

"""simple docstring"""


from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Tuple	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							Optional[int]=0.0	,    UpperCamelCase__				:							Optional[int] = None	,    UpperCamelCase__				:							str = "geglu"	,    UpperCamelCase__				:							Optional[int] = None	,    UpperCamelCase__				:							bool = False	,    UpperCamelCase__				:							bool = False	,    UpperCamelCase__				:							bool = False	,    UpperCamelCase__				:							bool = False	,    UpperCamelCase__				:							bool = True	,    UpperCamelCase__				:							str = "layer_norm"	,    UpperCamelCase__				:							bool = False	,    )->  int:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			List[Any]        =     only_cross_attention

						__lowerCAmelCase:			str        =     (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
						__lowerCAmelCase:			int        =     (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

						if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
								raise ValueError(
								    f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
								    f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.")

						# Define 3 blocks. Each block has its own normalization layer.
						# 1. Self-Attn
						if self.use_ada_layer_norm:
								__lowerCAmelCase:			int        =     AdaLayerNorm(__lowercase	,    __lowercase)
						elif self.use_ada_layer_norm_zero:
								__lowerCAmelCase:			Dict        =     AdaLayerNormZero(__lowercase	,    __lowercase)
						else:
								__lowerCAmelCase:			List[Any]        =     nn.LayerNorm(__lowercase	,    elementwise_affine=__lowercase)
						__lowerCAmelCase:			Optional[Any]        =     Attention(
						    query_dim=__lowercase	,    heads=__lowercase	,    dim_head=__lowercase	,    dropout=__lowercase	,    bias=__lowercase	,    cross_attention_dim=cross_attention_dim if only_cross_attention else None	,    upcast_attention=__lowercase	,    )

						# 2. Cross-Attn
						if cross_attention_dim is not None or double_self_attention:
								# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
								# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
								# the second cross attention block.
								__lowerCAmelCase:			Optional[Any]        =     (
								    AdaLayerNorm(__lowercase	,    __lowercase)
								    if self.use_ada_layer_norm
								    else nn.LayerNorm(__lowercase	,    elementwise_affine=__lowercase)
								)
								__lowerCAmelCase:			Optional[Any]        =     Attention(
								    query_dim=__lowercase	,    cross_attention_dim=cross_attention_dim if not double_self_attention else None	,    heads=__lowercase	,    dim_head=__lowercase	,    dropout=__lowercase	,    bias=__lowercase	,    upcast_attention=__lowercase	,    )  # is self-attn if encoder_hidden_states is none
						else:
								__lowerCAmelCase:			Optional[Any]        =     None
								__lowerCAmelCase:			Optional[Any]        =     None

						# 3. Feed-forward
						__lowerCAmelCase:			Any        =     nn.LayerNorm(__lowercase	,    elementwise_affine=__lowercase)
						__lowerCAmelCase:			Optional[Any]        =     FeedForward(__lowercase	,    dropout=__lowercase	,    activation_fn=__lowercase	,    final_dropout=__lowercase)

						# let chunk size default to None
						__lowerCAmelCase:			str        =     None
						__lowerCAmelCase:			int        =     0

				def 				lowercase_	(      self				:							Optional[Any]	,    UpperCamelCase__				:							Optional[int]	,    UpperCamelCase__				:							int)->  int:



						'''simple docstring'''


						__lowerCAmelCase:			int        =     chunk_size
						__lowerCAmelCase:			Dict        =     dim




				def 				lowercase_	(      self				:							Optional[int]	,    UpperCamelCase__				:							torch.FloatTensor	,    UpperCamelCase__				:							Optional[torch.FloatTensor] = None	,    UpperCamelCase__				:							Optional[torch.FloatTensor] = None	,    UpperCamelCase__				:							Optional[torch.FloatTensor] = None	,    UpperCamelCase__				:							Optional[torch.LongTensor] = None	,    UpperCamelCase__				:							Dict[str, Any] = None	,    UpperCamelCase__				:							Optional[torch.LongTensor] = None	,    )->  int:



