






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_  :	List[str] 				=					logging.get_logger(__name__)







def     A__   (  lowerCamelCase  )					->		List[Any]:
				UpperCamelCase_:							Union[str, Any]		=  DPTConfig()

				if "large" in checkpoint_url:
								UpperCamelCase_:							Tuple		=  10_24
								UpperCamelCase_:							Union[str, Any]		=  40_96
								UpperCamelCase_:							List[Any]		=  24
								UpperCamelCase_:							str		=  16
								UpperCamelCase_:							str		=  [5, 11, 17, 23]
								UpperCamelCase_:							Optional[Any]		=  [2_56, 5_12, 10_24, 10_24]
								UpperCamelCase_:							Any		=  (1, 3_84, 3_84)

				if "ade" in checkpoint_url:
								UpperCamelCase_:							List[Any]		=  True

								UpperCamelCase_:							Tuple		=  1_50
								UpperCamelCase_:							Optional[Any]		=  """huggingface/label-files"""
								UpperCamelCase_:							List[str]		=  """ade20k-id2label.json"""
								UpperCamelCase_:							List[str]		=  json.load(open(cached_download(hf_hub_url(snake_case_       ,      snake_case_       ,      repo_type="""dataset"""  )  )       ,      """r"""  )  )
								UpperCamelCase_:							List[Any]		=  {int(snake_case_  ): v for k, v in idalabel.items()}
								UpperCamelCase_:							Optional[int]		=  idalabel
								UpperCamelCase_:							List[Any]		=  {v: k for k, v in idalabel.items()}
								UpperCamelCase_:							Dict		=  [1, 1_50, 4_80, 4_80]

				return config, expected_shape







def     A__   (  lowerCamelCase  )					->		Dict:
				UpperCamelCase_:							Any		=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(snake_case_       ,      snake_case_  )







def     A__   (  lowerCamelCase  )					->		Optional[int]:




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

				return name







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[str]:




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







def     A__   (  )					->		Dict:
				UpperCamelCase_:							Any		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							str		=  Image.open(requests.get(snake_case_       ,      stream=snake_case_  ).raw  )
				return im







@torch.no_grad()
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				UpperCamelCase_,				UpperCamelCase_:							Tuple		=  get_dpt_config(snake_case_  )
				# load original state_dict from URL
				UpperCamelCase_:							Optional[int]		=  torch.hub.load_state_dict_from_url(snake_case_       ,      map_location="""cpu"""  )
				# remove certain keys
				remove_ignore_keys_(snake_case_  )
				# rename keys
				for key in state_dict.copy().keys():
								UpperCamelCase_:							Dict		=  state_dict.pop(snake_case_  )
								UpperCamelCase_:							Dict		=  val
				# read in qkv matrices
				read_in_q_k_v(snake_case_       ,      snake_case_  )

				# load HuggingFace model
				UpperCamelCase_:							Optional[Any]		=  DPTForSemanticSegmentation(snake_case_  ) if """ade""" in checkpoint_url else DPTForDepthEstimation(snake_case_  )
				model.load_state_dict(snake_case_  )
				model.eval()

				# Check outputs on an image
				UpperCamelCase_:							Tuple		=  4_80 if """ade""" in checkpoint_url else 3_84
				UpperCamelCase_:							List[str]		=  DPTImageProcessor(size=snake_case_  )

				UpperCamelCase_:							int		=  prepare_img()
				UpperCamelCase_:							List[Any]		=  image_processor(snake_case_       ,      return_tensors="""pt"""  )

				# forward pass
				UpperCamelCase_:							Dict		=  model(**snake_case_  ).logits if """ade""" in checkpoint_url else model(**snake_case_  ).predicted_depth

				# Assert logits
				UpperCamelCase_:							Tuple		=  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:
								UpperCamelCase_:							Dict		=  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(snake_case_  )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]       ,      snake_case_       ,      atol=1E-4  )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]       ,      snake_case_  )
				)

				Path(snake_case_  ).mkdir(exist_ok=snake_case_  )
				print(F'''Saving model to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(snake_case_  )
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
				image_processor.save_pretrained(snake_case_  )

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


if __name__ == "__main__":
							lowerCamelCase_  :	List[str] 				=					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.""",
							)

							lowerCamelCase_  :	Union[str, Any] 				=					parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def     A__   (  )					->		Union[str, Any]:
				import os as original_os
				from os import path as original_path
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join

				UpperCamelCase_:							Optional[int]		=  """__test_patch_submodule_mock__"""
				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								# Every way to access os.path.join must be patched, and the rest must stay untouched

								# check os.path.join
								assert isinstance(_test_patching.os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj  )
								assert _test_patching.os.path.join is mock

								# check path.join
								assert isinstance(_test_patching.path       ,      _PatchedModuleObj  )
								assert _test_patching.path.join is mock

								# check join
								assert _test_patching.join is mock

								# check that the other attributes are untouched
								assert _test_patching.os.rename is original_rename
								assert _test_patching.path.dirname is original_dirname
								assert _test_patching.os.path.dirname is original_dirname

								# Even renamed modules or objects must be patched

								# check renamed_os.path.join
								assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_os.path.join is mock

								# check renamed_path.join
								assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_path.join is mock

								# check renamed_join
								assert _test_patching.renamed_join is mock

								# check that the other attributes are untouched
								assert _test_patching.renamed_os.rename is original_rename
								assert _test_patching.renamed_path.dirname is original_dirname
								assert _test_patching.renamed_os.path.dirname is original_dirname

				# check that everthing is back to normal when the patch is over

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join







def     A__   (  )					->		Union[str, Any]:
				assert _test_patching.open is open

				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_builtin_mock__"""
				# _test_patching has "open" in its globals
				assert _test_patching.open is open
				with patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  ):
								assert _test_patching.open is mock

				# check that everthing is back to normal when the patch is over

				assert _test_patching.open is open







def     A__   (  )					->		Optional[Any]:
				# pandas.read_csv is not present in _test_patching
				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_missing_mock__"""
				with patch_submodule(_test_patching       ,      """pandas.read_csv"""       ,      lowerCamelCase  ):
								pass







def     A__   (  )					->		Any:
				# builtin should always be mocked even if they're not in the globals
				# in case they're loaded at one point
				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_missing_builtin_mock__"""
				# _test_patching doesn't have "len" in its globals
				assert getattr(_test_patching       ,      """len"""       ,      lowerCamelCase  ) is None
				with patch_submodule(_test_patching       ,      """len"""       ,      lowerCamelCase  ):
								assert _test_patching.len is mock
				assert _test_patching.len is len







def     A__   (  )					->		Any:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_start_and_stop_mock__"""
				UpperCamelCase_:							List[str]		=  patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  )
				assert _test_patching.open is open
				patch.start()
				assert _test_patching.open is mock
				patch.stop()
				assert _test_patching.open is open







def     A__   (  )					->		List[str]:
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_successive_join__"""
				UpperCamelCase_:							Any		=  """__test_patch_submodule_successive_dirname__"""
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_successive_rename__"""
				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename

				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

    # try another order
				with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename







def     A__   (  )					->		Union[str, Any]:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_doesnt_exist_mock__"""
				with patch_submodule(_test_patching       ,      """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass
				with patch_submodule(_test_patching       ,      """os.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass






