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_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		torch.FloatTensor


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase=3				,				__lowercase=3				,				__lowercase=("DownEncoderBlock2D",)				,				__lowercase=(64,)				,				__lowercase=2				,				__lowercase=32				,				__lowercase="silu"				,				__lowercase=True				,				) ->			Dict:
        super().__init__()
        __UpperCamelCase  :Optional[Any]    						=	layers_per_block

        __UpperCamelCase  :str    						=	torch.nn.Convad(
            __lowercase				,				block_out_channels[0]				,				kernel_size=3				,				stride=1				,				padding=1				,				)

        __UpperCamelCase  :int    						=	None
        __UpperCamelCase  :str    						=	nn.ModuleList([])

        # down
        __UpperCamelCase  :Optional[Any]    						=	block_out_channels[0]
        for i, down_block_type in enumerate(__lowercase):
            __UpperCamelCase  :Any    						=	output_channel
            __UpperCamelCase  :Dict    						=	block_out_channels[i]
            __UpperCamelCase  :Optional[int]    						=	i == len(__lowercase) - 1

            __UpperCamelCase  :List[str]    						=	get_down_block(
                __lowercase				,				num_layers=self.layers_per_block				,				in_channels=__lowercase				,				out_channels=__lowercase				,				add_downsample=not is_final_block				,				resnet_eps=1E-6				,				downsample_padding=0				,				resnet_act_fn=__lowercase				,				resnet_groups=__lowercase				,				attention_head_dim=__lowercase				,				temb_channels=__lowercase				,				)
            self.down_blocks.append(__lowercase)

        # mid
        __UpperCamelCase  :int    						=	UNetMidBlockaD(
            in_channels=block_out_channels[-1]				,				resnet_eps=1E-6				,				resnet_act_fn=__lowercase				,				output_scale_factor=1				,				resnet_time_scale_shift='''default'''				,				attention_head_dim=block_out_channels[-1]				,				resnet_groups=__lowercase				,				temb_channels=__lowercase				,				)

        # out
        __UpperCamelCase  :int    						=	nn.GroupNorm(num_channels=block_out_channels[-1]				,				num_groups=__lowercase				,				eps=1E-6)
        __UpperCamelCase  :str    						=	nn.SiLU()

        __UpperCamelCase  :Dict    						=	2 * out_channels if double_z else out_channels
        __UpperCamelCase  :List[Any]    						=	nn.Convad(block_out_channels[-1]				,				__lowercase				,				3				,				padding=1)

        __UpperCamelCase  :Any    						=	False







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Union[str, Any]:
        __UpperCamelCase  :Union[str, Any]    						=	x
        __UpperCamelCase  :List[str]    						=	self.conv_in(__lowercase)

        if self.training and self.gradient_checkpointing:

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

                return custom_forward

            # down
            if is_torch_version('''>='''				,				'''1.11.0'''):
                for down_block in self.down_blocks:
                    __UpperCamelCase  :List[str]    						=	torch.utils.checkpoint.checkpoint(
                        create_custom_forward(__lowercase)				,				__lowercase				,				use_reentrant=__lowercase)
                # middle
                __UpperCamelCase  :str    						=	torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block)				,				__lowercase				,				use_reentrant=__lowercase)
            else:
                for down_block in self.down_blocks:
                    __UpperCamelCase  :Optional[int]    						=	torch.utils.checkpoint.checkpoint(create_custom_forward(__lowercase)				,				__lowercase)
                # middle
                __UpperCamelCase  :Dict    						=	torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block)				,				__lowercase)

        else:
            # down
            for down_block in self.down_blocks:
                __UpperCamelCase  :List[Any]    						=	down_block(__lowercase)

            # middle
            __UpperCamelCase  :int    						=	self.mid_block(__lowercase)

        # post-process
        __UpperCamelCase  :int    						=	self.conv_norm_out(__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.conv_act(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	self.conv_out(__lowercase)

        return sample


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase=3				,				__lowercase=3				,				__lowercase=("UpDecoderBlock2D",)				,				__lowercase=(64,)				,				__lowercase=2				,				__lowercase=32				,				__lowercase="silu"				,				__lowercase="group"				,				) ->			List[Any]:
        super().__init__()
        __UpperCamelCase  :str    						=	layers_per_block

