


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Dict       					=			VQModel
 __lowercase			:							Dict       					=			'sample'
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=(32, 32)			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       str        =       4
        lowercase__:       Tuple        =       3

        lowercase__:       Union[str, Any]        =       floats_tensor((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)

        return {"sample": image}
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return (3, 32, 32)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return (3, 32, 32)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       {
            'block_out_channels': [32, 64],
            'in_channels': 3,
            'out_channels': 3,
            'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
            'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
            'latent_channels': 3,
        }
        lowercase__:       List[Any]        =       self.dummy_input
        return init_dict, inputs_dict
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       List[str]        =       VQModel.from_pretrained('fusing/vqgan-dummy'    ,			output_loading_info=lowerCAmelCase__			)
        self.assertIsNotNone(lowerCAmelCase__			)
        self.assertEqual(len(loading_info['missing_keys']			)    ,			0			)

        model.to(lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       model(**self.dummy_input			)

        assert image is not None, "Make sure output is not None"
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       VQModel.from_pretrained('fusing/vqgan-dummy'			)
        model.to(lowerCAmelCase__			).eval()

        torch.manual_seed(0			)
        if torch.cuda.is_available():
               torch.cuda.manual_seed_all(0			)

        lowercase__:       Tuple        =       torch.randn(1    ,			model.config.in_channels    ,			model.config.sample_size    ,			model.config.sample_size			)
        lowercase__:       List[str]        =       image.to(lowerCAmelCase__			)
        with torch.no_grad():
               lowercase__:       int        =       model(lowerCAmelCase__			).sample

        lowercase__:       Dict        =       output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        lowercase__:       str        =       torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]			)
        # fmt: on
        self.assertTrue(torch.allclose(lowerCAmelCase__    ,			lowerCAmelCase__    ,			atol=1E-3			)			)






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


