

"""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       snake_case__  ( __A     ):
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( __A					,  __A     ):







			@register_to_config
			def __init__(			self			: Optional[Any]			,  lowercase			: int = 3			,  lowercase			: int = 3			,  lowercase			: Tuple[str] = ("DownEncoderBlock2D",)			,  lowercase			: Tuple[str] = ("UpDecoderBlock2D",)			,  lowercase			: Tuple[int] = (64,)			,  lowercase			: int = 1			,  lowercase			: str = "silu"			,  lowercase			: int = 3			,  lowercase			: int = 32			,  lowercase			: int = 2_56			,  lowercase			: int = 32			,  lowercase			: Optional[int] = None			,  lowercase			: float = 0.1_8_2_1_5			,  lowercase			: str = "group"			,  ):





								'''simple docstring'''



								super().__init__()

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

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

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

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







			@apply_forward_hook
			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: torch.FloatTensor			,  lowercase			: bool = True  ):





								'''simple docstring'''



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

								if not return_dict:
													return (h,)

								return VQEncoderOutput(latents=lowercase  )







			@apply_forward_hook
			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: torch.FloatTensor			,  lowercase			: bool = False			,  lowercase			: bool = True  ):





								'''simple docstring'''



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

								if not return_dict:
													return (dec,)

								return DecoderOutput(sample=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: bool = True  ):





								'''simple docstring'''



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

								if not return_dict:
													return (dec,)

								return DecoderOutput(sample=lowercase  )








"""simple docstring"""






def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					if not all(x.isalpha() for x in string	):
										raise ValueError("String must only contain alphabetic characters."	)

					UpperCAmelCase			:   Optional[int]      =				sorted(string.lower()	)
					return len(_lowercase	) == len(set(_lowercase	)	)


if __name__ == "__main__":
	snake_case_   :   Tuple        =       input("""Enter a string """).strip()

	snake_case_   :   str        =       is_isogram(input_str)
	print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')


