
"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Optional[int]	,      snake_case__   :     List[str]	,      snake_case__   :     List[Any]     ):





	for attribute in key.split('.'     ):
		A						   =   getattr(__snake_case	,      __snake_case     )

	if weight_type is not None:
		A						   =   getattr(__snake_case	,      __snake_case     ).shape
	else:
		A						   =   hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		A						   =   value
	elif weight_type == "weight_g":
		A						   =   value
	elif weight_type == "weight_v":
		A						   =   value
	elif weight_type == "bias":
		A						   =   value
	else:
		A						   =   value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'     )






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Optional[Any]	,      snake_case__   :     Optional[int]     ):
	A						   =   []
	A						   =   fairseq_model.state_dict()

	A						   =   hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		A						   =   False
		if "conv_layers" in name:
			load_conv_layer(
			    __snake_case	,      __snake_case	,      __snake_case	,      __snake_case	,      hf_model.config.feat_extract_norm == 'group'	,      )
			A						   =   True
		else:
			for key, mapped_key in MAPPING.items():
				A						   =   'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or (key.split('w2v_model.'     )[-1] == name.split('.'     )[0] and not is_finetuned):
					A						   =   True
					if "*" in mapped_key:
						A						   =   name.split(__snake_case     )[0].split('.'     )[-2]
						A						   =   mapped_key.replace('*'	,      __snake_case     )
					if "weight_g" in name:
						A						   =   'weight_g'
					elif "weight_v" in name:
						A						   =   'weight_v'
					elif "weight" in name:
						A						   =   'weight'
					elif "bias" in name:
						A						   =   'bias'
					else:
						A						   =   None
					set_recursively(__snake_case	,      __snake_case	,      __snake_case	,      __snake_case	,      __snake_case     )
				continue
		if not is_used:
			unused_weights.append(__snake_case     )

	logger.warning(F'Unused weights: {unused_weights}'     )






def       _snake_case				(		snake_case__   :     int	,      snake_case__   :     Any	,      snake_case__   :     List[str]	,      snake_case__   :     str	,      snake_case__   :     Dict     ):
	A						   =   full_name.split('conv_layers.'     )[-1]
	A						   =   name.split('.'     )
	A						   =   int(items[0]     )
	A						   =   int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			A						   =   value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			A						   =   value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			A						   =   value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			A						   =   value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
	else:
		unused_weights.append(__snake_case     )






@torch.no_grad()
def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     List[str]	,      snake_case__   :     Tuple=None	,      snake_case__   :     int=None	,      snake_case__   :     str=True     ):





	if config_path is not None:
		A						   =   HubertConfig.from_pretrained(__snake_case     )
	else:
		A						   =   HubertConfig()

	if is_finetuned:
		if dict_path:
			A						   =   Dictionary.load(__snake_case     )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A						   =   target_dict.pad_index
			A						   =   target_dict.bos_index
			A						   =   target_dict.eos_index
			A						   =   len(target_dict.symbols     )
			A						   =   os.path.join(__snake_case	,      'vocab.json'     )
			if not os.path.isdir(__snake_case     ):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__snake_case     )     )
				return
			os.makedirs(__snake_case	,      exist_ok=__snake_case     )
			with open(__snake_case	,      'w'	,      encoding='utf-8'     ) as vocab_handle:
				json.dump(target_dict.indices	,      __snake_case     )
			A						   =   WavaVecaCTCTokenizer(
			    __snake_case	,      unk_token=target_dict.unk_word	,      pad_token=target_dict.pad_word	,      bos_token=target_dict.bos_word	,      eos_token=target_dict.eos_word	,      word_delimiter_token='|'	,      do_lower_case=__snake_case	,      )
			A						   =   True if config.feat_extract_norm == 'layer' else False
			A						   =   WavaVecaFeatureExtractor(
			    feature_size=1	,      sampling_rate=1_6000	,      padding_value=0	,      do_normalize=__snake_case	,      return_attention_mask=__snake_case	,      )
			A						   =   WavaVecaProcessor(feature_extractor=__snake_case	,      tokenizer=__snake_case     )
			processor.save_pretrained(__snake_case     )