        __UpperCamelCase  :Optional[Any]    						=	nn.Convad(
            __lowercase				,				block_out_channels[-1]				,				kernel_size=3				,				stride=1				,				padding=1				,				)

        __UpperCamelCase  :Union[str, Any]    						=	None
        __UpperCamelCase  :Optional[Any]    						=	nn.ModuleList([])

        __UpperCamelCase  :int    						=	in_channels if norm_type == '''spatial''' else None

        # mid
        __UpperCamelCase  :Optional[int]    						=	UNetMidBlockaD(
            in_channels=block_out_channels[-1]				,				resnet_eps=1E-6				,				resnet_act_fn=__lowercase				,				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=__lowercase				,				temb_channels=__lowercase				,				)

        # up
        __UpperCamelCase  :str    						=	list(reversed(__lowercase))
        __UpperCamelCase  :Union[str, Any]    						=	reversed_block_out_channels[0]
        for i, up_block_type in enumerate(__lowercase):
            __UpperCamelCase  :Optional[int]    						=	output_channel
            __UpperCamelCase  :Union[str, Any]    						=	reversed_block_out_channels[i]

            __UpperCamelCase  :Optional[Any]    						=	i == len(__lowercase) - 1

            __UpperCamelCase  :Union[str, Any]    						=	get_up_block(
                __lowercase				,				num_layers=self.layers_per_block + 1				,				in_channels=__lowercase				,				out_channels=__lowercase				,				prev_output_channel=__lowercase				,				add_upsample=not is_final_block				,				resnet_eps=1E-6				,				resnet_act_fn=__lowercase				,				resnet_groups=__lowercase				,				attention_head_dim=__lowercase				,				temb_channels=__lowercase				,				resnet_time_scale_shift=__lowercase				,				)
            self.up_blocks.append(__lowercase)
            __UpperCamelCase  :Dict    						=	output_channel

        # out
        if norm_type == "spatial":
            __UpperCamelCase  :Union[str, Any]    						=	SpatialNorm(block_out_channels[0]				,				__lowercase)
        else:
            __UpperCamelCase  :Any    						=	nn.GroupNorm(num_channels=block_out_channels[0]				,				num_groups=__lowercase				,				eps=1E-6)
        __UpperCamelCase  :int    						=	nn.SiLU()
        __UpperCamelCase  :Optional[Any]    						=	nn.Convad(block_out_channels[0]				,				__lowercase				,				3				,				padding=1)

        __UpperCamelCase  :Union[str, Any]    						=	False







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=None) ->			Union[str, Any]:
        __UpperCamelCase  :Tuple    						=	z
        __UpperCamelCase  :int    						=	self.conv_in(__lowercase)

        __UpperCamelCase  :Optional[int]    						=	next(iter(self.up_blocks.parameters())).dtype
        if self.training and self.gradient_checkpointing:

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

                return custom_forward

            if is_torch_version('''>='''				,				'''1.11.0'''):
                # middle
                __UpperCamelCase  :Union[str, Any]    						=	torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block)				,				__lowercase				,				__lowercase				,				use_reentrant=__lowercase)
                __UpperCamelCase  :Dict    						=	sample.to(__lowercase)

                # up
                for up_block in self.up_blocks:
                    __UpperCamelCase  :Tuple    						=	torch.utils.checkpoint.checkpoint(
                        create_custom_forward(__lowercase)				,				__lowercase				,				__lowercase				,				use_reentrant=__lowercase)
            else:
                # middle
                __UpperCamelCase  :int    						=	torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block)				,				__lowercase				,				__lowercase)
                __UpperCamelCase  :List[str]    						=	sample.to(__lowercase)

                # up
                for up_block in self.up_blocks:
                    __UpperCamelCase  :List[Any]    						=	torch.utils.checkpoint.checkpoint(create_custom_forward(__lowercase)				,				__lowercase				,				__lowercase)
        else:
            # middle
            __UpperCamelCase  :List[Any]    						=	self.mid_block(__lowercase				,				__lowercase)
            __UpperCamelCase  :Tuple    						=	sample.to(__lowercase)