						'''simple docstring'''


						if self.use_ada_layer_norm:
								__lowerCAmelCase:			Tuple        =     self.norma(__lowercase	,    __lowercase)
						elif self.use_ada_layer_norm_zero:
								__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase:			int        =     self.norma(
								    __lowercase	,    __lowercase	,    __lowercase	,    hidden_dtype=hidden_states.dtype)
						else:
								__lowerCAmelCase:			List[Any]        =     self.norma(__lowercase)

						__lowerCAmelCase:			Any        =     cross_attention_kwargs if cross_attention_kwargs is not None else {}

						__lowerCAmelCase:			List[Any]        =     self.attna(
						    __lowercase	,    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None	,    attention_mask=__lowercase	,    **__lowercase	,    )
						if self.use_ada_layer_norm_zero:
								__lowerCAmelCase:			Dict        =     gate_msa.unsqueeze(1) * attn_output
						__lowerCAmelCase:			List[str]        =     attn_output + hidden_states

						# 2. Cross-Attention
						if self.attna is not None:
								__lowerCAmelCase:			Optional[Any]        =     (
								    self.norma(__lowercase	,    __lowercase) if self.use_ada_layer_norm else self.norma(__lowercase)
								)

								__lowerCAmelCase:			List[Any]        =     self.attna(
								    __lowercase	,    encoder_hidden_states=__lowercase	,    attention_mask=__lowercase	,    **__lowercase	,    )
								__lowerCAmelCase:			Tuple        =     attn_output + hidden_states

						# 3. Feed-forward
						__lowerCAmelCase:			str        =     self.norma(__lowercase)

						if self.use_ada_layer_norm_zero:
								__lowerCAmelCase:			Any        =     norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

						if self._chunk_size is not None:
								# "feed_forward_chunk_size" can be used to save memory
								if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
										raise ValueError(
										    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.")

								__lowerCAmelCase:			Any        =     norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
								__lowerCAmelCase:			Any        =     torch.cat(
								    [self.ff(__lowercase) for hid_slice in norm_hidden_states.chunk(__lowercase	,    dim=self._chunk_dim)]	,    dim=self._chunk_dim	,    )
						else:
								__lowerCAmelCase:			Dict        =     self.ff(__lowercase)

						if self.use_ada_layer_norm_zero:
								__lowerCAmelCase:			Union[str, Any]        =     gate_mlp.unsqueeze(1) * ff_output

						__lowerCAmelCase:			List[Any]        =     ff_output + hidden_states

						return hidden_states





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Tuple	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							Optional[int] = None	,    UpperCamelCase__				:							int = 4	,    UpperCamelCase__				:							float = 0.0	,    UpperCamelCase__				:							str = "geglu"	,    UpperCamelCase__				:							bool = False	,    )->  Optional[Any]:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			Any        =     int(dim * mult)
						__lowerCAmelCase:			str        =     dim_out if dim_out is not None else dim

						if activation_fn == "gelu":
								__lowerCAmelCase:			List[Any]        =     GELU(__lowercase	,    __lowercase)
						if activation_fn == "gelu-approximate":
								__lowerCAmelCase:			Any        =     GELU(__lowercase	,    __lowercase	,    approximate="tanh")
						elif activation_fn == "geglu":
								__lowerCAmelCase:			Union[str, Any]        =     GEGLU(__lowercase	,    __lowercase)
						elif activation_fn == "geglu-approximate":
								__lowerCAmelCase:			List[Any]        =     ApproximateGELU(__lowercase	,    __lowercase)