		A						   =   HubertForCTC(__snake_case     )
	else:
		A						   =   HubertModel(__snake_case     )

	if is_finetuned:
		A   ,      A   ,      A						   =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]	,      arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
	else:
		A   ,      A   ,      A						   =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

	A						   =   model[0].eval()

	recursively_load_weights(__snake_case	,      __snake_case	,      __snake_case     )

	hf_wavavec.save_pretrained(__snake_case     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		_lowercase							   =				parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)

"""simple docstring"""


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    torch.FloatTensor







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[str]  ,A_     :      Dict=3  ,A_     :      int=3  ,A_     :      str=("DownEncoderBlock2D",)  ,A_     :      Dict=(64,)  ,A_     :      str=2  ,A_     :      Union[str, Any]=32  ,A_     :      Optional[int]="silu"  ,A_     :      str=True  ,)     ->   Union[str, Any]:
							super().__init__()
							A						   =   layers_per_block

							A						   =   torch.nn.Convad(
							    A_  ,block_out_channels[0]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

							A						   =   None
							A						   =   nn.ModuleList([]      )

							# down
							A						   =   block_out_channels[0]
							for i, down_block_type in enumerate(A_      ):
								A						   =   output_channel
								A						   =   block_out_channels[i]
								A						   =   i == len(A_      ) - 1

								A						   =   get_down_block(
								    A_  ,num_layers=self.layers_per_block  ,in_channels=A_  ,out_channels=A_  ,add_downsample=not is_final_block  ,resnet_eps=1e-6  ,downsample_padding=0  ,resnet_act_fn=A_  ,resnet_groups=A_  ,attention_head_dim=A_  ,temb_channels=A_  ,)
								self.down_blocks.append(A_      )

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,output_scale_factor=1  ,resnet_time_scale_shift='default'  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=A_  ,temb_channels=A_  ,)

							# out
							A						   =   nn.GroupNorm(num_channels=block_out_channels[-1]  ,num_groups=A_  ,eps=1e-6      )
							A						   =   nn.SiLU()

							A						   =   2 * out_channels if double_z else out_channels
							A						   =   nn.Convad(block_out_channels[-1]  ,A_  ,3  ,padding=1      )

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   x
							A						   =   self.conv_in(A_      )

							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      Dict      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

								# down
								if is_torch_version('>='  ,'1.11.0'      ):
									for down_block in self.down_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_      )  ,A_  ,use_reentrant=A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,use_reentrant=A_      )
								else:
									for down_block in self.down_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )  ,A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )  ,A_      )

							else:
								# down
								for down_block in self.down_blocks:
									A						   =   down_block(A_      )

								# middle
								A						   =   self.mid_block(A_      )

							# post-process
							A						   =   self.conv_norm_out(A_      )
							A						   =   self.conv_act(A_      )
							A						   =   self.conv_out(A_      )

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]=3  ,A_     :      Optional[int]=3  ,A_     :      str=("UpDecoderBlock2D",)  ,A_     :      Any=(64,)  ,A_     :      Optional[int]=2  ,A_     :      Optional[int]=32  ,A_     :      Tuple="silu"  ,A_     :      Optional[int]="group"  ,)     ->   Any:
							super().__init__()
							A						   =   layers_per_block

							A						   =   nn.Convad(
							    A_  ,block_out_channels[-1]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

							A						   =   None
							A						   =   nn.ModuleList([]      )

							A						   =   in_channels if norm_type == 'spatial' else None

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,output_scale_factor=1  ,resnet_time_scale_shift='default' if norm_type == 'group' else norm_type  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=A_  ,temb_channels=A_  ,)