            # up
            for up_block in self.up_blocks:
                __UpperCamelCase  :Optional[int]    						=	up_block(__lowercase				,				__lowercase)

        # post-process
        if latent_embeds is None:
            __UpperCamelCase  :List[Any]    						=	self.conv_norm_out(__lowercase)
        else:
            __UpperCamelCase  :Any    						=	self.conv_norm_out(__lowercase				,				__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	self.conv_act(__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.conv_out(__lowercase)

        return sample


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase=None				,				__lowercase="random"				,				__lowercase=False				,				__lowercase=True) ->			int:
        super().__init__()
        __UpperCamelCase  :Union[str, Any]    						=	n_e
        __UpperCamelCase  :Union[str, Any]    						=	vq_embed_dim
        __UpperCamelCase  :List[Any]    						=	beta
        __UpperCamelCase  :int    						=	legacy

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

        __UpperCamelCase  :Optional[Any]    						=	sane_index_shape







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







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







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Union[str, Any]:
        # reshape z -> (batch, height, width, channel) and flatten
        __UpperCamelCase  :Any    						=	z.permute(0				,				2				,				3				,				1).contiguous()
        __UpperCamelCase  :List[Any]    						=	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  :Optional[int]    						=	torch.argmin(torch.cdist(__lowercase				,				self.embedding.weight)				,				dim=1)

        __UpperCamelCase  :Dict    						=	self.embedding(__lowercase).view(z.shape)
        __UpperCamelCase  :Tuple    						=	None
        __UpperCamelCase  :Optional[int]    						=	None

        # compute loss for embedding
        if not self.legacy:
            __UpperCamelCase  :Optional[Any]    						=	self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
        else:
            __UpperCamelCase  :List[str]    						=	torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)

        # preserve gradients
        __UpperCamelCase  :str    						=	z + (z_q - z).detach()

        # reshape back to match original input shape
        __UpperCamelCase  :List[str]    						=	z_q.permute(0				,				3				,				1				,				2).contiguous()

        if self.remap is not None:
            __UpperCamelCase  :Tuple    						=	min_encoding_indices.reshape(z.shape[0]				,				-1)  # add batch axis
            __UpperCamelCase  :int    						=	self.remap_to_used(__lowercase)
            __UpperCamelCase  :str    						=	min_encoding_indices.reshape(-1				,				1)  # flatten

        if self.sane_index_shape:
            __UpperCamelCase  :Union[str, Any]    						=	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				,				__lowercase				,				__lowercase) ->			Dict:
        # shape specifying (batch, height, width, channel)
        if self.remap is not None:
            __UpperCamelCase  :Optional[int]    						=	indices.reshape(shape[0]				,				-1)  # add batch axis
            __UpperCamelCase  :List[Any]    						=	self.unmap_to_all(__lowercase)
            __UpperCamelCase  :Dict    						=	indices.reshape(-1)  # flatten again

        # get quantized latent vectors
        __UpperCamelCase  :Tuple    						=	self.embedding(__lowercase)

        if shape is not None:
            __UpperCamelCase  :Optional[Any]    						=	z_q.view(__lowercase)
            # reshape back to match original input shape
            __UpperCamelCase  :Any    						=	z_q.permute(0				,				3				,				1				,				2).contiguous()

        return z_q


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self				,				__lowercase = None) ->			torch.FloatTensor:
        # make sure sample is on the same device as the parameters and has same dtype
        __UpperCamelCase  :List[str]    						=	randn_tensor(
            self.mean.shape				,				generator=__lowercase				,				device=self.parameters.device				,				dtype=self.parameters.dtype)
        __UpperCamelCase  :Optional[int]    						=	self.mean + self.std * sample
        return x







    def     UpperCamelCase__     (			self				,				__lowercase=None) ->			List[str]:
        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				,				__lowercase				,				__lowercase=[1, 2, 3]) ->			Optional[int]:
        if self.deterministic:
            return torch.Tensor([0.0])
        __UpperCamelCase  :str    						=	np.log(2.0 * np.pi)
        return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,				2) / self.var				,				dim=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        return self.mean