						__lowerCAmelCase:			List[str]        =     nn.ModuleList([])
						# project in
						self.net.append(__lowercase)
						# project dropout
						self.net.append(nn.Dropout(__lowercase))
						# project out
						self.net.append(nn.Linear(__lowercase	,    __lowercase))
						# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
						if final_dropout:
								self.net.append(nn.Dropout(__lowercase))




				def 				lowercase_	(      self				:							str	,    UpperCamelCase__				:							Optional[int])->  Dict:



						'''simple docstring'''


						for module in self.net:
								__lowerCAmelCase:			Any        =     module(__lowercase)
						return hidden_states





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							str = "none")->  Any:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			Dict        =     nn.Linear(__lowercase	,    __lowercase)
						__lowerCAmelCase:			Tuple        =     approximate

				def 				lowercase_	(      self				:							Dict	,    UpperCamelCase__				:							List[Any])->  int:



						'''simple docstring'''


						if gate.device.type != "mps":
								return F.gelu(__lowercase	,    approximate=self.approximate)
						# mps: gelu is not implemented for float16
						return F.gelu(gate.to(dtype=torch.floataa)	,    approximate=self.approximate).to(dtype=gate.dtype)




				def 				lowercase_	(      self				:							Any	,    UpperCamelCase__				:							Tuple)->  Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:			Any        =     self.proj(__lowercase)
						__lowerCAmelCase:			Union[str, Any]        =     self.gelu(__lowercase)
						return hidden_states





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Tuple	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int)->  Tuple:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			List[str]        =     nn.Linear(__lowercase	,    dim_out * 2)

				def 				lowercase_	(      self				:							Tuple	,    UpperCamelCase__				:							List[str])->  Optional[int]:



						'''simple docstring'''


						if gate.device.type != "mps":
								return F.gelu(__lowercase)
						# mps: gelu is not implemented for float16
						return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)




				def 				lowercase_	(      self				:							str	,    UpperCamelCase__				:							int)->  Tuple:



						'''simple docstring'''


						__lowerCAmelCase ,					__lowerCAmelCase:			Optional[Any]        =     self.proj(__lowercase).chunk(2	,    dim=-1)
						return hidden_states * self.gelu(__lowercase)





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Optional[Any]	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int)->  Any:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			Optional[int]        =     nn.Linear(__lowercase	,    __lowercase)




				def 				lowercase_	(      self				:							List[Any]	,    UpperCamelCase__				:							List[str])->  Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:			Optional[Any]        =     self.proj(__lowercase)
						return x * torch.sigmoid(1.702 * x)





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							List[Any]	,    UpperCamelCase__				:							Optional[Any]	,    UpperCamelCase__				:							Any)->  Any:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			Optional[int]        =     nn.Embedding(__lowercase	,    __lowercase)
						__lowerCAmelCase:			Optional[int]        =     nn.SiLU()
						__lowerCAmelCase:			Optional[int]        =     nn.Linear(__lowercase	,    embedding_dim * 2)
						__lowerCAmelCase:			Dict        =     nn.LayerNorm(__lowercase	,    elementwise_affine=__lowercase)




				def 				lowercase_	(      self				:							Union[str, Any]	,    UpperCamelCase__				:							List[Any]	,    UpperCamelCase__				:							Optional[int])->  Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:			Tuple        =     self.linear(self.silu(self.emb(__lowercase)))
						__lowerCAmelCase ,					__lowerCAmelCase:			Tuple        =     torch.chunk(__lowercase	,    2)
						__lowerCAmelCase:			Dict        =     self.norm(__lowercase) * (1 + scale) + shift
						return x





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Optional[int]	,    UpperCamelCase__				:							Optional[int]	,    UpperCamelCase__				:							str)->  Dict:



						'''simple docstring'''


						super().__init__()

						__lowerCAmelCase:			Union[str, Any]        =     CombinedTimestepLabelEmbeddings(__lowercase	,    __lowercase)

						__lowerCAmelCase:			Dict        =     nn.SiLU()
						__lowerCAmelCase:			Union[str, Any]        =     nn.Linear(__lowercase	,    6 * embedding_dim	,    bias=__lowercase)
						__lowerCAmelCase:			Dict        =     nn.LayerNorm(__lowercase	,    elementwise_affine=__lowercase	,    eps=1e-6)




				def 				lowercase_	(      self				:							Dict	,    UpperCamelCase__				:							List[Any]	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							Tuple	,    UpperCamelCase__				:							int=None)->  Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:			Optional[Any]        =     self.linear(self.silu(self.emb(__lowercase	,    __lowercase	,    hidden_dtype=__lowercase)))
						__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase:			Optional[int]        =     emb.chunk(6	,    dim=1)
						__lowerCAmelCase:			List[Any]        =     self.norm(__lowercase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
						return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class        snake_case      (							nn.Module    ):

				def __init__(      self				:							Optional[Any]	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							int	,    UpperCamelCase__				:							Optional[str] = None	,    UpperCamelCase__				:							float = 1e-5)->  List[Any]:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:			Optional[int]        =     num_groups
						__lowerCAmelCase:			Optional[Any]        =     eps

						if act_fn is None:
								__lowerCAmelCase:			List[str]        =     None
						else:
								__lowerCAmelCase:			int        =     get_activation(__lowercase)

						__lowerCAmelCase:			Dict        =     nn.Linear(__lowercase	,    out_dim * 2)




				def 				lowercase_	(      self				:							Optional[Any]	,    UpperCamelCase__				:							Tuple	,    UpperCamelCase__				:							Dict)->  Any:



						'''simple docstring'''


						if self.act:
								__lowerCAmelCase:			Optional[int]        =     self.act(__lowercase)
						__lowerCAmelCase:			Optional[int]        =     self.linear(__lowercase)
						__lowerCAmelCase:			Tuple        =     emb[:, :, None, None]
						__lowerCAmelCase ,					__lowerCAmelCase:			Any        =     emb.chunk(2	,    dim=1)

						__lowerCAmelCase:			Optional[Any]        =     F.group_norm(__lowercase	,    self.num_groups	,    eps=self.eps)
						__lowerCAmelCase:			Dict        =     x * (1 + scale) + shift
						return x





'''simple docstring'''

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					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=DPTConfig()

     if "large" in checkpoint_url:
          __lowercase      		=10_24
          __lowercase      		=40_96
          __lowercase      		=24
          __lowercase      		=16
          __lowercase      		=[5, 11, 17, 23]
          __lowercase      		=[2_56, 5_12, 10_24, 10_24]
          __lowercase      		=(1, 3_84, 3_84)

     if "ade" in checkpoint_url:
          __lowercase      		=True

          __lowercase      		=1_50
          __lowercase      		='huggingface/label-files'
          __lowercase      		='ade20k-id2label.json'
          __lowercase      		=json.load(open(cached_download(hf_hub_url(lowercase__,       lowercase__,       repo_type='dataset'			)			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}
          __lowercase      		=[1, 1_50, 4_80, 4_80]

     return config, expected_shape

def 					__UpperCamelCase				(						lowercase__    :	Optional[int]			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any]			):






