





import pprint

import requests

UpperCamelCase__      ='https://zenquotes.io/api'






def     lowerCamelCase__			():
     return requests.get(API_ENDPOINT_URL + "/today"	).json()






def     lowerCamelCase__			():
     return requests.get(API_ENDPOINT_URL + "/random"	).json()


if __name__ == "__main__":
       UpperCamelCase__      =random_quotes()
       pprint.pprint(response)







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__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)