





'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=42






class     lowerCAmelCase__ (				UpperCAmelCase_      , UpperCAmelCase_    ):

       '''simple docstring'''

       @register_to_config
       def __init__( self					:				List[Any]       ,     a__					:				int = 3       ,     a__					:				int = 3       ,     a__					:				Tuple[str] = ("DownEncoderBlock2D",)       ,     a__					:				Tuple[str] = ("UpDecoderBlock2D",)       ,     a__					:				Tuple[int] = (64,)       ,     a__					:				int = 1       ,     a__					:				str = "silu"       ,     a__					:				int = 3       ,     a__					:				int = 32       ,     a__					:				int = 256       ,     a__					:				int = 32       ,     a__					:				Optional[int] = None       ,     a__					:				float = 0.18_215       ,     a__					:				str = "group"       ,     ):
              super().__init__()

              # pass init params to Encoder
              UpperCAmelCase  =						Encoder(
                  in_channels=a__       ,     out_channels=a__       ,     down_block_types=a__       ,     block_out_channels=a__       ,     layers_per_block=a__       ,     act_fn=a__       ,     norm_num_groups=a__       ,     double_z=a__       ,     )

              UpperCAmelCase  =						vq_embed_dim if vq_embed_dim is not None else latent_channels

              UpperCAmelCase  =						nn.Convad(a__       ,     a__       ,     1						)
              UpperCAmelCase  =						VectorQuantizer(a__       ,     a__       ,     beta=0.25       ,     remap=a__       ,     sane_index_shape=a__						)
              UpperCAmelCase  =						nn.Convad(a__       ,     a__       ,     1						)

              # pass init params to Decoder
              UpperCAmelCase  =						Decoder(
                  in_channels=a__       ,     out_channels=a__       ,     up_block_types=a__       ,     block_out_channels=a__       ,     layers_per_block=a__       ,     act_fn=a__       ,     norm_num_groups=a__       ,     norm_type=a__       ,     )

       @apply_forward_hook
       def 			__snake_case      ( self					:				Tuple       ,     a__					:				torch.FloatTensor       ,     a__					:				bool = True						):
              UpperCAmelCase  =						self.encoder(a__						)
              UpperCAmelCase  =						self.quant_conv(a__						)

              if not return_dict:
                     return (h,)

              return VQEncoderOutput(latents=a__						)

       @apply_forward_hook
       def 			__snake_case      ( self					:				str       ,     a__					:				torch.FloatTensor       ,     a__					:				bool = False       ,     a__					:				bool = True						):
              # also go through quantization layer
              if not force_not_quantize:
                     UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						self.quantize(a__						)
              else:
                     UpperCAmelCase  =						h
              UpperCAmelCase  =						self.post_quant_conv(a__						)
              UpperCAmelCase  =						self.decoder(a__       ,     quant if self.config.norm_type == '''spatial''' else None						)

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=a__						)



       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				torch.FloatTensor       ,     a__					:				bool = True						):
              UpperCAmelCase  =						sample
              UpperCAmelCase  =						self.encode(a__						).latents
              UpperCAmelCase  =						self.decode(a__						).sample

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=a__						)






"""simple docstring"""
from __future__ import annotations

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

def 				_snake_case      (   __snake_case							:      float						, __snake_case							:      float						, __snake_case							:      bool = False   ):




   """simple docstring"""







   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							:      NDArray[floataa]						, __snake_case							:      NDArray[floataa]						, __snake_case							:      float = 10**-1   ):




   """simple docstring"""







   _lowerCamelCase	:					NDArray[floataa]   =		cross(__snake_case						, __snake_case   )
   _lowerCamelCase	:					float   =		sum(__snake_case   )
   return abs(__snake_case   ) < eps


if __name__ == "__main__":
 # Test to check if it works
 UpperCAmelCase				=						array(
     [
         polar_force(718.4, 180 - 30),
         polar_force(879.54, 45),
         polar_force(100, -90),
     ]
 )

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

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 UpperCAmelCase				=						array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

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

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 UpperCAmelCase				=						array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

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

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()