     '''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
     ):
          __lowercase      		=name.replace('pretrained.model',       'dpt.encoder'			)
     if "pretrained.model" in name:
          __lowercase      		=name.replace('pretrained.model',       'dpt.embeddings'			)
     if "patch_embed" in name:
          __lowercase      		=name.replace('patch_embed',       'patch_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'position_embeddings'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "proj" in name and "project" not in name:
          __lowercase      		=name.replace('proj',       'projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'layer'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "scratch.output_conv" in name:
          __lowercase      		=name.replace('scratch.output_conv',       'head'			)
     if "scratch" in name:
          __lowercase      		=name.replace('scratch',       'neck'			)
     if "layer1_rn" in name:
          __lowercase      		=name.replace('layer1_rn',       'convs.0'			)
     if "layer2_rn" in name:
          __lowercase      		=name.replace('layer2_rn',       'convs.1'			)
     if "layer3_rn" in name:
          __lowercase      		=name.replace('layer3_rn',       'convs.2'			)
     if "layer4_rn" in name:
          __lowercase      		=name.replace('layer4_rn',       'convs.3'			)
     if "refinenet" in name:
          __lowercase      		=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
          __lowercase      		=name.replace(F'''refinenet{layer_idx}''',       F'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
     if "out_conv" in name:
          __lowercase      		=name.replace('out_conv',       'projection'			)
     if "resConfUnit1" in name:
          __lowercase      		=name.replace('resConfUnit1',       'residual_layer1'			)
     if "resConfUnit2" in name:
          __lowercase      		=name.replace('resConfUnit2',       'residual_layer2'			)
     if "conv1" in name:
          __lowercase      		=name.replace('conv1',       'convolution1'			)
     if "conv2" in name:
          __lowercase      		=name.replace('conv2',       'convolution2'			)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.0.project.0',       'neck.reassemble_stage.readout_projects.0.0'			)
     if "pretrained.act_postprocess2.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.0.project.0',       'neck.reassemble_stage.readout_projects.1.0'			)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.0.project.0',       'neck.reassemble_stage.readout_projects.2.0'			)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.0.project.0',       'neck.reassemble_stage.readout_projects.3.0'			)
     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.3',       'neck.reassemble_stage.layers.0.projection'			)
     if "pretrained.act_postprocess1.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess1.4',       'neck.reassemble_stage.layers.0.resize'			)
     if "pretrained.act_postprocess2.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.3',       'neck.reassemble_stage.layers.1.projection'			)
     if "pretrained.act_postprocess2.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess2.4',       'neck.reassemble_stage.layers.1.resize'			)
     if "pretrained.act_postprocess3.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess3.3',       'neck.reassemble_stage.layers.2.projection'			)
     if "pretrained.act_postprocess4.3" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.3',       'neck.reassemble_stage.layers.3.projection'			)
     if "pretrained.act_postprocess4.4" in name:
          __lowercase      		=name.replace('pretrained.act_postprocess4.4',       'neck.reassemble_stage.layers.3.resize'			)
     if "pretrained" in name:
          __lowercase      		=name.replace('pretrained',       'dpt'			)
     if "bn" in name:
          __lowercase      		=name.replace('bn',       'batch_norm'			)
     if "head" in name:
          __lowercase      		=name.replace('head',       'head.head'			)
     if "encoder.norm" in name:
          __lowercase      		=name.replace('encoder.norm',       'layernorm'			)
     if "auxlayer" in name:
          __lowercase      		=name.replace('auxlayer',       'auxiliary_head.head'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Optional[Any]			):






     '''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)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	str,       lowercase__    :	List[str],       lowercase__    :	List[str]			):






     '''simple docstring'''





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

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

     # Check outputs on an image
     __lowercase      		=4_80 if 'ade' in checkpoint_url else 3_84
     __lowercase      		=DPTImageProcessor(size=lowercase__			)

     __lowercase      		=prepare_img()
     __lowercase      		=image_processor(lowercase__,       return_tensors='pt'			)

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

     # Assert logits
     __lowercase      		=torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]			)
     if "ade" in checkpoint_url:
          __lowercase      		=torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]			)
     assert outputs.shape == torch.Size(lowercase__			)
     assert (
         torch.allclose(outputs[0, 0, :3, :3],       lowercase__,       atol=1E-4			)
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3],       lowercase__			)
     )

     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:
          print('Pushing model to hub...'			)
          model.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add model',       use_temp_dir=lowercase__,       )
          image_processor.push_to_hub(
              repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nielsr',       commit_message='Add image processor',       use_temp_dir=lowercase__,       )


if __name__ == "__main__":
       UpperCAmelCase					 =	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=True,
           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.''',
       )

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


