

import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_a:     Tuple							=      get_logger(__name__)

class     __UpperCamelCase     :
	SCREAMING_SNAKE_CASE__     					=  "dummy_data"
	SCREAMING_SNAKE_CASE__     					=  "datasets"
	SCREAMING_SNAKE_CASE__     					=  False




	def __init__(		self						:    Optional[int]		,						lowerCAmelCase						:    str		,						lowerCAmelCase						:    str		,						lowerCAmelCase						:    Union[Version, str]		,						lowerCAmelCase						:    Optional[str] = None		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = True		,						lowerCAmelCase						:    Optional[List[Callable]] = None		,						):


		'''simple docstring'''







		UpperCAmelCase_												=      0
		UpperCAmelCase_												=      dataset_name
		UpperCAmelCase_												=      cache_dir
		UpperCAmelCase_												=      use_local_dummy_data
		UpperCAmelCase_												=      config
		# download_callbacks take a single url as input
		UpperCAmelCase_												=      download_callbacks or []
		# if False, it doesn't load existing files and it returns the paths of the dummy files relative
		# to the dummy_data zip file root
		UpperCAmelCase_												=      load_existing_dummy_data

		# TODO(PVP, QL) might need to make this more general
		UpperCAmelCase_												=      str(__a				)
		# to be downloaded
		UpperCAmelCase_												=      None
		UpperCAmelCase_												=      None




	@property
	def    __A       (		self						:    Optional[Any]				):


		'''simple docstring'''







		if self._dummy_file is None:
			UpperCAmelCase_												=      self.download_dummy_data()
		return self._dummy_file




	@property
	def    __A       (		self						:    List[Any]				):


		'''simple docstring'''







		if self.config is not None:
			# structure is dummy / config_name / version_name
			return os.path.join("dummy"		,						self.config.name		,						self.version_name				)
		# structure is dummy / version_name
		return os.path.join("dummy"		,						self.version_name				)




	@property
	def    __A       (		self						:    Optional[int]				):


		'''simple docstring'''







		return os.path.join(self.dummy_data_folder		,						"dummy_data.zip"				)




	def    __A       (		self						:    Union[str, Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      (
		    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
		)

		UpperCAmelCase_												=      cached_path(
		    __a		,						cache_dir=self.cache_dir		,						extract_compressed_file=__a		,						force_extract=__a				)

		return os.path.join(__a		,						self.dummy_file_name				)




	@property
	def    __A       (		self						:    List[Any]				):


		'''simple docstring'''







		return os.path.join(self.datasets_scripts_dir		,						self.dataset_name		,						self.dummy_zip_file				)




	@property
	def    __A       (		self						:    Optional[int]				):


		'''simple docstring'''







		if self._bucket_url is None:
			UpperCAmelCase_												=      hf_github_url(self.dataset_name		,						self.dummy_zip_file.replace(os.sep		,						"/"				)				)
		return self._bucket_url




	@property
	def    __A       (		self						:    Any				):


		'''simple docstring'''







		if os.path.isdir(self.dummy_file				):
			return self.dummy_file
		# else cut off path to file -> example `xsum`.
		return "/".join(self.dummy_file.replace(os.sep		,						"/"				).split("/"				)[:-1]				)




	def    __A       (		self						:    List[str]		,						lowerCAmelCase						:    Optional[Any]		,						*lowerCAmelCase						:    Tuple				):


		'''simple docstring'''







		if self.load_existing_dummy_data:
			# dummy data is downloaded and tested
			UpperCAmelCase_												=      self.dummy_file
		else:
			# dummy data cannot be downloaded and only the path to dummy file is returned
			UpperCAmelCase_												=      self.dummy_file_name

		# special case when data_url is a dict
		if isinstance(__a		,						__a				):
			return self.create_dummy_data_dict(__a		,						__a				)
		elif isinstance(__a		,						(list, tuple)				):
			return self.create_dummy_data_list(__a		,						__a				)
		else:
			return self.create_dummy_data_single(__a		,						__a				)




	def    __A       (		self						:    Tuple		,						lowerCAmelCase						:    str		,						*lowerCAmelCase						:    Union[str, Any]				):


