





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 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   torch.FloatTensor





class 				a			(      __lowercase   ,__lowercase			):






				@register_to_config
				def __init__(  self			, _lowerCAmelCase = 3			, _lowerCAmelCase = 3			, _lowerCAmelCase = ("DownEncoderBlock2D",)			, _lowerCAmelCase = ("UpDecoderBlock2D",)			, _lowerCAmelCase = (64,)			, _lowerCAmelCase = 1			, _lowerCAmelCase = "silu"			, _lowerCAmelCase = 3			, _lowerCAmelCase = 32			, _lowerCAmelCase = 256			, _lowerCAmelCase = 32			, _lowerCAmelCase = None			, _lowerCAmelCase = 0.18215			, _lowerCAmelCase = "group"			, ):

										"""simple docstring"""




										super().__init__()

										# pass init params to Encoder
										__SCREAMING_SNAKE_CASE:				List[str]           =   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:				Optional[Any]           =   vq_embed_dim if vq_embed_dim is not None else latent_channels

										__SCREAMING_SNAKE_CASE:				Dict           =   nn.Convad(_lowerCAmelCase			, _lowerCAmelCase			, 1					)
										__SCREAMING_SNAKE_CASE:				Any           =   VectorQuantizer(_lowerCAmelCase			, _lowerCAmelCase			, beta=0.25			, remap=_lowerCAmelCase			, sane_index_shape=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   nn.Convad(_lowerCAmelCase			, _lowerCAmelCase			, 1					)

										# pass init params to Decoder
										__SCREAMING_SNAKE_CASE:				int           =   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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = True					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.encoder(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   self.quant_conv(_lowerCAmelCase					)

										if not return_dict:
																return (h,)

										return VQEncoderOutput(latents=_lowerCAmelCase					)






				@apply_forward_hook
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = False			, _lowerCAmelCase = True					):

										"""simple docstring"""




										if not force_not_quantize:
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.quantize(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   h
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.post_quant_conv(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   self.decoder(_lowerCAmelCase			, quant if self.config.norm_type == '''spatial''' else None					)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = True					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   sample
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.encode(_lowerCAmelCase					).latents
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.decode(_lowerCAmelCase					).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=_lowerCAmelCase					)










import re






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    bool:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   re.compile(
						    R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$'''					)

						return bool(re.search(UpperCamelCase__       ,			UpperCamelCase__					)					)


if __name__ == "__main__":
							lowerCAmelCase							:       str					 =	"""0094702343221"""

							print(is_sri_lankan_phone_number(phone))



