



'''simple docstring'''
def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       len(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[int]			    =       len(matrix[0]							)
						_UpperCamelCase :		List[Any]			    =       min(UpperCAmelCase_  ,  UpperCAmelCase_							)

						for row in range(UpperCAmelCase_							):
												# Check if diagonal element is not zero
												if matrix[row][row] != 0:
																		# Eliminate all the elements below the diagonal
																		for col in range(row + 1  ,  UpperCAmelCase_							):
																								_UpperCamelCase :		int			    =       matrix[col][row] / matrix[row][row]
																								for i in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
																														matrix[col][i] -= multiplier * matrix[row][i]
												else:
																		# Find a non-zero diagonal element to swap rows
																		_UpperCamelCase :		Tuple			    =       True
																		for i in range(row + 1  ,  UpperCAmelCase_							):
																								if matrix[i][row] != 0:
																														_UpperCamelCase :		Optional[Any]			    =       matrix[i], matrix[row]
																														_UpperCamelCase :		Optional[int]			    =       False
																														break
																		if reduce:
																								rank -= 1
																								for i in range(UpperCAmelCase_							):
																														_UpperCamelCase :		str			    =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
																		row -= 1

						return rank


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''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()
snake_case_			:     int  	=  logging.get_logger(__name__)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       DPTConfig(embedding_type='hybrid'							)

						if "large" in checkpoint_url:
												_UpperCamelCase :		Any			    =       1_0_2_4
												_UpperCamelCase :		List[Any]			    =       4_0_9_6
												_UpperCamelCase :		List[str]			    =       2_4
												_UpperCamelCase :		Tuple			    =       1_6
												_UpperCamelCase :		Union[str, Any]			    =       [5, 1_1, 1_7, 2_3]
												_UpperCamelCase :		Any			    =       [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												_UpperCamelCase :		Tuple			    =       (1, 3_8_4, 3_8_4)

						if "nyu" or "midas" in checkpoint_url:
												_UpperCamelCase :		Optional[int]			    =       7_6_8
												_UpperCamelCase :		Optional[Any]			    =       [1, 1, 1, 0.5]
												_UpperCamelCase :		List[Any]			    =       [2_5_6, 5_1_2, 7_6_8, 7_6_8]
												_UpperCamelCase :		Optional[int]			    =       1_5_0
												_UpperCamelCase :		Tuple			    =       1_6
												_UpperCamelCase :		Dict			    =       (1, 3_8_4, 3_8_4)
												_UpperCamelCase :		Optional[int]			    =       False
												_UpperCamelCase :		Optional[int]			    =       'project'

						if "ade" in checkpoint_url:
												_UpperCamelCase :		Dict			    =       True
												_UpperCamelCase :		Dict			    =       7_6_8
												_UpperCamelCase :		Union[str, Any]			    =       [1, 1, 1, 0.5]
												_UpperCamelCase :		Union[str, Any]			    =       1_5_0
												_UpperCamelCase :		str			    =       1_6
												_UpperCamelCase :		Tuple			    =       'huggingface/label-files'
												_UpperCamelCase :		Tuple			    =       'ade20k-id2label.json'
												_UpperCamelCase :		Tuple			    =       json.load(open(cached_download(hf_hub_url(UpperCAmelCase_  ,  UpperCAmelCase_  ,  repo_type='dataset'							)							)  ,  'r'							)							)
												_UpperCamelCase :		str			    =       {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
												_UpperCamelCase :		List[str]			    =       idalabel
												_UpperCamelCase :		Union[str, Any]			    =       {v: k for k, v in idalabel.items()}
												_UpperCamelCase :		int			    =       [1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase_  ,  UpperCAmelCase_							)






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

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

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						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 :		List[str]			    =       state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
												_UpperCamelCase :		List[str]			    =       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 :		List[str]			    =       in_proj_weight[: config.hidden_size, :]
												_UpperCamelCase :		int			    =       in_proj_bias[: config.hidden_size]
												_UpperCamelCase :		str			    =       in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCamelCase :		Union[str, Any]			    =       in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_UpperCamelCase :		List[Any]			    =       in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCamelCase :		Union[str, Any]			    =       in_proj_bias[-config.hidden_size :]






def A__   (					):
						_UpperCamelCase :		Optional[int]			    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase :		List[Any]			    =       Image.open(requests.get(UpperCAmelCase_  ,  stream=UpperCAmelCase_							).raw							)
						return im






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

						# load HuggingFace model
						_UpperCamelCase :		Union[str, Any]			    =       DPTForSemanticSegmentation(UpperCAmelCase_							) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase_							)
						model.load_state_dict(UpperCAmelCase_							)
						model.eval()

						# Check outputs on an image
						_UpperCamelCase :		Tuple			    =       4_8_0 if 'ade' in checkpoint_url else 3_8_4
						_UpperCamelCase :		Any			    =       DPTImageProcessor(size=UpperCAmelCase_							)

						_UpperCamelCase :		Union[str, Any]			    =       prepare_img()
						_UpperCamelCase :		Optional[int]			    =       image_processor(UpperCAmelCase_  ,  return_tensors='pt'							)

						# forward pass
						_UpperCamelCase :		Optional[Any]			    =       model(**UpperCAmelCase_							).logits if 'ade' in checkpoint_url else model(**UpperCAmelCase_							).predicted_depth

						if show_prediction:
												_UpperCamelCase :		List[str]			    =       (
												    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()) * 2_5_5							).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__":
					snake_case_			:     Any  	=  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',
					)

					snake_case_			:     Tuple  	=  parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)


