





'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor




class 					UpperCAmelCase	(     unittest.TestCase  ):




      def __init__(     self     :					Dict     ,     __snake_case     :					Dict     ,     __snake_case     :					Union[str, Any]=7     ,     __snake_case     :					Any=3     ,     __snake_case     :					Dict=18     ,     __snake_case     :					str=30     ,     __snake_case     :					int=4_00     ,     __snake_case     :					Optional[int]=True     ,     __snake_case     :					Dict=None     ,     __snake_case     :					Any=True     ,     __snake_case     :					Tuple=None     ,     __snake_case     :					List[Any]=True     ,     __snake_case     :					List[Any]=[0.5, 0.5, 0.5]     ,     __snake_case     :					Union[str, Any]=[0.5, 0.5, 0.5]     ,     __snake_case     :					Union[str, Any]=False     ,     )							-> List[str]:
            _lowerCAmelCase					=				size if size is not None else {"""height""": 20, """width""": 20}
            _lowerCAmelCase					=				crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
            _lowerCAmelCase					=				parent
            _lowerCAmelCase					=				batch_size
            _lowerCAmelCase					=				num_channels
            _lowerCAmelCase					=				image_size
            _lowerCAmelCase					=				min_resolution
            _lowerCAmelCase					=				max_resolution
            _lowerCAmelCase					=				do_resize
            _lowerCAmelCase					=				size
            _lowerCAmelCase					=				do_center_crop
            _lowerCAmelCase					=				crop_size
            _lowerCAmelCase					=				do_normalize
            _lowerCAmelCase					=				image_mean
            _lowerCAmelCase					=				image_std
            _lowerCAmelCase					=				do_reduce_labels




      def 							lowercase__       (     self     :					Tuple    )							-> Optional[int]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_reduce_labels": self.do_reduce_labels,
            }




def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				load_dataset("""hf-internal-testing/fixtures_ade20k"""				,      split="""test"""    )

      _lowerCAmelCase					=				Image.open(dataset[0]["""file"""]    )
      _lowerCAmelCase					=				Image.open(dataset[1]["""file"""]    )

      return image, map
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				load_dataset("""hf-internal-testing/fixtures_ade20k"""				,      split="""test"""    )

      _lowerCAmelCase					=				Image.open(ds[0]["""file"""]    )
      _lowerCAmelCase					=				Image.open(ds[1]["""file"""]    )
      _lowerCAmelCase					=				Image.open(ds[2]["""file"""]    )
      _lowerCAmelCase					=				Image.open(ds[3]["""file"""]    )

      return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Optional[int]			=  BeitImageProcessor if is_vision_available() else None




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				BeitImageProcessingTester(self    )




      @property
      def 							lowercase__       (     self     :					Any    )							-> Union[str, Any]:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(__snake_case     ,     """do_resize"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """size"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """do_center_crop"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """center_crop"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """do_normalize"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """image_mean"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """image_std"""    )    )




      def 							lowercase__       (     self     :					Any    )							-> Any:
            _lowerCAmelCase					=				self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size     ,     {"""height""": 20, """width""": 20}    )
            self.assertEqual(image_processor.crop_size     ,     {"""height""": 18, """width""": 18}    )
            self.assertEqual(image_processor.do_reduce_labels     ,     __snake_case    )

            _lowerCAmelCase					=				self.image_processing_class.from_dict(
                self.image_processor_dict     ,     size=42     ,     crop_size=84     ,     reduce_labels=__snake_case    )
            self.assertEqual(image_processor.size     ,     {"""height""": 42, """width""": 42}    )
            self.assertEqual(image_processor.crop_size     ,     {"""height""": 84, """width""": 84}    )
            self.assertEqual(image_processor.do_reduce_labels     ,     __snake_case    )




      def 							lowercase__       (     self     :					Optional[int]    )							-> List[Any]:
            pass




      def 							lowercase__       (     self     :					Any    )							-> Tuple:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     Image.Image    )

            # Test not batched input
            _lowerCAmelCase					=				image_processing(image_inputs[0]     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )

            # Test batched
            _lowerCAmelCase					=				image_processing(__snake_case     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )




      def 							lowercase__       (     self     :					Optional[int]    )							-> Any:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case     ,     numpify=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     np.ndarray    )

            # Test not batched input
            _lowerCAmelCase					=				image_processing(image_inputs[0]     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )

            # Test batched
            _lowerCAmelCase					=				image_processing(__snake_case     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )




      def 							lowercase__       (     self     :					Tuple    )							-> Tuple:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case     ,     torchify=__snake_case    )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     torch.Tensor    )

            # Test not batched input
            _lowerCAmelCase					=				image_processing(image_inputs[0]     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )

            # Test batched
            _lowerCAmelCase					=				image_processing(__snake_case     ,     return_tensors="""pt"""    ).pixel_values
            self.assertEqual(
                encoded_images.shape     ,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Tuple:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            _lowerCAmelCase					=				prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__snake_case     ,     torchify=__snake_case    )
            _lowerCAmelCase					=				[]
            for image in image_inputs:
                  self.assertIsInstance(__snake_case     ,     torch.Tensor    )
                  maps.append(torch.zeros(image.shape[-2:]    ).long()    )

            # Test not batched input
            _lowerCAmelCase					=				image_processing(image_inputs[0]     ,     maps[0]     ,     return_tensors="""pt"""    )
            self.assertEqual(
                encoding["""pixel_values"""].shape     ,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(
                encoding["""labels"""].shape     ,     (
                    1,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(encoding["""labels"""].dtype     ,     torch.long    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

            # Test batched
            _lowerCAmelCase					=				image_processing(__snake_case     ,     __snake_case     ,     return_tensors="""pt"""    )
            self.assertEqual(
                encoding["""pixel_values"""].shape     ,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(
                encoding["""labels"""].shape     ,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(encoding["""labels"""].dtype     ,     torch.long    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

            # Test not batched input (PIL images)
            _lowerCAmelCase	,				_lowerCAmelCase					=				prepare_semantic_single_inputs()

            _lowerCAmelCase					=				image_processing(__snake_case     ,     __snake_case     ,     return_tensors="""pt"""    )
            self.assertEqual(
                encoding["""pixel_values"""].shape     ,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(
                encoding["""labels"""].shape     ,     (
                    1,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(encoding["""labels"""].dtype     ,     torch.long    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

            # Test batched input (PIL images)
            _lowerCAmelCase	,				_lowerCAmelCase					=				prepare_semantic_batch_inputs()

            _lowerCAmelCase					=				image_processing(__snake_case     ,     __snake_case     ,     return_tensors="""pt"""    )
            self.assertEqual(
                encoding["""pixel_values"""].shape     ,     (
                    2,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(
                encoding["""labels"""].shape     ,     (
                    2,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )     ,     )
            self.assertEqual(encoding["""labels"""].dtype     ,     torch.long    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )




      def 							lowercase__       (     self     :					Tuple    )							-> str:
            # Initialize image_processing
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )

            # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
            _lowerCAmelCase	,				_lowerCAmelCase					=				prepare_semantic_single_inputs()
            _lowerCAmelCase					=				image_processing(__snake_case     ,     __snake_case     ,     return_tensors="""pt"""    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 1_50    )

            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				image_processing(__snake_case     ,     __snake_case     ,     return_tensors="""pt"""    )
            self.assertTrue(encoding["""labels"""].min().item() >= 0    )
            self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )













'''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()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				DPTConfig()

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

      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				True

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

      return config, expected_shape
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





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

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





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





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

      # load HuggingFace model
      _lowerCAmelCase					=				DPTForSemanticSegmentation(lowerCAmelCase    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      # Check outputs on an image
      _lowerCAmelCase					=				4_80 if """ade""" in checkpoint_url else 3_84
      _lowerCAmelCase					=				DPTImageProcessor(size=lowerCAmelCase    )

      _lowerCAmelCase					=				prepare_img()
      _lowerCAmelCase					=				image_processor(lowerCAmelCase				,      return_tensors="""pt"""    )

      # forward pass
      _lowerCAmelCase					=				model(**lowerCAmelCase    ).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase    ).predicted_depth

      # Assert logits
      _lowerCAmelCase					=				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    )
      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    )
      assert outputs.shape == torch.Size(lowerCAmelCase    )
      assert (
          torch.allclose(outputs[0, 0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]				,      lowerCAmelCase    )
      )

      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )
      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )
      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )

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


if __name__ == "__main__":
      A__							:  Optional[int]    =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.''',
      )

      A__							:  Optional[int]    =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






