





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__						:			Dict									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase				)						->  int:
				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(_UpperCAmelCase			,      _UpperCAmelCase			,      repo_type="""dataset"""				)				)			,      """r"""				)				)
								lowerCamelCase	 ={int(_UpperCAmelCase				): 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      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_UpperCAmelCase			,      _UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				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      _lowercase					(						)						->  Tuple:
				lowerCamelCase	 ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase	 =Image.open(requests.get(_UpperCAmelCase			,      stream=_UpperCAmelCase				).raw				)
				return im





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase ,  lowerCamelCase	 =get_dpt_config(_UpperCAmelCase				)
				# load original state_dict from URL
				lowerCamelCase	 =torch.hub.load_state_dict_from_url(_UpperCAmelCase			,      map_location="""cpu"""				)
				# remove certain keys
				remove_ignore_keys_(_UpperCAmelCase				)
				# rename keys
				for key in state_dict.copy().keys():
								lowerCamelCase	 =state_dict.pop(_UpperCAmelCase				)
								lowerCamelCase	 =val
				# read in qkv matrices
				read_in_q_k_v(_UpperCAmelCase			,      _UpperCAmelCase				)

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

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

				lowerCamelCase	 =prepare_img()
				lowerCamelCase	 =image_processor(_UpperCAmelCase			,      return_tensors="""pt"""				)

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

				# Assert logits
				lowerCamelCase	 =torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]				)
				if "ade" in checkpoint_url:
								lowerCamelCase	 =torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]				)
				assert outputs.shape == torch.Size(_UpperCAmelCase				)
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]			,      _UpperCAmelCase			,      atol=1e-4				)
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]			,      _UpperCAmelCase				)
				)

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

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


if __name__ == "__main__":
							UpperCAmelCase__						:			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.''',
							)

							UpperCAmelCase__						:			List[str]									=parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class       __A     (						a    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =SMALL_MODEL_IDENTIFIER
								lowerCamelCase	 ="""pt"""
								lowerCamelCase	 ="""tf"""


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =AutoModel.from_pretrained(self.test_model							)
								model_pt.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =TFAutoModel.from_pretrained(self.test_model   , from_pt=UpperCAmelCase_							)
								model_tf.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""mock_framework"""

								# Framework provided - return whatever the user provides
								lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model   , UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# PyTorch not in environment -> use TensorFlow
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Both in environment -> use PyTorch
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# Both not in environment -> raise error
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)