		'''simple docstring'''







		return self.download_and_extract(__a				)




	def    __A       (		self						:    List[Any]		,						lowerCAmelCase						:    List[Any]		,						lowerCAmelCase						:    List[str]				):


		'''simple docstring'''







		return self.download_and_extract(__a				)




	def    __A       (		self						:    Union[str, Any]		,						lowerCAmelCase						:    int		,						*lowerCAmelCase						:    Union[str, Any]		,						**lowerCAmelCase						:    Union[str, Any]				):


		'''simple docstring'''







		return path




	def    __A       (		self						:    str				):


		'''simple docstring'''







		return {}




	def    __A       (		self						:    str		,						lowerCAmelCase						:    Union[str, Any]		,						lowerCAmelCase						:    Union[str, Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      {}
		for key, single_urls in data_url.items():
			for download_callback in self.download_callbacks:
				if isinstance(__a		,						__a				):
					for single_url in single_urls:
						download_callback(__a				)
				else:
					UpperCAmelCase_												=      single_urls
					download_callback(__a				)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
			if isinstance(__a		,						__a				):
				UpperCAmelCase_												=      [os.path.join(__a		,						urllib.parse.quote_plus(Path(__a				).name				)				) for x in single_urls]
			else:
				UpperCAmelCase_												=      single_urls
				UpperCAmelCase_												=      os.path.join(__a		,						urllib.parse.quote_plus(Path(__a				).name				)				)
			UpperCAmelCase_												=      value

		# make sure that values are unique
		if all(isinstance(__a		,						__a				) for i in dummy_data_dict.values()				) and len(set(dummy_data_dict.values()				)				) < len(
		    dummy_data_dict.values()				):
			# append key to value to make its name unique
			UpperCAmelCase_												=      {key: value + key for key, value in dummy_data_dict.items()}

		return dummy_data_dict




	def    __A       (		self						:    Tuple		,						lowerCAmelCase						:    str		,						lowerCAmelCase						:    Union[str, Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      []
		# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
		UpperCAmelCase_												=      all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"		,						__a				)				) for url in data_url				)
		UpperCAmelCase_												=      all(
		    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"				) for url in data_url				)
		if data_url and (is_tf_records or is_pubmed_records):
			UpperCAmelCase_												=      [data_url[0]] * len(__a				)
		for single_url in data_url:
			for download_callback in self.download_callbacks:
				download_callback(__a				)
			# we force the name of each key to be the last file / folder name of the url path
			# if the url has arguments, we need to encode them with urllib.parse.quote_plus
			UpperCAmelCase_												=      os.path.join(__a		,						urllib.parse.quote_plus(single_url.split("/"				)[-1]				)				)
			dummy_data_list.append(__a				)
		return dummy_data_list




	def    __A       (		self						:    Optional[int]		,						lowerCAmelCase						:    Union[str, Any]		,						lowerCAmelCase						:    int				):


		'''simple docstring'''







		for download_callback in self.download_callbacks:
			download_callback(__a				)
		# we force the name of each key to be the last file / folder name of the url path
		# if the url has arguments, we need to encode them with urllib.parse.quote_plus
		UpperCAmelCase_												=      os.path.join(__a		,						urllib.parse.quote_plus(data_url.split("/"				)[-1]				)				)
		if os.path.exists(__a				) or not self.load_existing_dummy_data:
			return value
		else:
			# Backward compatibility, maybe deprecate at one point.
			# For many datasets with single url calls to dl_manager.download_and_extract,
			# the dummy_data.zip file is actually the zipped downloaded file
			# while now we expected the dummy_data.zip file to be a directory containing
			# the downloaded file.
			return path_to_dummy_data




	def    __A       (		self						:    int				):


		'''simple docstring'''







		pass




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		pass




	def    __A       (		self						:    Optional[Any]		,						lowerCAmelCase						:    Dict				):


		'''simple docstring'''







		def _iter_archive_members(lowerCAmelCase						:    List[str]				):
			# this preserves the order of the members inside the ZIP archive
			UpperCAmelCase_												=      Path(self.dummy_file				).parent
			UpperCAmelCase_												=      path.relative_to(__a				)
			with ZipFile(self.local_path_to_dummy_data				) as zip_file:
				UpperCAmelCase_												=      zip_file.namelist()
			for member in members:
				if member.startswith(relative_path.as_posix()				):
					yield dummy_parent_path.joinpath(__a				)

		UpperCAmelCase_												=      Path(__a				)
		UpperCAmelCase_												=      _iter_archive_members(__a				) if self.use_local_dummy_data else path.rglob("*"				)
		for file_path in file_paths:
			if file_path.is_file() and not file_path.name.startswith((".", "__")				):
				yield file_path.relative_to(__a				).as_posix(), file_path.open("rb"				)




	def    __A       (		self						:    Dict		,						lowerCAmelCase						:    Any				):


		'''simple docstring'''







		if not isinstance(__a		,						__a				):
			UpperCAmelCase_												=      [paths]
		for path in paths:
			if os.path.isfile(__a				):
				if os.path.basename(__a				).startswith((".", "__")				):
					return
				yield path
			else:
				for dirpath, dirnames, filenames in os.walk(__a				):
					if os.path.basename(__a				).startswith((".", "__")				):
						continue
					dirnames.sort()
					for filename in sorted(__a				):
						if filename.startswith((".", "__")				):
							continue
						yield os.path.join(__a		,						__a				)






"""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()
lowerCamelCase__						= logging.get_logger(__name__)




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							DPTConfig(embedding_type="hybrid"      )

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

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

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

      return config, expected_shape




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							["pretrained.model.head.weight", "pretrained.model.head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		int:




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

      if ".." in name:
            _UpperCamelCase   :						List[str]    =							name.replace(".."		,"."      )

      if "stem.conv" in name:
            _UpperCamelCase   :						Optional[int]    =							name.replace("stem.conv"		,"bit.embedder.convolution"      )
      if "blocks" in name:
            _UpperCamelCase   :						Optional[int]    =							name.replace("blocks"		,"layers"      )
      if "convolution" in name and "backbone" in name:
            _UpperCamelCase   :						Any    =							name.replace("convolution"		,"conv"      )
      if "layer" in name and "backbone" in name:
            _UpperCamelCase   :						Optional[Any]    =							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   :						Union[str, Any]    =							name.replace("embedder.conv"		,"embedder.convolution"      )
      if "backbone.bit.encoder.stem.norm" in name:
            _UpperCamelCase   :						Dict    =							name.replace("backbone.bit.encoder.stem.norm"		,"backbone.bit.embedder.norm"      )
      return name




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """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   :						Tuple    =							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   :						Tuple    =							in_proj_weight[: config.hidden_size, :]
            _UpperCamelCase   :						Dict    =							in_proj_bias[: config.hidden_size]
            _UpperCamelCase   :						Optional[int]    =							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _UpperCamelCase   :						Dict    =							in_proj_weight[
                -config.hidden_size :, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[-config.hidden_size :]




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Dict    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						str    =							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   :						Optional[int]    =							torch.load(lowercase_		,map_location="cpu"      )
      # remove certain keys
      remove_ignore_keys_(lowercase_      )
      # rename keys
      for key in state_dict.copy().keys():
            _UpperCamelCase   :						Union[str, Any]    =							state_dict.pop(lowercase_      )
            _UpperCamelCase   :						Tuple    =							val
      # read in qkv matrices
      read_in_q_k_v(lowercase_		,lowercase_      )

      # load HuggingFace model
      _UpperCamelCase   :						Dict    =							DPTForSemanticSegmentation(lowercase_      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase_      )
      model.load_state_dict(lowercase_      )
      model.eval()

      # Check outputs on an image
      _UpperCamelCase   :						List[Any]    =							480 if "ade" in checkpoint_url else 384
      _UpperCamelCase   :						List[str]    =							DPTImageProcessor(size=lowercase_      )

      _UpperCamelCase   :						int    =							prepare_img()
      _UpperCamelCase   :						List[str]    =							image_processor(lowercase_		,return_tensors="pt"      )

      # forward pass
      _UpperCamelCase   :						Any    =							model(**lowercase_      ).logits if "ade" in checkpoint_url else model(**lowercase_      ).predicted_depth

      if show_prediction:
            _UpperCamelCase   :						Optional[int]    =							(
                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__":
  lowerCamelCase__						= 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",
  )

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