
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase :			str              =	logging.get_logger(__name__)


def 				A	( lowercase			)       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					DPTConfig(embedding_type='hybrid'			)

		if "large" in checkpoint_url:
				UpperCamelCase          =					1_024
				UpperCamelCase          =					4_096
				UpperCamelCase          =					24
				UpperCamelCase          =					16
				UpperCamelCase          =					[5, 11, 17, 23]
				UpperCamelCase          =					[256, 512, 1_024, 1_024]
				UpperCamelCase          =					(1, 384, 384)

		if "nyu" or "midas" in checkpoint_url:
				UpperCamelCase          =					768
				UpperCamelCase          =					[1, 1, 1, 0.5]
				UpperCamelCase          =					[256, 512, 768, 768]
				UpperCamelCase          =					150
				UpperCamelCase          =					16
				UpperCamelCase          =					(1, 384, 384)
				UpperCamelCase          =					False
				UpperCamelCase          =					'project'

		if "ade" in checkpoint_url:
				UpperCamelCase          =					True
				UpperCamelCase          =					768
				UpperCamelCase          =					[1, 1, 1, 0.5]
				UpperCamelCase          =					150
				UpperCamelCase          =					16
				UpperCamelCase          =					'huggingface/label-files'
				UpperCamelCase          =					'ade20k-id2label.json'
				UpperCamelCase          =					json.load(open(cached_download(hf_hub_url(lowercase   ,	lowercase   ,	repo_type='dataset'			)			)   ,	'r'			)			)
				UpperCamelCase          =					{int(lowercase			): v for k, v in idalabel.items()}
				UpperCamelCase          =					idalabel
				UpperCamelCase          =					{v: k for k, v in idalabel.items()}
				UpperCamelCase          =					[1, 150, 480, 480]

		return config, expected_shape


def 				A	( lowercase			)       ->  Dict:
		'''simple docstring'''

		UpperCamelCase          =					['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowercase   ,	lowercase			)


def 				A	( lowercase			)       ->  Dict:
		'''simple docstring'''

		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				UpperCamelCase          =					name.replace('pretrained.model'   ,	'dpt.encoder'			)
		if "pretrained.model" in name:
				UpperCamelCase          =					name.replace('pretrained.model'   ,	'dpt.embeddings'			)
		if "patch_embed" in name:
				UpperCamelCase          =					name.replace('patch_embed'   ,	''			)
		if "pos_embed" in name:
				UpperCamelCase          =					name.replace('pos_embed'   ,	'position_embeddings'			)
		if "attn.proj" in name:
				UpperCamelCase          =					name.replace('attn.proj'   ,	'attention.output.dense'			)
		if "proj" in name and "project" not in name:
				UpperCamelCase          =					name.replace('proj'   ,	'projection'			)
		if "blocks" in name:
				UpperCamelCase          =					name.replace('blocks'   ,	'layer'			)
		if "mlp.fc1" in name:
				UpperCamelCase          =					name.replace('mlp.fc1'   ,	'intermediate.dense'			)
		if "mlp.fc2" in name:
				UpperCamelCase          =					name.replace('mlp.fc2'   ,	'output.dense'			)
		if "norm1" in name and "backbone" not in name:
				UpperCamelCase          =					name.replace('norm1'   ,	'layernorm_before'			)
		if "norm2" in name and "backbone" not in name:
				UpperCamelCase          =					name.replace('norm2'   ,	'layernorm_after'			)
		if "scratch.output_conv" in name:
				UpperCamelCase          =					name.replace('scratch.output_conv'   ,	'head'			)
		if "scratch" in name:
				UpperCamelCase          =					name.replace('scratch'   ,	'neck'			)
		if "layer1_rn" in name:
				UpperCamelCase          =					name.replace('layer1_rn'   ,	'convs.0'			)
		if "layer2_rn" in name:
				UpperCamelCase          =					name.replace('layer2_rn'   ,	'convs.1'			)
		if "layer3_rn" in name:
				UpperCamelCase          =					name.replace('layer3_rn'   ,	'convs.2'			)
		if "layer4_rn" in name:
				UpperCamelCase          =					name.replace('layer4_rn'   ,	'convs.3'			)
		if "refinenet" in name:
				UpperCamelCase          =					int(name[len('neck.refinenet'			) : len('neck.refinenet'			) + 1]			)
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				UpperCamelCase          =					name.replace(f'''refinenet{layer_idx}'''   ,	f'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
		if "out_conv" in name:
				UpperCamelCase          =					name.replace('out_conv'   ,	'projection'			)
		if "resConfUnit1" in name:
				UpperCamelCase          =					name.replace('resConfUnit1'   ,	'residual_layer1'			)
		if "resConfUnit2" in name:
				UpperCamelCase          =					name.replace('resConfUnit2'   ,	'residual_layer2'			)
		if "conv1" in name:
				UpperCamelCase          =					name.replace('conv1'   ,	'convolution1'			)
		if "conv2" in name:
				UpperCamelCase          =					name.replace('conv2'   ,	'convolution2'			)
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess1.0.project.0'   ,	'neck.reassemble_stage.readout_projects.0.0'			)
		if "pretrained.act_postprocess2.0.project.0" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess2.0.project.0'   ,	'neck.reassemble_stage.readout_projects.1.0'			)
		if "pretrained.act_postprocess3.0.project.0" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess3.0.project.0'   ,	'neck.reassemble_stage.readout_projects.2.0'			)
		if "pretrained.act_postprocess4.0.project.0" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess4.0.project.0'   ,	'neck.reassemble_stage.readout_projects.3.0'			)

		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess1.3'   ,	'neck.reassemble_stage.layers.0.projection'			)
		if "pretrained.act_postprocess1.4" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess1.4'   ,	'neck.reassemble_stage.layers.0.resize'			)
		if "pretrained.act_postprocess2.3" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess2.3'   ,	'neck.reassemble_stage.layers.1.projection'			)
		if "pretrained.act_postprocess2.4" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess2.4'   ,	'neck.reassemble_stage.layers.1.resize'			)
		if "pretrained.act_postprocess3.3" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess3.3'   ,	'neck.reassemble_stage.layers.2.projection'			)
		if "pretrained.act_postprocess4.3" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess4.3'   ,	'neck.reassemble_stage.layers.3.projection'			)
		if "pretrained.act_postprocess4.4" in name:
				UpperCamelCase          =					name.replace('pretrained.act_postprocess4.4'   ,	'neck.reassemble_stage.layers.3.resize'			)
		if "pretrained" in name:
				UpperCamelCase          =					name.replace('pretrained'   ,	'dpt'			)
		if "bn" in name:
				UpperCamelCase          =					name.replace('bn'   ,	'batch_norm'			)
		if "head" in name:
				UpperCamelCase          =					name.replace('head'   ,	'head.head'			)
		if "encoder.norm" in name:
				UpperCamelCase          =					name.replace('encoder.norm'   ,	'layernorm'			)
		if "auxlayer" in name:
				UpperCamelCase          =					name.replace('auxlayer'   ,	'auxiliary_head.head'			)
		if "backbone" in name:
				UpperCamelCase          =					name.replace('backbone'   ,	'backbone.bit.encoder'			)

		if ".." in name:
				UpperCamelCase          =					name.replace('..'   ,	'.'			)

		if "stem.conv" in name:
				UpperCamelCase          =					name.replace('stem.conv'   ,	'bit.embedder.convolution'			)
		if "blocks" in name:
				UpperCamelCase          =					name.replace('blocks'   ,	'layers'			)
		if "convolution" in name and "backbone" in name:
				UpperCamelCase          =					name.replace('convolution'   ,	'conv'			)
		if "layer" in name and "backbone" in name:
				UpperCamelCase          =					name.replace('layer'   ,	'layers'			)
		if "backbone.bit.encoder.bit" in name:
				UpperCamelCase          =					name.replace('backbone.bit.encoder.bit'   ,	'backbone.bit'			)
		if "embedder.conv" in name:
				UpperCamelCase          =					name.replace('embedder.conv'   ,	'embedder.convolution'			)
		if "backbone.bit.encoder.stem.norm" in name:
				UpperCamelCase          =					name.replace('backbone.bit.encoder.stem.norm'   ,	'backbone.bit.embedder.norm'			)
		return name


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		for i in range(config.num_hidden_layers			):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				UpperCamelCase          =					state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
				UpperCamelCase          =					state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase          =					in_proj_weight[: config.hidden_size, :]
				UpperCamelCase          =					in_proj_bias[: config.hidden_size]
				UpperCamelCase          =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase          =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase          =					in_proj_weight[
				    -config.hidden_size :, :
				]
				UpperCamelCase          =					in_proj_bias[-config.hidden_size :]


def 				A	( )       ->  Tuple:
		'''simple docstring'''

		UpperCamelCase          =					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			)
		return im


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

		UpperCamelCase     ,  UpperCamelCase          =					get_dpt_config(lowercase			)
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)
		# remove certain keys
		remove_ignore_keys_(lowercase			)
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase          =					state_dict.pop(lowercase			)
				UpperCamelCase          =					val
		# read in qkv matrices
		read_in_q_k_v(lowercase   ,	lowercase			)

		# load HuggingFace model
		UpperCamelCase          =					DPTForSemanticSegmentation(lowercase			) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase			)
		model.load_state_dict(lowercase			)
		model.eval()

		# Check outputs on an image
		UpperCamelCase          =					480 if 'ade' in checkpoint_url else 384
		UpperCamelCase          =					DPTImageProcessor(size=lowercase			)

		UpperCamelCase          =					prepare_img()
		UpperCamelCase          =					image_processor(lowercase   ,	return_tensors='pt'			)

		# forward pass
		UpperCamelCase          =					model(**lowercase			).logits if 'ade' in checkpoint_url else model(**lowercase			).predicted_depth

		if show_prediction:
				UpperCamelCase          =					(
				    torch.nn.functional.interpolate(
				        outputs.unsqueeze(1			)   ,	size=(image.size[1], image.size[0])   ,	mode='bicubic'   ,	align_corners=lowercase   ,	)
				    .squeeze()
				    .cpu()
				    .numpy()
				)

				Image.fromarray((prediction / prediction.max()) * 255			).show()

		if pytorch_dump_folder_path is not None:
				Path(lowercase			).mkdir(exist_ok=lowercase			)
				print(f'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(lowercase			)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
				image_processor.save_pretrained(lowercase			)

		if push_to_hub:
				model.push_to_hub('ybelkada/dpt-hybrid-midas'			)
				image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'			)


if __name__ == "__main__":
					_UpperCAmelCase :			List[Any]              =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
					    type=str,
					    help="URL of the original DPT checkpoint you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=False,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					)
					parser.add_argument(
					    "--model_name",
					    default="dpt-large",
					    type=str,
					    help="Name of the model, in case you're pushing to the hub.",
					)
					parser.add_argument(
					    "--show_prediction",
					    action="store_true",
					)

					_UpperCAmelCase :			int              =	parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)


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     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	torch.FloatTensor






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("DownEncoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_=True    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

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

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

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

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

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

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					x
				UpperCamelCase          =					self.conv_in(A_	)

				if self.training and self.gradient_checkpointing:

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

								return custom_forward

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

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

						# middle
						UpperCamelCase          =					self.mid_block(A_	)

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

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("UpDecoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_="group"    ,				)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

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

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

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

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

						UpperCamelCase          =					i == len(A_	) - 1

						UpperCamelCase          =					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_	)
						UpperCamelCase          =					output_channel

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

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					z
				UpperCamelCase          =					self.conv_in(A_	)

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

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

								return custom_forward

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

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

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

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

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

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_=None    ,				A_="random"    ,				A_=False    ,				A_=True	)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					n_e
				UpperCamelCase          =					vq_embed_dim
				UpperCamelCase          =					beta
				UpperCamelCase          =					legacy

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

				UpperCamelCase          =					remap
				if self.remap is not None:
						self.register_buffer('used'    ,				torch.tensor(np.load(self.remap	)	)	)
						UpperCamelCase          =					self.used.shape[0]
						UpperCamelCase          =					unknown_index  # "random" or "extra" or integer
						if self.unknown_index == "extra":
								UpperCamelCase          =					self.re_embed
								UpperCamelCase          =					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:
						UpperCamelCase          =					n_e

				UpperCamelCase          =					sane_index_shape



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					inds.shape
				assert len(A_	) > 1
				UpperCamelCase          =					inds.reshape(ishape[0]    ,				-1	)
				UpperCamelCase          =					self.used.to(A_	)
				UpperCamelCase          =					(inds[:, :, None] == used[None, None, ...]).long()
				UpperCamelCase          =					match.argmax(-1	)
				UpperCamelCase          =					match.sum(2	) < 1
				if self.unknown_index == "random":
						UpperCamelCase          =					torch.randint(0    ,				self.re_embed    ,				size=new[unknown].shape	).to(device=new.device	)
				else:
						UpperCamelCase          =					self.unknown_index
				return new.reshape(A_	)



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



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				# reshape z -> (batch, height, width, channel) and flatten
				UpperCamelCase          =					z.permute(0    ,				2    ,				3    ,				1	).contiguous()
				UpperCamelCase          =					z.view(-1    ,				self.vq_embed_dim	)

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

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

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

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

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

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

				if self.sane_index_shape:
						UpperCamelCase          =					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 __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				# shape specifying (batch, height, width, channel)
				if self.remap is not None:
						UpperCamelCase          =					indices.reshape(shape[0]    ,				-1	)  # add batch axis
						UpperCamelCase          =					self.unmap_to_all(A_	)
						UpperCamelCase          =					indices.reshape(-1	)  # flatten again

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

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

				return z_q






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_=False	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					parameters
				UpperCamelCase     ,  UpperCamelCase          =					torch.chunk(A_    ,				2    ,				dim=1	)
				UpperCamelCase          =					torch.clamp(self.logvar    ,				-30.0    ,				20.0	)
				UpperCamelCase          =					deterministic
				UpperCamelCase          =					torch.exp(0.5 * self.logvar	)
				UpperCamelCase          =					torch.exp(self.logvar	)
				if self.deterministic:
						UpperCamelCase          =					UpperCamelCase          =					torch.zeros_like(
						    self.mean    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)



		def __UpperCamelCase			(			self    ,				A_ = None	)  -> torch.FloatTensor:
				"""simple docstring"""
				# make sure sample is on the same device as the parameters and has same dtype
				UpperCamelCase          =					randn_tensor(
				    self.mean.shape    ,				generator=A_    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)
				UpperCamelCase          =					self.mean + self.std * sample
				return x



		def __UpperCamelCase			(			self    ,				A_=None	)  -> Tuple:
				"""simple docstring"""
				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 __UpperCamelCase			(			self    ,				A_    ,				A_=[1, 2, 3]	)  -> Optional[Any]:
				"""simple docstring"""
				if self.deterministic:
						return torch.Tensor([0.0]	)
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return self.mean