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()
__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	DPTConfig(embedding_type='''hybrid'''			)

    if "large" in checkpoint_url:
        __UpperCamelCase  :Dict    						=	1_024
        __UpperCamelCase  :Optional[int]    						=	4_096
        __UpperCamelCase  :int    						=	24
        __UpperCamelCase  :List[str]    						=	16
        __UpperCamelCase  :List[str]    						=	[5, 11, 17, 23]
        __UpperCamelCase  :Optional[int]    						=	[256, 512, 1_024, 1_024]
        __UpperCamelCase  :str    						=	(1, 384, 384)

    if "nyu" or "midas" in checkpoint_url:
        __UpperCamelCase  :str    						=	768
        __UpperCamelCase  :Dict    						=	[1, 1, 1, 0.5]
        __UpperCamelCase  :List[str]    						=	[256, 512, 768, 768]
        __UpperCamelCase  :int    						=	150
        __UpperCamelCase  :Union[str, Any]    						=	16
        __UpperCamelCase  :str    						=	(1, 384, 384)
        __UpperCamelCase  :List[str]    						=	False
        __UpperCamelCase  :List[Any]    						=	'''project'''

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

    return config, expected_shape



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''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  :Dict    						=	name.replace('''pretrained.model'''						,    '''dpt.encoder'''			)
    if "pretrained.model" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.model'''						,    '''dpt.embeddings'''			)
    if "patch_embed" in name:
        __UpperCamelCase  :Any    						=	name.replace('''patch_embed'''						,    ''''''			)
    if "pos_embed" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pos_embed'''						,    '''position_embeddings'''			)
    if "attn.proj" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''attn.proj'''						,    '''attention.output.dense'''			)
    if "proj" in name and "project" not in name:
        __UpperCamelCase  :Any    						=	name.replace('''proj'''						,    '''projection'''			)
    if "blocks" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''blocks'''						,    '''layer'''			)
    if "mlp.fc1" in name:
        __UpperCamelCase  :int    						=	name.replace('''mlp.fc1'''						,    '''intermediate.dense'''			)
    if "mlp.fc2" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''mlp.fc2'''						,    '''output.dense'''			)
    if "norm1" in name and "backbone" not in name:
        __UpperCamelCase  :Dict    						=	name.replace('''norm1'''						,    '''layernorm_before'''			)
    if "norm2" in name and "backbone" not in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''norm2'''						,    '''layernorm_after'''			)
    if "scratch.output_conv" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''scratch.output_conv'''						,    '''head'''			)
    if "scratch" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''scratch'''						,    '''neck'''			)
    if "layer1_rn" in name:
        __UpperCamelCase  :int    						=	name.replace('''layer1_rn'''						,    '''convs.0'''			)
    if "layer2_rn" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer2_rn'''						,    '''convs.1'''			)
    if "layer3_rn" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer3_rn'''						,    '''convs.2'''			)
    if "layer4_rn" in name:
        __UpperCamelCase  :int    						=	name.replace('''layer4_rn'''						,    '''convs.3'''			)
    if "refinenet" in name:
        __UpperCamelCase  :Tuple    						=	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  :Dict    						=	name.replace(f"""refinenet{layer_idx}"""						,    f"""fusion_stage.layers.{abs(layer_idx-4			)}"""			)
    if "out_conv" in name:
        __UpperCamelCase  :Union[str, Any]    						=	name.replace('''out_conv'''						,    '''projection'''			)
    if "resConfUnit1" in name:
        __UpperCamelCase  :int    						=	name.replace('''resConfUnit1'''						,    '''residual_layer1'''			)
    if "resConfUnit2" in name:
        __UpperCamelCase  :str    						=	name.replace('''resConfUnit2'''						,    '''residual_layer2'''			)
    if "conv1" in name:
        __UpperCamelCase  :Any    						=	name.replace('''conv1'''						,    '''convolution1'''			)
    if "conv2" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''conv2'''						,    '''convolution2'''			)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        __UpperCamelCase  :Union[str, Any]    						=	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  :Tuple    						=	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  :Any    						=	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  :Tuple    						=	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  :Tuple    						=	name.replace('''pretrained.act_postprocess1.3'''						,    '''neck.reassemble_stage.layers.0.projection'''			)
    if "pretrained.act_postprocess1.4" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess1.4'''						,    '''neck.reassemble_stage.layers.0.resize'''			)
    if "pretrained.act_postprocess2.3" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''pretrained.act_postprocess2.3'''						,    '''neck.reassemble_stage.layers.1.projection'''			)
    if "pretrained.act_postprocess2.4" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess2.4'''						,    '''neck.reassemble_stage.layers.1.resize'''			)
    if "pretrained.act_postprocess3.3" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess3.3'''						,    '''neck.reassemble_stage.layers.2.projection'''			)
    if "pretrained.act_postprocess4.3" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess4.3'''						,    '''neck.reassemble_stage.layers.3.projection'''			)
    if "pretrained.act_postprocess4.4" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''pretrained.act_postprocess4.4'''						,    '''neck.reassemble_stage.layers.3.resize'''			)
    if "pretrained" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained'''						,    '''dpt'''			)
    if "bn" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''bn'''						,    '''batch_norm'''			)
    if "head" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''head'''						,    '''head.head'''			)
    if "encoder.norm" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''encoder.norm'''						,    '''layernorm'''			)
    if "auxlayer" in name:
        __UpperCamelCase  :Optional[Any]    						=	name.replace('''auxlayer'''						,    '''auxiliary_head.head'''			)
    if "backbone" in name:
        __UpperCamelCase  :int    						=	name.replace('''backbone'''						,    '''backbone.bit.encoder'''			)

