
'''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       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  42







class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase    ):

	@register_to_config
	def __init__(      self   :						List[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_82_15  ,					UpperCAmelCase_   :						str = "group"  ,					):
			super().__init__()

			# pass init params to Encoder
			UpperCamelCase__				:					List[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_  ,					)

			UpperCamelCase__				:					Tuple					=					vq_embed_dim if vq_embed_dim is not None else latent_channels

			UpperCamelCase__				:					str					=					nn.Convad(UpperCAmelCase_  ,					UpperCAmelCase_  ,					1)
			UpperCamelCase__				:					Tuple					=					VectorQuantizer(UpperCAmelCase_  ,					UpperCAmelCase_  ,					beta=0.25  ,					remap=UpperCAmelCase_  ,					sane_index_shape=UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					nn.Convad(UpperCAmelCase_  ,					UpperCAmelCase_  ,					1)

			# pass init params to Decoder
			UpperCamelCase__				:					List[str]					=					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      __UpperCamelCase							(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						torch.FloatTensor  ,					UpperCAmelCase_   :						bool = True):
			UpperCamelCase__				:					Dict					=					self.encoder(UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					self.quant_conv(UpperCAmelCase_)

			if not return_dict:
					return (h,)

			return VQEncoderOutput(latents=UpperCAmelCase_)

	@apply_forward_hook
	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						torch.FloatTensor  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = True):
			# also go through quantization layer
			if not force_not_quantize:
					UpperCamelCase__				:					List[str]					=					self.quantize(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					Tuple					=					h
			UpperCamelCase__				:					Union[str, Any]					=					self.post_quant_conv(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.decoder(UpperCAmelCase_  ,					quant if self.config.norm_type == 'spatial' else None)

			if not return_dict:
					return (dec,)

			return DecoderOutput(sample=UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						torch.FloatTensor  ,					UpperCAmelCase_   :						bool = True):
			UpperCamelCase__				:					str					=					sample
			UpperCamelCase__				:					int					=					self.encode(UpperCAmelCase_).latents
			UpperCamelCase__				:					Union[str, Any]					=					self.decode(UpperCAmelCase_).sample

			if not return_dict:
					return (dec,)

			return DecoderOutput(sample=UpperCAmelCase_)












'''simple docstring'''




from typing import Union

import fire
import torch
from tqdm import tqdm







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = "cpu"     ,      lowerCamelCase_ = None)							->     None:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location=lowerCamelCase_)
		for k, v in tqdm(state_dict.items()):
				if not isinstance(lowerCamelCase_     ,      torch.Tensor):
						raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin')
				UpperCamelCase__				:					int					=					v.half()
		if save_path is None:  # overwrite src_path
				UpperCamelCase__				:					List[Any]					=					src_path
		torch.save(lowerCamelCase_     ,      lowerCamelCase_)


if __name__ == "__main__":
		fire.Fire(convert)




