



"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_pytesseract_available():
			from PIL import Image

			from transformers import LayoutLMvaImageProcessor






class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=18						,	SCREAMING_SNAKE_CASE__=30						,	SCREAMING_SNAKE_CASE__=400						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=True						,	)      ->	Any:
				A__											=					size if size is not None else {"height": 18, "width": 18}
				A__											=					parent
				A__											=					batch_size
				A__											=					num_channels
				A__											=					image_size
				A__											=					min_resolution
				A__											=					max_resolution
				A__											=					do_resize
				A__											=					size
				A__											=					apply_ocr







			def   snake_case__	(      self		)      ->	List[Any]:
				return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Any							  =     LayoutLMvaImageProcessor if is_pytesseract_available() else None


			def   snake_case__	(      self		)      ->	str:
				A__											=					LayoutLMvaImageProcessingTester(self		)


			@property
			def   snake_case__	(      self		)      ->	Any:
				return self.image_processor_tester.prepare_image_processor_dict()


			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_resize"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"size"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"apply_ocr"		)		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size						,	{"height": 18, "width": 18}		)

				A__											=					self.image_processing_class.from_dict(self.image_processor_dict						,	size=42		)
				self.assertEqual(image_processor.size						,	{"height": 42, "width": 42}		)


			def   snake_case__	(      self		)      ->	List[str]:
				pass


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

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

				self.assertIsInstance(encoding.words						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(encoding.boxes						,	SCREAMING_SNAKE_CASE__		)

				# Test batched
				A__											=					image_processing(SCREAMING_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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)


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

				# Test not batched input
				A__											=					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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)

				# Test batched
				A__											=					image_processing(SCREAMING_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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)


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

				# Test not batched input
				A__											=					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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)

				# Test batched
				A__											=					image_processing(SCREAMING_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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)







			def   snake_case__	(      self		)      ->	int:
				# with apply_OCR = True
				A__											=					LayoutLMvaImageProcessor()

				from datasets import load_dataset

				A__											=					load_dataset("hf-internal-testing/fixtures_docvqa"						,	split="test"		)

				A__											=					Image.open(ds[0]["file"]		).convert("RGB"		)

				A__											=					image_processing(SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				self.assertEqual(encoding.pixel_values.shape						,	(1, 3, 224, 224)		)
				self.assertEqual(len(encoding.words		)						,	len(encoding.boxes		)		)

				# fmt: off
				# the words and boxes were obtained with Tesseract 4.1.1
				A__											=					[["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
				A__											=					[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
				# fmt: on

				self.assertListEqual(encoding.words						,	SCREAMING_SNAKE_CASE__		)
				self.assertListEqual(encoding.boxes						,	SCREAMING_SNAKE_CASE__		)

				# with apply_OCR = False
				A__											=					LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__		)

				A__											=					image_processing(SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		)

				self.assertEqual(encoding.pixel_values.shape						,	(1, 3, 224, 224)		)











'''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)






