"""simple docstring"""




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__lowerCamelCase							 =     2
class 			UpperCamelCase__:





						def __init__(				self						,*,  # begin keyword-only arguments
						    __UpperCAmelCase="<s>"						,__UpperCAmelCase="<pad>"						,__UpperCAmelCase="</s>"						,__UpperCAmelCase="<unk>"						,__UpperCAmelCase=None						,)    -> Tuple:
													A__   ,     A__   ,     A__   ,     A__		   =							bos, unk, pad, eos
													A__		   =							[]
													A__		   =							[]
													A__		   =							{}
													A__		   =							self.add_symbol(__UpperCAmelCase   )
													A__		   =							self.add_symbol(__UpperCAmelCase   )
													A__		   =							self.add_symbol(__UpperCAmelCase   )
													A__		   =							self.add_symbol(__UpperCAmelCase   )
													if extra_special_symbols:
																				for s in extra_special_symbols:
																											self.add_symbol(__UpperCAmelCase   )
													A__		   =							len(self.symbols   )





						def __eq__(				self						,__UpperCAmelCase   )    -> Tuple:
													return self.indices == other.indices





						def __getitem__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													if idx < len(self.symbols   ):
																				return self.symbols[idx]
													return self.unk_word





						def __len__(				self   )    -> Dict:
													return len(self.symbols   )





						def __contains__(				self						,__UpperCAmelCase   )    -> List[str]:
													return sym in self.indices





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase   )    -> List[Any]:
													A__		   =							cls()
													d.add_from_file(__UpperCAmelCase   )
													return d





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=1						,__UpperCAmelCase=False   )    -> Optional[int]:
													if word in self.indices and not overwrite:
																				A__		   =							self.indices[word]
																				A__		   =							self.count[idx] + n
																				return idx
													else:
																				A__		   =							len(self.symbols   )
																				A__		   =							idx
																				self.symbols.append(__UpperCAmelCase   )
																				self.count.append(__UpperCAmelCase   )
																				return idx





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													return 0





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Any:
													if isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				try:
																											with open(__UpperCAmelCase						,'r'						,encoding='utf-8'   ) as fd:
																																		self.add_from_file(__UpperCAmelCase   )
																				except FileNotFoundError as fnfe:
																											raise fnfe
																				except UnicodeError:
																											raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(__UpperCAmelCase   )   )
																				return

													A__		   =							f.readlines()
													A__		   =							self._load_meta(__UpperCAmelCase   )

													for line in lines[indices_start_line:]:
																				try:
																											A__   ,     A__		   =							line.rstrip().rsplit(' '						,1   )
																											if field == "#fairseq:overwrite":
																																		A__		   =							True
																																		A__   ,     A__		   =							line.rsplit(' '						,1   )
																											else:
																																		A__		   =							False
																											A__		   =							int(__UpperCAmelCase   )
																											A__		   =							line
																											if word in self and not overwrite:
																																		raise RuntimeError(
																																		    'Duplicate word found when loading Dictionary: \'{}\'. '
																																		    'Duplicate words can overwrite earlier ones by adding the '
																																		    '#fairseq:overwrite flag at the end of the corresponding row '
																																		    'in the dictionary file. If using the Camembert model, please '
																																		    'download an updated copy of the model file.'.format(__UpperCAmelCase   )   )
																											self.add_symbol(__UpperCAmelCase						,n=__UpperCAmelCase						,overwrite=__UpperCAmelCase   )
																				except ValueError:
																											raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''   )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							dict((re.sub(r'@@$'				,			''				,			UpperCamelCase__ ), v) if k.endswith('@@' ) else (re.sub(r'$'				,			'</w>'				,			UpperCamelCase__ ), v) for k, v in d.items() )
							A__		   =							'<s> <pad> </s> <unk>'.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'''{k}</w>''']
														A__		   =							d[k]  # restore
							return da






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							if not os.path.exists(UpperCamelCase__ ):
														raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!''' )
							os.makedirs(UpperCamelCase__				,			exist_ok=UpperCamelCase__ )
							print(F'''Writing results to {pytorch_dump_folder_path}''' )

							# handle various types of models

							A__		   =							os.path.join(UpperCamelCase__				,			'checkpoint.pt' )
							if not os.path.isfile(UpperCamelCase__ ):
														raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' )
							A__		   =							torch.load(UpperCamelCase__				,			map_location='cpu' )

							A__		   =							chkpt['cfg']['model']

							# dicts
							A__		   =							os.path.join(UpperCamelCase__				,			'dict.txt' )
							if not os.path.isfile(UpperCamelCase__ ):
														raise ValueError(F'''path to the file {dict_file} does not exist!''' )
							A__		   =							Dictionary.load(UpperCamelCase__ )
							A__		   =							rewrite_dict_keys(src_dict.indices )
							A__		   =							len(UpperCamelCase__ )
							A__		   =							os.path.join(UpperCamelCase__				,			VOCAB_FILES_NAMES['vocab_file'] )
							print(F'''Generating {src_vocab_file} of {src_vocab_size} records''' )
							with open(UpperCamelCase__				,			'w'				,			encoding='utf-8' ) as f:
														f.write(json.dumps(UpperCamelCase__				,			ensure_ascii=UpperCamelCase__				,			indent=UpperCamelCase__ ) )

							# merges_file (bpecodes)
							A__		   =							os.path.join(UpperCamelCase__				,			'bpecodes' )
							if not os.path.isfile(UpperCamelCase__ ):
														raise ValueError(F'''path to the file {bpecodes_file} does not exist!''' )

							A__		   =							os.path.join(UpperCamelCase__				,			VOCAB_FILES_NAMES['merges_file'] )
							shutil.copyfile(UpperCamelCase__				,			UpperCamelCase__ )

							# model config
							A__		   =							os.path.join(UpperCamelCase__				,			'config.json' )

							A__		   =							{
							    'activation_dropout': args['activation_dropout'],
							    'architectures': ['BioGptForCausalLM'],
							    'attention_probs_dropout_prob': args['attention_dropout'],
							    'bos_token_id': 0,
							    'eos_token_id': 2,
							    'hidden_act': args['activation_fn'],
							    'hidden_dropout_prob': args['dropout'],
							    'hidden_size': args['decoder_embed_dim'],
							    'initializer_range': 0.0_2,
							    'intermediate_size': args['decoder_ffn_embed_dim'],
							    'layer_norm_eps': 1E-12,
							    'layerdrop': args['decoder_layerdrop'],
							    'max_position_embeddings': args['max_target_positions'],
							    'model_type': 'biogpt',
							    'num_attention_heads': args['decoder_attention_heads'],
							    'num_hidden_layers': args['decoder_layers'],
							    'pad_token_id': 1,
							    'scale_embedding': not args['no_scale_embedding'],
							    'tie_word_embeddings': args['share_decoder_input_output_embed'],
							    'vocab_size': src_vocab_size,
							}

							# good hparam defaults to start with

							print(F'''Generating {biogpt_model_config_file}''' )
							with open(UpperCamelCase__				,			'w'				,			encoding='utf-8' ) as f:
														f.write(json.dumps(UpperCamelCase__				,			ensure_ascii=UpperCamelCase__				,			indent=UpperCamelCase__ ) )

							# tokenizer config
							A__		   =							os.path.join(UpperCamelCase__				,			UpperCamelCase__ )

							A__		   =							{
							    'bos_token': '<s>',
							    'eos_token': '</s>',
							    'model_max_length': 1_024,
							    'pad_token': '<pad>',
							    'special_tokens_map_file': None,
							    'tokenizer_class': 'BioGptTokenizer',
							    'unk_token': '<unk>',
							}

							print(F'''Generating {biogpt_tokenizer_config_file}''' )
							with open(UpperCamelCase__				,			'w'				,			encoding='utf-8' ) as f:
														f.write(json.dumps(UpperCamelCase__				,			ensure_ascii=UpperCamelCase__				,			indent=UpperCamelCase__ ) )

							# model
							A__		   =							chkpt['model']

							# remove unneeded keys
							A__		   =							[
							    'decoder.version',
							]
							for k in ignore_keys:
														model_state_dict.pop(UpperCamelCase__				,			UpperCamelCase__ )

							A__		   =							list(model_state_dict.keys() )
							for layer_name in layer_names:
														if layer_name.endswith('output_projection.weight' ):
																					A__		   =							model_state_dict.pop(UpperCamelCase__ )
														else:
																					A__		   =							model_state_dict.pop(UpperCamelCase__ )

							A__		   =							BioGptConfig.from_pretrained(UpperCamelCase__ )
							A__		   =							BioGptForCausalLM(UpperCamelCase__ )

							# check that it loads ok
							model_new.load_state_dict(UpperCamelCase__ )

							# save
							A__		   =							os.path.join(UpperCamelCase__				,			UpperCamelCase__ )
							print(F'''Generating {pytorch_weights_dump_path}''' )
							torch.save(UpperCamelCase__				,			UpperCamelCase__ )

							print('Conversion is done!' )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--biogpt_checkpoint_path",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
				        " bpecodes, etc."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






"""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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  torch.FloatTensor






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=3						,__UpperCAmelCase=("DownEncoderBlock2D",)						,__UpperCAmelCase=(64,)						,__UpperCAmelCase=2						,__UpperCAmelCase=32						,__UpperCAmelCase="silu"						,__UpperCAmelCase=True						,)    -> Union[str, Any]:
													super().__init__()
													A__		   =							layers_per_block

													A__		   =							torch.nn.Convad(
													    __UpperCAmelCase						,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(__UpperCAmelCase   ):
																				A__		   =							output_channel
																				A__		   =							block_out_channels[i]
																				A__		   =							i == len(__UpperCAmelCase   ) - 1

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

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

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

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

													A__		   =							False





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[int]:
													A__		   =							x
													A__		   =							self.conv_in(__UpperCAmelCase   )

													if self.training and self.gradient_checkpointing:

																				def create_custom_forward(__UpperCAmelCase   ):
																											def custom_forward(*__UpperCAmelCase   ):
																																		return module(*__UpperCAmelCase   )

																											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(__UpperCAmelCase   )						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block   )						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																				else:
																											for down_block in self.down_blocks:
																																		A__		   =							torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase   )						,__UpperCAmelCase   )
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block   )						,__UpperCAmelCase   )

													else:
																				# down
																				for down_block in self.down_blocks:
																											A__		   =							down_block(__UpperCAmelCase   )

																				# middle
																				A__		   =							self.mid_block(__UpperCAmelCase   )

													# post-process
													A__		   =							self.conv_norm_out(__UpperCAmelCase   )
													A__		   =							self.conv_act(__UpperCAmelCase   )
													A__		   =							self.conv_out(__UpperCAmelCase   )

													return sample






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=3						,__UpperCAmelCase=("UpDecoderBlock2D",)						,__UpperCAmelCase=(64,)						,__UpperCAmelCase=2						,__UpperCAmelCase=32						,__UpperCAmelCase="silu"						,__UpperCAmelCase="group"						,)    -> Any:
													super().__init__()
													A__		   =							layers_per_block

													A__		   =							nn.Convad(
													    __UpperCAmelCase						,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=__UpperCAmelCase						,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=__UpperCAmelCase						,temb_channels=__UpperCAmelCase						,)

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

																				A__		   =							i == len(__UpperCAmelCase   ) - 1

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

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

													A__		   =							False





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None   )    -> Dict:
													A__		   =							z
													A__		   =							self.conv_in(__UpperCAmelCase   )

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

																				def create_custom_forward(__UpperCAmelCase   ):
																											def custom_forward(*__UpperCAmelCase   ):
																																		return module(*__UpperCAmelCase   )

																											return custom_forward

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

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

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

																				# up
																				for up_block in self.up_blocks:
																											A__		   =							up_block(__UpperCAmelCase						,__UpperCAmelCase   )

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

													return sample






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase="random"						,__UpperCAmelCase=False						,__UpperCAmelCase=True   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							inds.shape
													assert len(__UpperCAmelCase   ) > 1
													A__		   =							inds.reshape(ishape[0]						,-1   )
													A__		   =							self.used.to(__UpperCAmelCase   )
													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(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							inds.shape
													assert len(__UpperCAmelCase   ) > 1
													A__		   =							inds.reshape(ishape[0]						,-1   )
													A__		   =							self.used.to(__UpperCAmelCase   )
													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						,__UpperCAmelCase   )
													return back.reshape(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													# 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(__UpperCAmelCase						,self.embedding.weight   )						,dim=1   )

													A__		   =							self.embedding(__UpperCAmelCase   ).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(__UpperCAmelCase   )
																				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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													# 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(__UpperCAmelCase   )
																				A__		   =							indices.reshape(-1   )  # flatten again

													# get quantized latent vectors
													A__		   =							self.embedding(__UpperCAmelCase   )

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

													return z_q






class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													A__		   =							parameters
													A__   ,     A__		   =							torch.chunk(__UpperCAmelCase						,2						,dim=1   )
													A__		   =							torch.clamp(self.logvar						,-3_0.0						,2_0.0   )
													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  snake_case__		(				self						,__UpperCAmelCase = 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=__UpperCAmelCase						,device=self.parameters.device						,dtype=self.parameters.dtype   )
													A__		   =							self.mean + self.std * sample
													return x





						def  snake_case__		(				self						,__UpperCAmelCase=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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=[1, 2, 3]   )    -> List[Any]:
													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=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													return self.mean





