





"""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()
a		:				Any  			=    logging.get_logger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       DPTConfig(embedding_type="hybrid"      )

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

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

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

	return config, expected_shape






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]      )				->Dict:






	'''simple docstring'''






	a   :	Union[str, Any]			=       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
	for k in ignore_keys:
		state_dict.pop(_lowercase					, _lowercase      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->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
	):
		a   :	Union[str, Any]			=       name.replace("pretrained.model"					, "dpt.encoder"      )
	if "pretrained.model" in name:
		a   :	str			=       name.replace("pretrained.model"					, "dpt.embeddings"      )
	if "patch_embed" in name:
		a   :	Union[str, Any]			=       name.replace("patch_embed"					, ""      )
	if "pos_embed" in name:
		a   :	Union[str, Any]			=       name.replace("pos_embed"					, "position_embeddings"      )
	if "attn.proj" in name:
		a   :	Union[str, Any]			=       name.replace("attn.proj"					, "attention.output.dense"      )
	if "proj" in name and "project" not in name:
		a   :	Optional[Any]			=       name.replace("proj"					, "projection"      )
	if "blocks" in name:
		a   :	Optional[int]			=       name.replace("blocks"					, "layer"      )
	if "mlp.fc1" in name:
		a   :	int			=       name.replace("mlp.fc1"					, "intermediate.dense"      )
	if "mlp.fc2" in name:
		a   :	Any			=       name.replace("mlp.fc2"					, "output.dense"      )
	if "norm1" in name and "backbone" not in name:
		a   :	Tuple			=       name.replace("norm1"					, "layernorm_before"      )
	if "norm2" in name and "backbone" not in name:
		a   :	Union[str, Any]			=       name.replace("norm2"					, "layernorm_after"      )
	if "scratch.output_conv" in name:
		a   :	List[str]			=       name.replace("scratch.output_conv"					, "head"      )
	if "scratch" in name:
		a   :	Any			=       name.replace("scratch"					, "neck"      )
	if "layer1_rn" in name:
		a   :	List[Any]			=       name.replace("layer1_rn"					, "convs.0"      )
	if "layer2_rn" in name:
		a   :	Optional[Any]			=       name.replace("layer2_rn"					, "convs.1"      )
	if "layer3_rn" in name:
		a   :	List[str]			=       name.replace("layer3_rn"					, "convs.2"      )
	if "layer4_rn" in name:
		a   :	Dict			=       name.replace("layer4_rn"					, "convs.3"      )
	if "refinenet" in name:
		a   :	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
		a   :	Optional[Any]			=       name.replace(F"""refinenet{layer_idx}"""					, F"""fusion_stage.layers.{abs(layer_idx-4      )}"""      )
	if "out_conv" in name:
		a   :	Dict			=       name.replace("out_conv"					, "projection"      )
	if "resConfUnit1" in name:
		a   :	str			=       name.replace("resConfUnit1"					, "residual_layer1"      )
	if "resConfUnit2" in name:
		a   :	Union[str, Any]			=       name.replace("resConfUnit2"					, "residual_layer2"      )
	if "conv1" in name:
		a   :	Optional[Any]			=       name.replace("conv1"					, "convolution1"      )
	if "conv2" in name:
		a   :	Tuple			=       name.replace("conv2"					, "convolution2"      )
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		a   :	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:
		a   :	Dict			=       name.replace("pretrained.act_postprocess2.0.project.0"					, "neck.reassemble_stage.readout_projects.1.0"      )
	if "pretrained.act_postprocess3.0.project.0" in name:
		a   :	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:
		a   :	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:
		a   :	Any			=       name.replace("pretrained.act_postprocess1.3"					, "neck.reassemble_stage.layers.0.projection"      )
	if "pretrained.act_postprocess1.4" in name:
		a   :	Optional[int]			=       name.replace("pretrained.act_postprocess1.4"					, "neck.reassemble_stage.layers.0.resize"      )
	if "pretrained.act_postprocess2.3" in name:
		a   :	str			=       name.replace("pretrained.act_postprocess2.3"					, "neck.reassemble_stage.layers.1.projection"      )
	if "pretrained.act_postprocess2.4" in name:
		a   :	str			=       name.replace("pretrained.act_postprocess2.4"					, "neck.reassemble_stage.layers.1.resize"      )
	if "pretrained.act_postprocess3.3" in name:
		a   :	Tuple			=       name.replace("pretrained.act_postprocess3.3"					, "neck.reassemble_stage.layers.2.projection"      )
	if "pretrained.act_postprocess4.3" in name:
		a   :	str			=       name.replace("pretrained.act_postprocess4.3"					, "neck.reassemble_stage.layers.3.projection"      )
	if "pretrained.act_postprocess4.4" in name:
		a   :	Optional[int]			=       name.replace("pretrained.act_postprocess4.4"					, "neck.reassemble_stage.layers.3.resize"      )
	if "pretrained" in name:
		a   :	List[str]			=       name.replace("pretrained"					, "dpt"      )
	if "bn" in name:
		a   :	List[str]			=       name.replace("bn"					, "batch_norm"      )
	if "head" in name:
		a   :	Optional[Any]			=       name.replace("head"					, "head.head"      )
	if "encoder.norm" in name:
		a   :	Optional[int]			=       name.replace("encoder.norm"					, "layernorm"      )
	if "auxlayer" in name:
		a   :	List[Any]			=       name.replace("auxlayer"					, "auxiliary_head.head"      )
	if "backbone" in name:
		a   :	List[str]			=       name.replace("backbone"					, "backbone.bit.encoder"      )

	if ".." in name:
		a   :	List[str]			=       name.replace(".."					, "."      )

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					Tuple      )				->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)
		a   :	Any			=       state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
		a   :	Optional[int]			=       state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""      )
		# next, add query, keys and values (in that order) to the state dict
		a   :	Any			=       in_proj_weight[: config.hidden_size, :]
		a   :	Dict			=       in_proj_bias[: config.hidden_size]
		a   :	Union[str, Any]			=       in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		a   :	List[str]			=       in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		a   :	Tuple			=       in_proj_weight[
		    -config.hidden_size :, :
		]
		a   :	str			=       in_proj_bias[-config.hidden_size :]






def    _SCREAMING_SNAKE_CASE	(			)				->List[Any]:






	'''simple docstring'''






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






@torch.no_grad()
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					str					, _lowercase		:					int					, _lowercase		:					List[Any]					, _lowercase		:					List[Any]      )				->List[str]:






	'''simple docstring'''






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

	# load HuggingFace model
	a   :	Optional[Any]			=       DPTForSemanticSegmentation(_lowercase      ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_lowercase      )
	model.load_state_dict(_lowercase      )
	model.eval()

	# Check outputs on an image
	a   :	str			=       480 if 'ade' in checkpoint_url else 384
	a   :	Dict			=       DPTImageProcessor(size=_lowercase      )

	a   :	int			=       prepare_img()
	a   :	Optional[int]			=       image_processor(_lowercase					, return_tensors="pt"      )

	# forward pass
	a   :	List[Any]			=       model(**_lowercase      ).logits if 'ade' in checkpoint_url else model(**_lowercase      ).predicted_depth

	if show_prediction:
		a   :	str			=       (
		    torch.nn.functional.interpolate(
		        outputs.unsqueeze(1      )					, size=(image.size[1], image.size[0])					, mode="bicubic"					, align_corners=_lowercase					, )
		    .squeeze()
		    .cpu()
		    .numpy()
		)

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

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

	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		:				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 random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


