






'''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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			42





class UpperCAmelCase_					(    __A				,       __A				):




      """simple docstring"""




      @register_to_config
      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		int = 3     ,      UpperCAmelCase			:		int = 3     ,      UpperCAmelCase			:		Tuple[str] = ("DownEncoderBlock2D",)     ,      UpperCAmelCase			:		Tuple[str] = ("UpDecoderBlock2D",)     ,      UpperCAmelCase			:		Tuple[int] = (64,)     ,      UpperCAmelCase			:		int = 1     ,      UpperCAmelCase			:		str = "silu"     ,      UpperCAmelCase			:		int = 3     ,      UpperCAmelCase			:		int = 32     ,      UpperCAmelCase			:		int = 256     ,      UpperCAmelCase			:		int = 32     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		float = 0.1_8_2_1_5     ,      UpperCAmelCase			:		str = "group"     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       super().__init__()

       # pass init params to Encoder
       lowercase					:   Union[str, Any]				  =Encoder(
           in_channels=UpperCAmelCase     ,      out_channels=UpperCAmelCase     ,      down_block_types=UpperCAmelCase     ,      block_out_channels=UpperCAmelCase     ,      layers_per_block=UpperCAmelCase     ,      act_fn=UpperCAmelCase     ,      norm_num_groups=UpperCAmelCase     ,      double_z=UpperCAmelCase     ,      )

       lowercase					:   Union[str, Any]				  =vq_embed_dim if vq_embed_dim is not None else latent_channels

       lowercase					:   Any				  =nn.Convad(UpperCAmelCase     ,      UpperCAmelCase     ,      1		)
       lowercase					:   Optional[Any]				  =VectorQuantizer(UpperCAmelCase     ,      UpperCAmelCase     ,      beta=0.2_5     ,      remap=UpperCAmelCase     ,      sane_index_shape=UpperCAmelCase		)
       lowercase					:   List[Any]				  =nn.Convad(UpperCAmelCase     ,      UpperCAmelCase     ,      1		)

       # pass init params to Decoder
       lowercase					:   Union[str, Any]				  =Decoder(
           in_channels=UpperCAmelCase     ,      out_channels=UpperCAmelCase     ,      up_block_types=UpperCAmelCase     ,      block_out_channels=UpperCAmelCase     ,      layers_per_block=UpperCAmelCase     ,      act_fn=UpperCAmelCase     ,      norm_num_groups=UpperCAmelCase     ,      norm_type=UpperCAmelCase     ,      )






      @apply_forward_hook
      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		torch.FloatTensor     ,      UpperCAmelCase			:		bool = True		)  ->  VQEncoderOutput:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.encoder(UpperCAmelCase		)
       lowercase					:   Any				  =self.quant_conv(UpperCAmelCase		)

       if not return_dict:
        return (h,)

       return VQEncoderOutput(latents=UpperCAmelCase		)






      @apply_forward_hook
      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		torch.FloatTensor     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = True		)  ->  Union[DecoderOutput, torch.FloatTensor]:


       '''simple docstring'''

       if not force_not_quantize:
        lowercase  ,		lowercase  ,		lowercase					:   Tuple				  =self.quantize(UpperCAmelCase		)
       else:
        lowercase					:   Dict				  =h
       lowercase					:   Tuple				  =self.post_quant_conv(UpperCAmelCase		)
       lowercase					:   List[Any]				  =self.decoder(UpperCAmelCase     ,      quant if self.config.norm_type == '''spatial''' else None		)

       if not return_dict:
        return (dec,)

       return DecoderOutput(sample=UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		torch.FloatTensor     ,      UpperCAmelCase			:		bool = True		)  ->  Union[DecoderOutput, torch.FloatTensor]:


       '''simple docstring'''

       lowercase					:   int				  =sample
       lowercase					:   Dict				  =self.encode(UpperCAmelCase		).latents
       lowercase					:   Optional[Any]				  =self.decode(UpperCAmelCase		).sample

       if not return_dict:
        return (dec,)

       return DecoderOutput(sample=UpperCAmelCase		)











'''simple docstring'''



import mpmath  # for roots of unity
import numpy as np
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Optional[int]=None		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =list(poly_a or [0]		)[:]
       lowercase					:   Dict				  =list(poly_b or [0]		)[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
        self.polyA.pop()
       lowercase					:   int				  =len(self.polyA		)

       while self.polyB[-1] == 0:
        self.polyB.pop()
       lowercase					:   List[str]				  =len(self.polyB		)

       # Add 0 to make lengths equal a power of 2
       lowercase					:   Tuple				  =int(
           2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

       while len(self.polyA		) < self.c_max_length:
        self.polyA.append(0		)
       while len(self.polyB		) < self.c_max_length:
        self.polyB.append(0		)
       # A complex root used for the fourier transform
       lowercase					:   Optional[int]				  =complex(mpmath.root(x=1     ,      n=self.c_max_length     ,      k=1		)		)

       # The product
       lowercase					:   str				  =self.__multiply()






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
       # Corner case
       if len(UpperCAmelCase		) <= 1:
        return dft[0]
       #
       lowercase					:   List[Any]				  =self.c_max_length // 2
       while next_ncol > 0:
        lowercase					:   str				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   List[str]				  =self.root**next_ncol

        # First half of next step
        lowercase					:   Union[str, Any]				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Second half of next step
        lowercase					:   Any				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Update
        lowercase					:   Tuple				  =new_dft
        lowercase					:   List[Any]				  =next_ncol // 2
       return dft[0]






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.__dft('''A'''		)
       lowercase					:   Union[str, Any]				  =self.__dft('''B'''		)
       lowercase					:   Any				  =[[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]		) <= 1:
        return inverce_c[0]
       # Inverse DFT
       lowercase					:   Tuple				  =2
       while next_ncol <= self.c_max_length:
        lowercase					:   Tuple				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   Tuple				  =self.root ** (next_ncol // 2)
        lowercase					:   Optional[int]				  =1
        # First half of next step
        for j in range(self.c_max_length // next_ncol		):
         for i in range(next_ncol // 2		):
          # Even positions
          new_inverse_c[i].append(
              (
                  inverce_c[i][j]
                  + inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / 2		)
          # Odd positions
          new_inverse_c[i + next_ncol // 2].append(
              (
                  inverce_c[i][j]
                  - inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / (2 * current_root)		)
         current_root *= root
        # Update
        lowercase					:   List[Any]				  =new_inverse_c
        next_ncol *= 2
       # Unpack
       lowercase					:   List[str]				  =[round(x[0].real     ,      8		) + round(x[0].imag     ,      8		) * 1J for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
        inverce_c.pop()
       return inverce_c






      def __str__( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''A = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]		)		)
       lowercase					:   List[str]				  ='''B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]		)		)
       lowercase					:   Optional[Any]				  ='''A*B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.product		)		)

       return f'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()



