


'''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()
_lowercase    :    Optional[Any]			 =  logging.get_logger(__name__)


def    lowerCamelCase__   ( A     :    int  ):




						'''simple docstring'''






						UpperCAmelCase         =			DPTConfig(embedding_type='''hybrid'''  )

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

						if "nyu" or "midas" in checkpoint_url:
												UpperCAmelCase         =			7_68
												UpperCAmelCase         =			[1, 1, 1, 0.5]
												UpperCAmelCase         =			[2_56, 5_12, 7_68, 7_68]
												UpperCAmelCase         =			1_50
												UpperCAmelCase         =			16
												UpperCAmelCase         =			(1, 3_84, 3_84)
												UpperCAmelCase         =			False
												UpperCAmelCase         =			'''project'''

						if "ade" in checkpoint_url:
												UpperCAmelCase         =			True
												UpperCAmelCase         =			7_68
												UpperCAmelCase         =			[1, 1, 1, 0.5]
												UpperCAmelCase         =			1_50
												UpperCAmelCase         =			16
												UpperCAmelCase         =			'''huggingface/label-files'''
												UpperCAmelCase         =			'''ade20k-id2label.json'''
												UpperCAmelCase         =			json.load(open(cached_download(hf_hub_url(A   ,     A   ,     repo_type='''dataset'''  )  )   ,     '''r'''  )  )
												UpperCAmelCase         =			{int(A  ): v for k, v in idalabel.items()}
												UpperCAmelCase         =			idalabel
												UpperCAmelCase         =			{v: k for k, v in idalabel.items()}
												UpperCAmelCase         =			[1, 1_50, 4_80, 4_80]

						return config, expected_shape


def    lowerCamelCase__   ( A     :    Union[str, Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
						for k in ignore_keys:
												state_dict.pop(A   ,     A  )


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






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

						if ".." in name:
												UpperCAmelCase         =			name.replace('''..'''   ,     '''.'''  )

						if "stem.conv" in name:
												UpperCAmelCase         =			name.replace('''stem.conv'''   ,     '''bit.embedder.convolution'''  )
						if "blocks" in name:
												UpperCAmelCase         =			name.replace('''blocks'''   ,     '''layers'''  )
						if "convolution" in name and "backbone" in name:
												UpperCAmelCase         =			name.replace('''convolution'''   ,     '''conv'''  )
						if "layer" in name and "backbone" in name:
												UpperCAmelCase         =			name.replace('''layer'''   ,     '''layers'''  )
						if "backbone.bit.encoder.bit" in name:
												UpperCAmelCase         =			name.replace('''backbone.bit.encoder.bit'''   ,     '''backbone.bit'''  )
						if "embedder.conv" in name:
												UpperCAmelCase         =			name.replace('''embedder.conv'''   ,     '''embedder.convolution'''  )
						if "backbone.bit.encoder.stem.norm" in name:
												UpperCAmelCase         =			name.replace('''backbone.bit.encoder.stem.norm'''   ,     '''backbone.bit.embedder.norm'''  )
						return name


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Union[str, Any]  ):




						'''simple docstring'''






						for i in range(config.num_hidden_layers  ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												UpperCAmelCase         =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
												UpperCAmelCase         =			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         =			in_proj_weight[: config.hidden_size, :]
												UpperCAmelCase         =			in_proj_bias[: config.hidden_size]
												UpperCAmelCase         =			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase         =			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase         =			in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase         =			in_proj_bias[-config.hidden_size :]


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase         =			Image.open(requests.get(A   ,     stream=A  ).raw  )
						return im


@torch.no_grad()
def    lowerCamelCase__   ( A     :    Dict   ,     A     :    List[str]   ,     A     :    Any   ,     A     :    Dict   ,     A     :    Optional[int]  ):




						'''simple docstring'''






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

						# load HuggingFace model
						UpperCAmelCase         =			DPTForSemanticSegmentation(A  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(A  )
						model.load_state_dict(A  )
						model.eval()

						# Check outputs on an image
						UpperCAmelCase         =			4_80 if '''ade''' in checkpoint_url else 3_84
						UpperCAmelCase         =			DPTImageProcessor(size=A  )

						UpperCAmelCase         =			prepare_img()
						UpperCAmelCase         =			image_processor(A   ,     return_tensors='''pt'''  )

						# forward pass
						UpperCAmelCase         =			model(**A  ).logits if '''ade''' in checkpoint_url else model(**A  ).predicted_depth

						if show_prediction:
												UpperCAmelCase         =			(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1  )   ,     size=(image.size[1], image.size[0])   ,     mode='''bicubic'''   ,     align_corners=A   ,     )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 2_55  ).show()

						if pytorch_dump_folder_path is not None:
												Path(A  ).mkdir(exist_ok=A  )
												print(f"""Saving model to {pytorch_dump_folder_path}"""  )
												model.save_pretrained(A  )
												print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
												image_processor.save_pretrained(A  )

						if push_to_hub:
												model.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )
												image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''  )


if __name__ == "__main__":
	_lowercase    :    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=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	_lowercase    :    str			 =  parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





