





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 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   DPTConfig()

		if "large" in checkpoint_url:
				SCREAMING_SNAKE_CASE__								=   1_024
				SCREAMING_SNAKE_CASE__								=   4_096
				SCREAMING_SNAKE_CASE__								=   24
				SCREAMING_SNAKE_CASE__								=   16
				SCREAMING_SNAKE_CASE__								=   [5, 11, 17, 23]
				SCREAMING_SNAKE_CASE__								=   [256, 512, 1_024, 1_024]
				SCREAMING_SNAKE_CASE__								=   (1, 384, 384)

		if "ade" in checkpoint_url:
				SCREAMING_SNAKE_CASE__								=   True

				SCREAMING_SNAKE_CASE__								=   150
				SCREAMING_SNAKE_CASE__								=   """huggingface/label-files"""
				SCREAMING_SNAKE_CASE__								=   """ade20k-id2label.json"""
				SCREAMING_SNAKE_CASE__								=   json.load(open(cached_download(hf_hub_url(UpperCamelCase__       , UpperCamelCase__       , repo_type="""dataset"""     )     )       , """r"""     )     )
				SCREAMING_SNAKE_CASE__								=   {int(UpperCamelCase__     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE__								=   idalabel
				SCREAMING_SNAKE_CASE__								=   {v: k for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE__								=   [1, 150, 480, 480]

		return config, expected_shape






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






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

		return name






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






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






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Any       , UpperCamelCase__:				str       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_dpt_config(UpperCamelCase__     )
		# load original state_dict from URL
		SCREAMING_SNAKE_CASE__								=   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():
				SCREAMING_SNAKE_CASE__								=   state_dict.pop(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   val
		# read in qkv matrices
		read_in_q_k_v(UpperCamelCase__       , UpperCamelCase__     )

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

		# Check outputs on an image
		SCREAMING_SNAKE_CASE__								=   480 if """ade""" in checkpoint_url else 384
		SCREAMING_SNAKE_CASE__								=   DPTImageProcessor(size=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   prepare_img()
		SCREAMING_SNAKE_CASE__								=   image_processor(UpperCamelCase__       , return_tensors="""pt"""     )

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

		# Assert logits
		SCREAMING_SNAKE_CASE__								=   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:
				SCREAMING_SNAKE_CASE__								=   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__":
	_lowerCamelCase 					=      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 					=      parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs