




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_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      42


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    @register_to_config
    def __init__(			self      :	Tuple					, 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 = 0.18215					, UpperCAmelCase__      :	str = "group"					, )						->Optional[int]:





           '''simple docstring'''

           super().__init__()

           # pass init params to Encoder
           A__       =    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__					, )

           A__       =    vq_embed_dim if vq_embed_dim is not None else latent_channels

           A__       =    nn.Convad(UpperCAmelCase__					, UpperCAmelCase__					, 1)
           A__       =    VectorQuantizer(UpperCAmelCase__					, UpperCAmelCase__					, beta=0.25					, remap=UpperCAmelCase__					, sane_index_shape=UpperCAmelCase__)
           A__       =    nn.Convad(UpperCAmelCase__					, UpperCAmelCase__					, 1)

           # pass init params to Decoder
           A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	torch.FloatTensor					, UpperCAmelCase__      :	bool = True)						->VQEncoderOutput:





           '''simple docstring'''

           A__       =    self.encoder(UpperCAmelCase__)
           A__       =    self.quant_conv(UpperCAmelCase__)

           if not return_dict:
                  return (h,)

           return VQEncoderOutput(latents=UpperCAmelCase__)


    @apply_forward_hook
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	torch.FloatTensor					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = True)						->Union[DecoderOutput, torch.FloatTensor]:





           '''simple docstring'''

           if not force_not_quantize:
                  A__ ,							A__ ,							A__       =    self.quantize(UpperCAmelCase__)
           else:
                  A__       =    h
           A__       =    self.post_quant_conv(UpperCAmelCase__)
           A__       =    self.decoder(UpperCAmelCase__					, quant if self.config.norm_type == '''spatial''' else None)

           if not return_dict:
                  return (dec,)

           return DecoderOutput(sample=UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	torch.FloatTensor					, UpperCAmelCase__      :	bool = True)						->Union[DecoderOutput, torch.FloatTensor]:





           '''simple docstring'''

           A__       =    sample
           A__       =    self.encode(UpperCAmelCase__).latents
           A__       =    self.decode(UpperCAmelCase__).sample

           if not return_dict:
                  return (dec,)

           return DecoderOutput(sample=UpperCAmelCase__)








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase	:		str							       =   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase	:		Tuple							       =   typing.Union[np.floataa, int, float]  # noqa: UP007







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return np.sqrt(np.sum((np.asarray(lowercase_						) - np.asarray(lowercase_						)) ** 2						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return sum((va - va) ** 2 for va, va in zip(lowercase_ ,				lowercase_						)						) ** (1 / 2)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         from timeit import timeit

         print('''Without Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
         print('''With Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
  benchmark()


