



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__  :  List[Any]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Any									= DPTConfig(embedding_type='''hybrid''' )

		if "large" in checkpoint_url:
				lowerCAmelCase_			:     Any									= 1024
				lowerCAmelCase_			:     Tuple									= 4096
				lowerCAmelCase_			:     List[Any]									= 24
				lowerCAmelCase_			:     int									= 16
				lowerCAmelCase_			:     Tuple									= [5, 11, 17, 23]
				lowerCAmelCase_			:     Optional[Any]									= [256, 512, 1024, 1024]
				lowerCAmelCase_			:     str									= (1, 384, 384)

		if "nyu" or "midas" in checkpoint_url:
				lowerCAmelCase_			:     Union[str, Any]									= 768
				lowerCAmelCase_			:     int									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     List[str]									= [256, 512, 768, 768]
				lowerCAmelCase_			:     int									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= (1, 384, 384)
				lowerCAmelCase_			:     List[str]									= False
				lowerCAmelCase_			:     Union[str, Any]									= '''project'''

		if "ade" in checkpoint_url:
				lowerCAmelCase_			:     Any									= True
				lowerCAmelCase_			:     Any									= 768
				lowerCAmelCase_			:     Optional[int]									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     Union[str, Any]									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Any									= '''ade20k-id2label.json'''
				lowerCAmelCase_			:     str									= json.load(open(cached_download(hf_hub_url(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' ) )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     Optional[Any]									= idalabel
				lowerCAmelCase_			:     Optional[int]									= {v: k for k, v in idalabel.items()}
				lowerCAmelCase_			:     List[Any]									= [1, 150, 480, 480]

		return config, expected_shape


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     int									= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(__UpperCamelCase     ,__UpperCamelCase )


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

		if ".." in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''..'''     ,'''.''' )

		if "stem.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''stem.conv'''     ,'''bit.embedder.convolution''' )
		if "blocks" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''blocks'''     ,'''layers''' )
		if "convolution" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''convolution'''     ,'''conv''' )
		if "layer" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''layer'''     ,'''layers''' )
		if "backbone.bit.encoder.bit" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''backbone.bit.encoder.bit'''     ,'''backbone.bit''' )
		if "embedder.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''embedder.conv'''     ,'''embedder.convolution''' )
		if "backbone.bit.encoder.stem.norm" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''backbone.bit.encoder.stem.norm'''     ,'''backbone.bit.embedder.norm''' )
		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__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)
				lowerCAmelCase_			:     Dict									= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
				lowerCAmelCase_			:     Tuple									= 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_			:     str									= in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase_			:     str									= in_proj_bias[: config.hidden_size]
				lowerCAmelCase_			:     Optional[Any]									= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase_			:     int									= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase_			:     Any									= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase_			:     Dict									= in_proj_bias[-config.hidden_size :]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_			:     Dict									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		return im


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		str ):
		lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= get_dpt_config(__UpperCamelCase )
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		lowerCAmelCase_			:     List[str]									= torch.load(__UpperCamelCase     ,map_location='''cpu''' )
		# remove certain keys
		remove_ignore_keys_(__UpperCamelCase )
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_			:     Any									= state_dict.pop(__UpperCamelCase )
				lowerCAmelCase_			:     Optional[Any]									= val
		# read in qkv matrices
		read_in_q_k_v(__UpperCamelCase     ,__UpperCamelCase )

		# load HuggingFace model
		lowerCAmelCase_			:     List[Any]									= DPTForSemanticSegmentation(__UpperCamelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )
		model.eval()

		# Check outputs on an image
		lowerCAmelCase_			:     Tuple									= 480 if '''ade''' in checkpoint_url else 384
		lowerCAmelCase_			:     Optional[int]									= DPTImageProcessor(size=__UpperCamelCase )

		lowerCAmelCase_			:     Union[str, Any]									= prepare_img()
		lowerCAmelCase_			:     str									= image_processor(__UpperCamelCase     ,return_tensors='''pt''' )

		# forward pass
		lowerCAmelCase_			:     Tuple									= model(**__UpperCamelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCamelCase ).predicted_depth

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

				Image.fromarray((prediction / prediction.max()) * 255 ).show()

		if pytorch_dump_folder_path is not None:
				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:
				model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
				image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )


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=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''',
						)

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










from pathlib import Path

import fire


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     List[str]									= Path(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= Path(__UpperCamelCase )
		dest_dir.mkdir(exist_ok=__UpperCamelCase )
		for path in src_dir.iterdir():
				lowerCAmelCase_			:     Optional[Any]									= [x.rstrip() for x in list(path.open().readlines() )][:n]
				lowerCAmelCase_			:     List[str]									= dest_dir.joinpath(path.name )
				print(__UpperCamelCase )
				dest_path.open('''w''' ).write('''\n'''.join(__UpperCamelCase ) )


if __name__ == "__main__":
						fire.Fire(minify)