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

    if "stem.conv" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''stem.conv'''						,    '''bit.embedder.convolution'''			)
    if "blocks" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''blocks'''						,    '''layers'''			)
    if "convolution" in name and "backbone" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''convolution'''						,    '''conv'''			)
    if "layer" in name and "backbone" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer'''						,    '''layers'''			)
    if "backbone.bit.encoder.bit" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''backbone.bit.encoder.bit'''						,    '''backbone.bit'''			)
    if "embedder.conv" in name:
        __UpperCamelCase  :Optional[Any]    						=	name.replace('''embedder.conv'''						,    '''embedder.convolution'''			)
    if "backbone.bit.encoder.stem.norm" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''backbone.bit.encoder.stem.norm'''						,    '''backbone.bit.embedder.norm'''			)
    return name



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''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  :Any    						=	state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""			)
        __UpperCamelCase  :Dict    						=	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  :Union[str, Any]    						=	in_proj_weight[: config.hidden_size, :]
        __UpperCamelCase  :Dict    						=	in_proj_bias[: config.hidden_size]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __UpperCamelCase  :Dict    						=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __UpperCamelCase  :str    						=	in_proj_weight[
            -config.hidden_size :, :
        ]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_bias[-config.hidden_size :]



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase  :List[Any]    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			)
    return im



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	get_dpt_config(SCREAMING_SNAKE_CASE			)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    __UpperCamelCase  :Optional[Any]    						=	torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)
    # remove certain keys
    remove_ignore_keys_(SCREAMING_SNAKE_CASE			)
    # rename keys
    for key in state_dict.copy().keys():
        __UpperCamelCase  :Any    						=	state_dict.pop(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Optional[int]    						=	val
    # read in qkv matrices
    read_in_q_k_v(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # load HuggingFace model
    __UpperCamelCase  :Union[str, Any]    						=	DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE			) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE			)
    model.eval()

    # Check outputs on an image
    __UpperCamelCase  :Any    						=	480 if '''ade''' in checkpoint_url else 384
    __UpperCamelCase  :Optional[Any]    						=	DPTImageProcessor(size=SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Dict    						=	prepare_img()
    __UpperCamelCase  :Any    						=	image_processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			)

    # forward pass
    __UpperCamelCase  :Optional[Any]    						=	model(**SCREAMING_SNAKE_CASE			).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE			).predicted_depth

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

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

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

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


if __name__ == "__main__":
    __lowercase											=      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''',
    )

    __lowercase											=      parser.parse_args()
    convert_dpt_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
    )





