length < 0:
				raise ValueError('vol_cube() only accepts non-negative values'       )
		return pow(snake_case	,      3       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or radius < 0:
				raise ValueError('vol_spherical_cap() only accepts non-negative values'       )
		# Volume is 1/3 pi * height squared * (3 * radius - height)
		return 1 / 3 * pi * pow(snake_case	,      2       ) * (3 * radius - height)



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						float:
		if radius_a < 0 or radius_a < 0 or centers_distance < 0:
				raise ValueError('vol_spheres_intersect() only accepts non-negative values'       )
		if centers_distance == 0:
				return vol_sphere(min(snake_case	,      snake_case       )       )

		__lowercase					=    (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)
		__lowercase					=    (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)

		return vol_spherical_cap(snake_case	,      snake_case       ) + vol_spherical_cap(snake_case	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						float:

		if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
				raise ValueError(
				    'vol_spheres_union() only accepts non-negative values, non-zero radius'       )

		if centers_distance == 0:
				return vol_sphere(max(snake_case	,      snake_case       )       )

		return (
		    vol_sphere(snake_case       )
		    + vol_sphere(snake_case       )
		    - vol_spheres_intersect(snake_case	,      snake_case	,      snake_case       )
		)



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						float:
		if width < 0 or height < 0 or length < 0:
				raise ValueError('vol_cuboid() only accepts non-negative values'       )
		return float(width * height * length       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or area_of_base < 0:
				raise ValueError('vol_cone() only accepts non-negative values'       )
		return area_of_base * height / 3.0



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or radius < 0:
				raise ValueError('vol_right_circ_cone() only accepts non-negative values'       )
		return pi * pow(snake_case	,      2       ) * height / 3.0



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or area_of_base < 0:
				raise ValueError('vol_prism() only accepts non-negative values'       )
		return float(area_of_base * height       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or area_of_base < 0:
				raise ValueError('vol_pyramid() only accepts non-negative values'       )
		return area_of_base * height / 3.0



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						float:
		if radius < 0:
				raise ValueError('vol_sphere() only accepts non-negative values'       )
		# Volume is 4/3 * pi * radius cubed
		return 4 / 3 * pi * pow(snake_case	,      3       )



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						float:
		if radius < 0:
				raise ValueError('vol_hemisphere() only accepts non-negative values'       )
		# Volume is radius cubed * pi * 2/3
		return pow(snake_case	,      3       ) * pi * 2 / 3



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if height < 0 or radius < 0:
				raise ValueError('vol_circular_cylinder() only accepts non-negative values'       )
		# Volume is radius squared * height * pi
		return pow(snake_case	,      2       ) * height * pi



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						float:
		# Volume - (outer_radius squared - inner_radius squared) * pi * height
		if inner_radius < 0 or outer_radius < 0 or height < 0:
				raise ValueError(
				    'vol_hollow_circular_cylinder() only accepts non-negative values'       )
		if outer_radius <= inner_radius:
				raise ValueError('outer_radius must be greater than inner_radius'       )
		return pi * (pow(snake_case	,      2       ) - pow(snake_case	,      2       )) * height



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						float:
		# Volume is 1/3 * pi * height *
		#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
		if radius_a < 0 or radius_a < 0 or height < 0:
				raise ValueError('vol_conical_frustum() only accepts non-negative values'       )
		return (
		    1
		    / 3
		    * pi
		    * height
		    * (pow(snake_case	,      2       ) + pow(snake_case	,      2       ) + radius_a * radius_a)
		)



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						float:
		if torus_radius < 0 or tube_radius < 0:
				raise ValueError('vol_torus() only accepts non-negative values'       )
		return 2 * pow(snake_case	,      2       ) * torus_radius * pow(snake_case	,      2       )



def    SCREAMING_SNAKE_CASE							(			)		->						Any:
		print('Volumes:'       )
		print(F"Cube: {vol_cube(2       ) = }"       )  # = 8
		print(F"Cuboid: {vol_cuboid(2	,      2	,      2       ) = }"       )  # = 8
		print(F"Cone: {vol_cone(2	,      2       ) = }"       )  # ~= 1.33
		print(F"Right Circular Cone: {vol_right_circ_cone(2	,      2       ) = }"       )  # ~= 8.38
		print(F"Prism: {vol_prism(2	,      2       ) = }"       )  # = 4
		print(F"Pyramid: {vol_pyramid(2	,      2       ) = }"       )  # ~= 1.33
		print(F"Sphere: {vol_sphere(2       ) = }"       )  # ~= 33.5
		print(F"Hemisphere: {vol_hemisphere(2       ) = }"       )  # ~= 16.75
		print(F"Circular Cylinder: {vol_circular_cylinder(2	,      2       ) = }"       )  # ~= 25.1
		print(F"Torus: {vol_torus(2	,      2       ) = }"       )  # ~= 157.9
		print(F"Conical Frustum: {vol_conical_frustum(2	,      2	,      4       ) = }"       )  # ~= 58.6
		print(F"Spherical cap: {vol_spherical_cap(1	,      2       ) = }"       )  # ~= 5.24
		print(F"Spheres intersetion: {vol_spheres_intersect(2	,      2	,      1       ) = }"       )  # ~= 21.21
		print(F"Spheres union: {vol_spheres_union(2	,      2	,      1       ) = }"       )  # ~= 45.81
		print(
		    F"Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1	,      2	,      3       ) = }"       )  # ~= 28.3


if __name__ == "__main__":
		main()











import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    'block_out_channels': (32, 64, 128, 256),
			    'in_channels': 14,
			    'out_channels': 14,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1.0,
			    'out_block_type': 'OutConv1DBlock',
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    model(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'				,			subfolder='unet'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    model.config.in_channels
			__lowercase					=    16
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

			__lowercase					=    output.abs().sum()
			__lowercase					=    output.abs().max()

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    int      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

			self.assertIsNotNone(__lowerCamelCase      )
			__lowercase					=    torch.Size((inputs_dict['sample'].shape[0], 1)      )
			self.assertEqual(output.shape				,			__lowerCamelCase				,			'Input and output shapes do not match'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'in_channels': 14,
			    'out_channels': 14,
			    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
			    'up_block_types': [],
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': [32, 64, 128, 256],
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'use_timestep_embedding': True,
			    'freq_shift': 1.0,
			    'flip_sin_to_cos': False,
			    'time_embedding_type': 'positional',
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    value_function(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    value_function.config.in_channels
			__lowercase					=    14
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