							# up
							A						   =   list(reversed(A_      )      )
							A						   =   reversed_block_out_channels[0]
							for i, up_block_type in enumerate(A_      ):
								A						   =   output_channel
								A						   =   reversed_block_out_channels[i]

								A						   =   i == len(A_      ) - 1

								A						   =   get_up_block(
								    A_  ,num_layers=self.layers_per_block + 1  ,in_channels=A_  ,out_channels=A_  ,prev_output_channel=A_  ,add_upsample=not is_final_block  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,resnet_groups=A_  ,attention_head_dim=A_  ,temb_channels=A_  ,resnet_time_scale_shift=A_  ,)
								self.up_blocks.append(A_      )
								A						   =   output_channel

							# out
							if norm_type == "spatial":
								A						   =   SpatialNorm(block_out_channels[0]  ,A_      )
							else:
								A						   =   nn.GroupNorm(num_channels=block_out_channels[0]  ,num_groups=A_  ,eps=1e-6      )
							A						   =   nn.SiLU()
							A						   =   nn.Convad(block_out_channels[0]  ,A_  ,3  ,padding=1      )

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      str  ,A_     :      Union[str, Any]=None      )     ->   Any:
							A						   =   z
							A						   =   self.conv_in(A_      )

							A						   =   next(iter(self.up_blocks.parameters()      )      ).dtype
							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      List[Any]      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

								if is_torch_version('>='  ,'1.11.0'      ):
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,A_  ,use_reentrant=A_      )
									A						   =   sample.to(A_      )

									# up
									for up_block in self.up_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_      )  ,A_  ,A_  ,use_reentrant=A_      )
								else:
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,A_      )
									A						   =   sample.to(A_      )

									# up
									for up_block in self.up_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )  ,A_  ,A_      )
							else:
								# middle
								A						   =   self.mid_block(A_  ,A_      )
								A						   =   sample.to(A_      )

								# up
								for up_block in self.up_blocks:
									A						   =   up_block(A_  ,A_      )

        # post-process
							if latent_embeds is None:
								A						   =   self.conv_norm_out(A_      )
							else:
								A						   =   self.conv_norm_out(A_  ,A_      )
							A						   =   self.conv_act(A_      )
							A						   =   self.conv_out(A_      )

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      str  ,A_     :      Dict=None  ,A_     :      List[Any]="random"  ,A_     :      Optional[int]=False  ,A_     :      str=True      )     ->   List[str]:
							super().__init__()
							A						   =   n_e
							A						   =   vq_embed_dim
							A						   =   beta
							A						   =   legacy

							A						   =   nn.Embedding(self.n_e  ,self.vq_embed_dim      )
							self.embedding.weight.data.uniform_(-1.0 / self.n_e  ,1.0 / self.n_e      )

							A						   =   remap
							if self.remap is not None:
								self.register_buffer('used'  ,torch.tensor(np.load(self.remap      )      )      )
								A						   =   self.used.shape[0]
								A						   =   unknown_index  # "random" or "extra" or integer
								if self.unknown_index == "extra":
									A						   =   self.re_embed
									A						   =   self.re_embed + 1
								print(
								    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
								    F'Using {self.unknown_index} for unknown indices.'      )
							else:
								A						   =   n_e

							A						   =   sane_index_shape


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]      )     ->   Any:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							A						   =   (inds[:, :, None] == used[None, None, ...]).long()
							A						   =   match.argmax(-1      )
							A						   =   match.sum(2      ) < 1
							if self.unknown_index == "random":
								A						   =   torch.randint(0  ,self.re_embed  ,size=new[unknown].shape      ).to(device=new.device      )
							else:
								A						   =   self.unknown_index
							return new.reshape(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]      )     ->   List[Any]:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							if self.re_embed > self.used.shape[0]:  # extra token
								A						   =   0  # simply set to zero
							A						   =   torch.gather(used[None, :][inds.shape[0] * [0], :]  ,1  ,A_      )
							return back.reshape(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[Any]      )     ->   str:
							# reshape z -> (batch, height, width, channel) and flatten
							A						   =   z.permute(0  ,2  ,3  ,1      ).contiguous()
							A						   =   z.view(-1  ,self.vq_embed_dim      )

							# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
							A						   =   torch.argmin(torch.cdist(A_  ,self.embedding.weight      )  ,dim=1      )

							A						   =   self.embedding(A_      ).view(z.shape      )
							A						   =   None
							A						   =   None

							# compute loss for embedding
							if not self.legacy:
								A						   =   self.beta * torch.mean((z_q.detach() - z) ** 2      ) + torch.mean((z_q - z.detach()) ** 2      )
							else:
								A						   =   torch.mean((z_q.detach() - z) ** 2      ) + self.beta * torch.mean((z_q - z.detach()) ** 2      )

							# preserve gradients
							A						   =   z + (z_q - z).detach()

							# reshape back to match original input shape
							A						   =   z_q.permute(0  ,3  ,1  ,2      ).contiguous()

							if self.remap is not None:
								A						   =   min_encoding_indices.reshape(z.shape[0]  ,-1      )  # add batch axis
								A						   =   self.remap_to_used(A_      )
								A						   =   min_encoding_indices.reshape(-1  ,1      )  # flatten

							if self.sane_index_shape:
								A						   =   min_encoding_indices.reshape(z_q.shape[0]  ,z_q.shape[2]  ,z_q.shape[3]      )

							return z_q, loss, (perplexity, min_encodings, min_encoding_indices)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      str      )     ->   Union[str, Any]:
							# shape specifying (batch, height, width, channel)
							if self.remap is not None:
								A						   =   indices.reshape(shape[0]  ,-1      )  # add batch axis
								A						   =   self.unmap_to_all(A_      )
								A						   =   indices.reshape(-1      )  # flatten again

							# get quantized latent vectors
							A						   =   self.embedding(A_      )

							if shape is not None:
								A						   =   z_q.view(A_      )
								# reshape back to match original input shape
								A						   =   z_q.permute(0  ,3  ,1  ,2      ).contiguous()

							return z_q







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      Tuple  ,A_     :      Dict=False      )     ->   List[str]:
							A						   =   parameters
							A   ,      A						   =   torch.chunk(A_  ,2  ,dim=1      )
							A						   =   torch.clamp(self.logvar  ,-30.0  ,20.0      )
							A						   =   deterministic
							A						   =   torch.exp(0.5 * self.logvar      )
							A						   =   torch.exp(self.logvar      )
							if self.deterministic:
								A						   =   A						   =   torch.zeros_like(
								    self.mean  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[torch.Generator] = None      )     ->   torch.FloatTensor:
							# make sure sample is on the same device as the parameters and has same dtype
							A						   =   randn_tensor(
							    self.mean.shape  ,generator=A_  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )
							A						   =   self.mean + self.std * sample
							return x


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Tuple=None      )     ->   int:
							if self.deterministic:
								return torch.Tensor([0.0]      )
							else:
								if other is None:
									return 0.5 * torch.sum(torch.pow(self.mean  ,2      ) + self.var - 1.0 - self.logvar  ,dim=[1, 2, 3]      )
								else:
									return 0.5 * torch.sum(
									    torch.pow(self.mean - other.mean  ,2      ) / other.var
									    + self.var / other.var
									    - 1.0
									    - self.logvar
									    + other.logvar  ,dim=[1, 2, 3]  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]  ,A_     :      Union[str, Any]=[1, 2, 3]      )     ->   List[str]:
							if self.deterministic:
								return torch.Tensor([0.0]      )
							A						   =   np.log(2.0 * np.pi      )
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean  ,2      ) / self.var  ,dim=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							return self.mean